Intelligent control equipment signal acquisition method and device, equipment and storage medium

Abstract

The invention relates to the technical field of equipment intelligent control, and discloses an intelligent control equipment signal acquisition method and device, equipment and a storage medium. Intelligent control equipment is subjected to digital simulation to obtain a scene cooperation intelligent control model, and a scene live simulation framework and a perception interference analysis framework are formed based on model analysis; according to the method, real-time intelligent control characteristics are collected and substituted into a framework to obtain a scene live information matrix and a sensing interference information matrix, sensing cooperative countermeasures of all devices are obtained according to matrix analysis, the driving devices conduct work and scene sensing according to the cooperative countermeasures, and signal collection and interference removal are achieved. The sensing interference among the intelligent control devices is eliminated, the cooperative work among the devices is realized, more accurate control and sensing capabilities are provided for the devices, and the problem that the sensing capabilities of the devices are mutually influenced by the work among a plurality of intelligent control devices in the prior art is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent control of equipment, and in particular to a method, device, equipment and storage medium for collecting signals of intelligent control equipment. 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 perception interference, and ensure the stability and accuracy of the system. Due to the complex relationship between the working parameters, perception parameters, working environment and perception objects of multiple intelligent devices, most of the current intelligent control systems only optimize the working or perception effects of the equipment in certain local ranges, and lack attention to the collaborative optimization of the entire system.

[0003] In particular, smart home systems achieve automation and remote control by connecting various smart devices (such as smart lights, smart air conditioners, smart door locks, smart speakers, etc.). These smart devices may interfere with each other during operation, thus affecting the operating 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 collecting signals from an intelligent control device, aiming to solve the problem in the prior art that the operations of multiple intelligent control devices affect the perception capabilities of the devices.

[0005] The present invention is implemented in this way. In a first aspect, the present invention provides a method for collecting signals of an intelligent control device, comprising: 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 perception interference removal signal collection of the intelligent control device for the designated working scene.

[0006] In a second aspect, the present invention provides a device for collecting signals of an intelligent control device, which is used to implement a method for collecting signals of an intelligent control device as described in any one of the first aspects.

[0007] In a third aspect, the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program executable on the processor, and when the processor executes the computer program, the method for collecting signals of an intelligent control device as described in any one of the first aspects is implemented.

[0008] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the processor executes a signal acquisition method for an intelligent control device as described in any one of the first aspects.

[0009] The present invention provides a method for collecting signals of an intelligent control device, which has the following beneficial effects: The present invention performs digital simulation on each intelligent control device to obtain a scene collaborative intelligent control model, forms a scene real-time simulation framework and a perception interference analysis framework based on the model analysis, collects real-time intelligent control features and substitutes them into the framework to obtain a scene real-time information matrix and a perception interference information matrix, obtains the perception collaborative countermeasures of each device according to the matrix analysis, drives the device to work and perceive the scene according to the collaborative countermeasures, and realizes signal collection and interference removal. This method can eliminate the perception interference between intelligent control devices through precise modeling and real-time feedback, realize collaborative work between devices, provide more precise control and perception capabilities for the devices, and solves the problem in the prior art that the work between multiple intelligent control devices affects the perception capabilities of the devices. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 It is a schematic diagram of the steps of a method for collecting signals of an intelligent control device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0011] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with 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 intended to limit the present invention.

[0012] The implementation of the present invention is described in detail below in conjunction with specific embodiments.

[0013] Reference Figure 1 As shown, a preferred embodiment of the present invention is provided.

[0014] In a first aspect, the present invention provides a method for collecting signals of an intelligent control device, comprising: S1: 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; S2: 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 a scenario real-time simulation framework and a perception interference analysis framework of the scenario collaborative intelligent control model; S3: 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; S4: 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; S5: 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; S6: driving each of the intelligent control devices to perform device operation and scene perception according to the perception coordination strategy, so as to realize the perception interference removal signal collection of the intelligent control device for the designated working scene.

[0015] Specifically, in step S1 of the embodiment provided by the present invention, the system collects performance information of each intelligent control device through sensors, historical data or the monitoring function of the device itself. The performance information generally includes the working status, response time, power consumption, load capacity, perception accuracy, etc. of the device. For example, information such as the brightness adjustment range, color temperature adjustment capability, and response time of intelligent lighting devices are obtained; information such as the temperature control range, wind speed setting, energy efficiency level, and noise level of intelligent air-conditioning devices are obtained; information such as the volume adjustment range, sound quality capability, and response time of intelligent audio devices are obtained; and potential mutual influences between devices are obtained through correlation analysis of the performances of different devices. For example, the temperature control of the air conditioner may affect the reading of the temperature and humidity sensor, and the brightness of the light may affect the automatic adjustment of the curtains.

[0016] More specifically, based on the collected device performance information, a mathematical model of each intelligent control device is established to reflect the performance of the device 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 airflow and temperature changes in the room. The digital model of the lamp can simulate the changes in its brightness and color temperature adjustment on user perception.

[0017] More specifically, based on the above device model, the operating status and performance indicators of each device are combined to build a comprehensive scenario model. In this model, the behaviors of each device will interact with each other to simulate the comprehensive effect in the home environment. For example, the interactive simulation of smart air conditioners, smart lamps, smart speakers and other devices can take into account the impact of different devices on the room environment (such as temperature, brightness, noise, etc.). At this point, the collaborative working effect of the devices is integrated into a scenario collaborative intelligent control model.

[0018] More specifically, the accuracy and effectiveness of the model are verified by comparing the actual collected data with the simulation results. 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-conditioning simulation results and the actual temperature changes, the heat exchange efficiency of the air-conditioning model can be adjusted, or more environmental factors (such as opening and closing windows) can be considered. Based on the verification, the actual effect of the scenario model is optimized. For example, unnecessary interference may occur between certain devices (such as air conditioning airflow affecting the accuracy of temperature and humidity sensors). The model is optimized to reduce these interferences, thereby improving the overall collaborative efficiency of the system.

[0019] More specifically, the digital simulation results of all devices and their interactive relationships are integrated into a unified scene-based collaborative intelligent control model. This model can describe the collaborative working effect between devices in the smart home scene. The scene-based collaborative intelligent control model can provide the ability to coordinate device scheduling, optimize scene effects, and dynamically adjust equipment. For example, under certain environmental changes (such as people entering the room and changes in indoor temperature), the model can automatically adjust the working status of equipment such as air-conditioning temperature and light brightness to ensure environmental comfort and energy-saving effects.

[0020] It is understandable that by establishing a digital simulation model, the working status and interaction of the equipment can be accurately predicted without actually running the equipment. The working efficiency of the equipment and the overall effect of the scene can be optimized. For example, in the living room scene, the working mode of the air conditioner, lighting and audio can be automatically adjusted through model analysis to achieve the best comfort. Through performance correlation analysis, potential interference sources between devices can be identified (such as air conditioning airflow interfering with the operation of the temperature and humidity sensor), and the occurrence of interference can be reduced by optimizing the model. For example, the model can automatically adjust the wind speed direction of the air conditioner to prevent the temperature and humidity sensor from being affected by the wind flow, thereby improving the accuracy of the sensor.

[0021] More specifically, the scene-based collaborative intelligent control model can dynamically adjust the working status of the device based on real-time data and environmental changes. For example, when the temperature or brightness in the room changes, the model will adjust the air-conditioning temperature, light brightness, etc. according to the current state of the intelligent control device to ensure that environmental conditions always remain within the ideal range. The model can not only optimize the operating efficiency of the equipment, but also automatically adjust the behavior of the equipment according to user needs and environmental changes. For example, when the user is at home, the model can automatically adjust the air-conditioning temperature and light brightness, and when leaving home, it will automatically shut down the device or adjust it to energy-saving mode.

[0022] More specifically, through the intelligent scheduling of the collaborative intelligent control model, the system can automatically adjust the operation of equipment according to the activities of family members and environmental changes, reduce unnecessary energy consumption, and 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 user's comfortable range while avoiding waste of resources between devices. The system can gradually optimize the scene collaborative intelligent control model through learning of historical data and user behavior, making the collaborative work between devices more and more efficient and in line with the user's personal needs and habits.

[0023] Specifically, in step S2 of the embodiment provided by the present invention, the working effect of each intelligent control device is analyzed according to the scene collaborative intelligent control model. The working effect of the device refers to its impact on the perceived object (for example, people in the room, temperature, humidity, etc.) under specific environmental conditions, for example, the effect of the temperature control of the intelligent air conditioner on the temperature change in the room, the impact of the brightness adjustment of the intelligent lamps on the ambient brightness, etc.

[0024] More specifically, the perception objects that interact with the device are analyzed, including environmental factors (such as temperature and humidity, light, air quality, etc.) and user-related factors (such as user activities, location, preferences, etc.). These perception objects are the reference basis for the device to dynamically adjust according to its working results. For example, the temperature adjustment of the air conditioner will affect the feedback of the indoor temperature perception object, and the brightness change of the light will affect the perception of the human eye. By analyzing the interaction between the device and the perception object, the impact of the device on the perception object is identified. For example, the ambient temperature and humidity data perceived by the smart temperature and humidity sensor is 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 an interactive closed loop.

[0025] More specifically, based on the scene collaborative intelligent control model, the system needs to collect the working status data of each device and the status data of the perception 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 status of the perception object that interacts with the device (such as indoor temperature, light intensity, etc.). These real-time data are input into the scene real-time simulation framework to simulate the working effects of each device in the current environment. For example, the comprehensive performance of devices such as air conditioners adjusting temperature, lamps adjusting brightness, and audio adjusting volume in the current scene is simulated, and the mutual influence between devices is considered. Based on the simulation results, the matching degree between the device effects in the current scene and the perception object is evaluated. For example, whether the temperature and humidity sensors are interfered with by the air flow of the air conditioner, or whether the lighting adjustment meets the needs of users.

[0026] More specifically, analyze the possible interference sources between different devices. For example, the airflow of the air conditioner may affect the perception accuracy of the temperature and humidity sensor, and the volume of the audio equipment may affect the working effect of other sensors in the environment (such as noise sensors). Through the scene collaborative intelligent control model, identify these potential interference sources, analyze the degree of impact of these interference sources on the device perception objects, and determine which interference between devices is significant. For example, the airflow of the air conditioner may cause a large error in the reading of the temperature and humidity sensor, while the brightness change of the lamp may not affect the temperature and humidity sensor. In this way, identify the interference effects between devices, and formulate strategies for interference removal or mitigation based on the interference analysis. For example, reduce interference to the temperature and humidity sensor by adjusting the air conditioner wind direction or wind speed, or avoid perception interference by adjusting the working mode of the device (such as automatic adjustment of the brightness of the lamp).

[0027] More specifically, the results of the scene real-time simulation framework and the perception interference analysis framework are integrated to form a comprehensive feedback mechanism. This mechanism can evaluate the performance of the equipment in the working process in real time and analyze the perception interference between devices. By real-time monitoring of the interaction between the equipment and the perception object, the system can adjust the working state of the equipment in real time according to the changes in the scene. For example, when it is recognized that the air flow of the air conditioner interferes with the temperature and humidity sensors, the system will automatically adjust the air direction of the air conditioner to reduce interference.

[0028] It is understandable that through the analysis of the working effect of the equipment and the interactivity of the perceived objects, it is possible to accurately predict the impact of each device on the environment and the user. For example, it is possible to predict whether the temperature control of the air conditioner can reach the comfortable ambient temperature expected by the user within the set time, and whether the brightness adjustment of the lamps meets the user's needs. Through the scene real-time simulation framework, the system can dynamically adjust the working mode of the equipment according to the actual working status and environmental conditions of the equipment to ensure the optimal collaborative work between the equipment. For example, it can optimize the interaction between the air conditioner and the temperature and humidity sensors to ensure the stability and comfort of the environment.

[0029] More specifically, through the perception interference analysis framework, the system can identify potential sources of interference between devices and optimize device settings to reduce performance degradation caused by interference between devices. For example, it can adjust the working mode of the device to avoid measurement errors caused by interactions between devices. Through real-time simulation feedback and optimization strategies, the automation control capabilities 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 efficient collaboration of the equipment. The system can monitor the interaction between the device and the perceived object in real time, and adaptively adjust the device according to 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.

[0030] Specifically, in steps S3 and S4 of the embodiment provided by the present invention, the working status of each intelligent control device in the scene (such as intelligent air conditioning, intelligent lighting, smart home audio, etc.) is obtained in real time through sensors or communication protocols, and the working status parameters include: air conditioning, temperature setting value, wind speed, operation mode (cooling / heating), indoor temperature and humidity, etc.; lighting equipment, brightness setting, switch status, color temperature (such as RGB adjustment), working hours, etc.; other equipment (such as curtains, audio, humidifiers, etc.), their working parameters (such as switch status, volume, humidity adjustment, etc.) should also be collected in real time.

[0031] More specifically, in addition to the device status, the status data of the perception object (such as ambient temperature and humidity, air quality, light intensity, noise, etc.) also needs to be collected in real time. These perception data provide a key basis for subsequent scenario simulation and interference analysis. The working parameters of each device are collected in real time through the device's embedded sensors, smart gateways, and Internet of Things protocols (such as Zigbee, Wi-Fi, Bluetooth, etc.). The data acquisition system should have high-frequency and high-precision acquisition capabilities to ensure that it can capture the dynamic changes of devices and perception objects.

[0032] More specifically, the real-time collected equipment working parameters and perception object status data are substituted into the intelligent control model to calculate the intelligent control features of the current scene. The intelligent control features reflect the interaction between the equipment and the environment (including the perception object) under the current working state. For example, the working efficiency of the air conditioner is calculated based on the current indoor and outdoor temperature difference, the set temperature of the air conditioner, the wind speed, etc. The working effect of the air conditioner, the adjustment effect of the lighting, the impact of the brightness change of the current lighting system on the indoor lighting environment, the comprehensive comfort index, and the combination of temperature, humidity, light, noise and other perception data are used to calculate a comprehensive comfort index.

[0033] More specifically, the real-time intelligent control features are substituted into the scene real-time simulation framework, which uses the working parameters of the intelligent control device and the real-time data of the perceived object to simulate the actual effect of the device in the current working scene, for example, simulating the impact of the air-conditioning set temperature on the temperature in the room, simulating the impact of lighting adjustment on indoor brightness changes, and even the impact on other perceived data such as noise and air quality. The real-time intelligent control features are substituted into the perception interference analysis framework to analyze the mutual interference between devices or between devices and perceived objects. For example, the airflow of the air conditioner may affect the perception accuracy of the temperature and humidity sensor, and the operation of the lighting equipment may interfere with the readings of the light sensor.

[0034] More specifically, the scene real-time information matrix includes various impacts of the equipment on the environment, such as temperature changes: the result of the combined effect of air conditioning and ambient temperature, lighting brightness changes: the result of the combined effect of light adjustment and ambient light intensity, air quality changes: the combination of adjustment by equipment such as humidifiers and environmental perception; the perception interference information matrix summarizes the degree of mutual interference between devices and the degree of interference to the perception object, for example, the impact of air conditioning airflow on temperature and humidity sensors, the impact of lighting equipment on light sensors, etc. These two matrices are usually stored in the form of two-dimensional matrices. The elements of the matrix represent the relationship or interference intensity between different devices or perception objects. Through matrix analysis, we can further optimize equipment scheduling, reduce interference, and improve the scene intelligent control effect.

[0035] More specifically, the scene actual information matrix and the perceived interference information matrix are combined to form a comprehensive feedback mechanism. Based on the real-time simulation and interference analysis results, the system automatically adjusts the working status of the equipment and optimizes the overall environmental performance. According to the scene actual situation and interference information feedback, the system can make adaptive adjustments. For example, when it is identified 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 when the perceived interference is large, to ensure that the comfort and effect in the scene are maximized.

[0036] It is understandable that by collecting equipment working parameters and perception object data in real time, the current environmental status can be accurately grasped, and real-time intelligent control features can be calculated, thereby performing precise environmental regulation. By substituting the scene real-time simulation framework and the perception interference analysis framework, the synergy and mutual interference of different devices can be fully simulated and analyzed, the working mode of the equipment can be optimized, and efficient collaboration can be achieved. Through the perception interference analysis framework, the system can identify the interference relationship between devices or between devices and perception objects in real time, adjust equipment parameters in time, avoid interference affecting environmental quality, and improve user experience. According to the scene real-time information matrix and the perception interference information matrix, the system can automatically adjust the working status of the equipment according to real-time feedback, so that the equipment and environment in the scene 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 life experience.

[0037] Specifically, in steps S5 and S6 of the embodiment provided by the present invention, the matrix includes the operating status of all devices in the specified working scene and the interaction between the devices and the environment, which provides data support for the synergy and mutual interference between devices. The matrix records the degree of interference between devices and between devices and perceived objects. By analyzing this interference information, it is possible to identify which devices may cause interference and affect the environmental perception results.

[0038] More specifically, based on the scene real-time 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 scene. The interference signals may come from the device's electromagnetic radiation, temperature changes, noise, etc., affecting the sensor's accurate perception of the environmental state. Based on the interference information, the system calculates and proposes collaborative adjustment strategies between devices to achieve the effect of removing or reducing interference. For example: if the temperature sensor is affected by the air-conditioning wind speed, the system can adjust the air-conditioning wind speed or adjust the position of the sensor to reduce interference. If the light of the lighting equipment 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, the collaborative control strategy is optimized, and the accuracy of scene perception and the efficient operation of the equipment are guaranteed.

[0039] More specifically, in the end, through the above-mentioned collaborative countermeasures analysis, the system obtains 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 how to coordinate with other devices to reduce possible perception interference, such as: air conditioning, adjust the wind speed or change the temperature setting to avoid affecting the temperature and humidity sensors, lighting, adjust the brightness or angle to avoid interfering with the ambient light sensor, sensor, adjust the sampling frequency or position to reduce the impact of environmental noise or air flow on perception data.

[0040] More specifically, based on the analysis results, a perception coordination strategy matrix is ​​formed for each device. Each element in the matrix represents a device's perception adjustment strategy in a specific scenario or its coordinated action with other devices. Through matrix visualization, the system can better understand the collaborative relationship between devices and their respective adjustment goals.

[0041] More specifically, based on the generated perception collaborative countermeasures, the system will drive each device to make adjustments according to the collaborative countermeasures, for example, adjusting the working mode of the air conditioner, controlling the brightness of the lighting equipment, or adjusting the position of the sensor. During the device adjustment process, the system continuously monitors the perception data to ensure dynamic feedback between the device working status and the collaborative countermeasures. For example, when the signal received by the temperature and humidity sensor is interfered by the airflow of the air conditioner, the system eliminates the interference by collaboratively adjusting the air speed or temperature of the air conditioner and optimizes the sensor data.

[0042] More specifically, after the adjustment of the coordinated countermeasures, the device continues to collect perception data. At this time, the data collected by the sensor should no longer be affected by the interference, or the interference has been minimized, and 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 perception interference still exists in some cases, the system will continue to adjust the working parameters of the device based on real-time feedback to ensure the effect of removing perception interference.

[0043] More specifically, after the implementation of perception and collaborative countermeasures, the system continuously collects equipment operation data and perception data, and analyzes the effectiveness of the current collaborative countermeasures. If new interference sources are found or the collaborative effect is poor, the system will automatically adjust the collaborative strategy and optimize the working status of the equipment to form a closed-loop optimization mechanism. The system can also use machine learning algorithms to further optimize collaborative countermeasures based on historical data and real-time feedback, thereby improving the system's adaptability and perception effects in different working scenarios.

[0044] It is understandable that through the analysis and implementation of collaborative countermeasures, it is possible to accurately identify and remove the perception interference between devices, avoid unnecessary mutual influence during the operation of devices, and ensure the accuracy of environmental perception data. Through collaborative countermeasures, devices can be intelligently coordinated, so that each device can dynamically adjust according to the status of other devices, reduce interference, optimize work efficiency, and achieve high coordination of intelligent control systems. After the perception interference is effectively removed, the system can obtain accurate environmental status data in real time and provide high-quality perception results. This provides more precise support for applications such as smart homes and automated environmental control.

[0045] More specifically, the system has the ability to adjust in real time and can dynamically optimize according to the changing working environment and equipment status to ensure that the synergy effect and perception results of the equipment are continuously maintained in the best state. By reducing interference between devices and improving the accuracy of perception data, users will be able to enjoy a more intelligent, comfortable, and energy-saving working and living environment. Through the coordinated optimization between devices, the waste of resources is avoided. For example, excessive operation or malfunction of equipment caused by interference is reduced, and overall energy efficiency is improved.

[0046] The present invention provides a method for collecting signals of an intelligent control device, which has the following beneficial effects: The present invention performs digital simulation on each intelligent control device to obtain a scene collaborative intelligent control model, forms a scene real-time simulation framework and a perception interference analysis framework based on the model analysis, collects real-time intelligent control features and substitutes them into the framework to obtain a scene real-time information matrix and a perception interference information matrix, obtains the perception collaborative countermeasures of each device according to the matrix analysis, drives the device to work and perceive the scene according to the collaborative countermeasures, and realizes signal collection and interference removal. This method can eliminate the perception interference between intelligent control devices through precise modeling and real-time feedback, realize collaborative work between devices, provide more precise control and perception capabilities for the devices, and solves the problem in the prior art that the work between multiple intelligent control devices affects the perception capabilities of the devices.

[0047] Preferably, the step 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 includes: S11: collecting 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, so as to obtain device working performance data, device perception performance data, and device setting location data of each intelligent control device, wherein the intelligent control device working performance data, device perception performance data, and device setting location data together constitute device performance association information of the intelligent control device; S12: simulating and combining the working and perceptual performances of each of the intelligent control devices according to the device working performance data and the device perceptual performance data of each of the intelligent control devices, so as to obtain a basic simulation unit corresponding to each of the intelligent control devices; S13: According to the device setting position data of each of the intelligent control devices, each of the basic simulation units is integrated and processed, and according to the device setting position data of each of the intelligent control devices, the range of working performance and perception performance of each of the basic simulation units after the overall integration is adjusted to obtain a scene collaborative intelligent control model.

[0048] Specifically, in order to fully understand the performance of each intelligent control device in a specified working scenario, it is first necessary to collect data on the device from multiple dimensions. These dimensions include: device working performance, collecting the device's operating status data (for example, the device's load, operating speed, power consumption, etc.), which reflect the device's working ability in the scenario; device perception performance, collecting the device's perception data (such as sensor accuracy, sensitivity, response time, etc.), which represents the device's ability to perceive environmental changes; device setting position, recording the position of each device in the scene and its relative position relationship (for example, the device's coordinates, installation angle, working range, etc.).

[0049] More specifically, the above three types of data (equipment working performance data, equipment perception performance data, and equipment setting location data) are integrated together to form the equipment performance association information of each intelligent control device. This information provides multi-dimensional performance parameters of the equipment, so that subsequent simulation analysis can reflect the comprehensive performance of the equipment in actual work.

[0050] More specifically, based on the acquired equipment working performance data and equipment perception performance data, the following simulations are performed: working performance simulation, based on the equipment working performance data, the equipment working status model is established. By simulating the operation of the equipment in a specified working scenario, the possible operating status, load distribution and performance changes of the equipment are predicted; perception performance simulation, based on the equipment perception performance data, a model of the equipment's response to environmental changes is established. This simulation can help predict the equipment's ability to respond to environmental changes in different scenarios, including perception accuracy, delay, noise interference, etc., and combine working performance and perception performance. Through mathematical models or algorithms (for example, linear regression, neural networks, etc.), the basic simulation unit of each device is obtained. Each basic simulation unit represents the comprehensive performance of the device in a specified scenario (including performance in both working and perception aspects).

[0051] More specifically, the actual position of each device in the scene will affect its performance. For example, the distance of the device from the target area, the position angle, and obstructions may affect its working and perception capabilities. Therefore, it is necessary to integrate the basic simulation units as a whole according to the device's setting position data, and adjust the performance of each basic simulation unit according to the device's setting position data. For example, if the device is set in a complex environment or near an interference source, the device's working performance may be reduced. At this time, the performance is corrected according to the specific location of the device; the perception performance range is adjusted. The perception capability of the device will also be affected by its location. For example, the detection range of the sensor may change due to changes in the position angle, or the device may be affected by ambient light, noise, etc., and the perception accuracy may be different. Combined with the location data of all devices and the correlation between them, the comprehensive working and perception capabilities of each device at its location are obtained. At this time, the working performance and perception performance of the device have been adjusted and optimized accordingly according to its location.

[0052] More specifically, after completing the combination of the basic simulation units of each device and adjusting the position dependence, the simulation units of all devices are combined as a whole to obtain a scenario-based collaborative intelligent control model, which describes the comprehensive behavior of each intelligent control device in a specified working scenario, including the interaction between devices, collaborative working modes, perception results and their optimization adjustments. In the model, by calculating the synergy between devices (such as interference between devices, benefits of collaborative work, etc.), the intelligent control of the entire scene is optimized, and through optimization algorithms (such as optimized scheduling, optimal path planning, etc.), the work efficiency and perception accuracy of the overall system are further improved. The obtained scenario collaborative intelligent control model can not only provide a decision-making basis for collaborative scheduling between devices, but also realize real-time adjustment and optimization, adapt to various environmental changes in the scene, thereby improving the work and perception effects of the equipment in actual applications.

[0053] It is understandable that through the multi-dimensional data collection and analysis of equipment working performance, perception performance and setting location, the system can accurately simulate the performance of each device in a specified working scenario, and provide a reliable basis for subsequent intelligent control decisions. By combining the working and perception performance of the equipment for simulation and adjusting according to the location data, the comprehensive performance of each device can be comprehensively improved, so that it can perform the best working and perception capabilities in actual working scenarios. Based on the generated collaborative intelligent control model, the system can dynamically adjust the collaboration mode of each device, optimize the overall work efficiency and perception effect, and improve the collaborative efficiency between devices.

[0054] More specifically, the final scenario-based collaborative intelligent control model can provide real-time decision support for the automation system, ensuring that equipment can work together under different environments and conditions, avoiding mutual interference between equipment, and improving the overall system intelligence level. Through real-time monitoring and simulation, the system can dynamically adjust according to environmental changes to keep equipment performance in the optimal state. Whether it is environmental changes or equipment failures, it can respond quickly and adjust strategies 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.

[0055] 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: 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; 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; 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; 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; 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; S26: mapping working parameters of the perceived interference status curves of each basic simulation unit according to the mapping relationship between each basic simulation unit and the collaborative work effect characteristic map, so as to obtain a mapping relationship between the perceived interference status curves of each basic simulation unit and the working parameters of each basic simulation unit; S27: Based on the mapping relationship, reverse analysis and correlation integration of the factors causing the perceived interference are performed on each of the perceived interference state curves to obtain a perceived interference analysis framework of the scene collaborative intelligent control model.

[0056] Specifically, for each basic simulation unit (i.e., each intelligent control device) in the scene collaborative intelligent control model, the traversal setting of executable working parameters is performed. This step refers to the system's multi-dimensional setting and adjustment of the working parameters of each device, covering various possible states of device operation, including workload, operating speed, sensor sensitivity, etc., to generate a sequence of preliminary working parameters for each basic simulation unit, i.e., all possible combinations of working parameters.

[0057] More specifically, based on the generated sequence of preparatory working parameters, an independent device working effect simulation is performed on each basic simulation unit. The simulation process should take into account the working status, response capability and performance of the device under different parameter settings, and generate an independent working effect characteristic curve for each basic simulation unit under different preparatory working parameters. The curve reflects the performance of the device under various working conditions, such as efficiency, power consumption, response time, etc.

[0058] More specifically, based on the scenario-based collaborative intelligent control model, the independent working effect characteristic curves of each basic simulation unit are interactively superimposed on the collaborative working effect characteristics of multiple devices. That is, in the simulation process, the interaction between devices is considered, and the collaborative working effects are superimposed to demonstrate the comprehensive performance of the devices in an integrated environment. A complete collaborative working effect characteristic map is obtained, which reflects the overall performance of different devices when working together, including collaborative gains, collaborative losses and influencing factors between devices.

[0059] More specifically, based on the obtained collaborative work effect characteristic map, the working parameters of each basic simulation unit are mapped to determine the relationship between the working parameters of each device and the collaborative work effect. Through these mapping relationships, the factors that produce the collaborative effect are further reversely analyzed and integrated in terms of correlation. This means that based on the performance of the collaborative work effect, it is analyzed which factors (such as device location, performance differences, interference, etc.) have a positive or negative impact on the collaborative effect, and a complete scenario real-time simulation framework is obtained, which not only describes the independent performance of each device, but also reflects the collaborative effects between devices and the impact of their interactions on the overall effect.

[0060] 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.

[0061] 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.

[0062] 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.

[0063] 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.

[0064] 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.

[0065] More specifically, through a comprehensive analysis of working parameters and perceived interference, targeted optimization solutions can be provided for the equipment to ensure that when working collaboratively, the equipment can not only maintain optimal working performance but also minimize perceived interference, thereby improving the overall intelligence level of the system. The resulting scene collaborative intelligent control model has the ability to adaptively adjust and can respond to environmental changes and device status changes in real time. By optimizing working parameters and perception configuration, it ensures that the system can perform optimally in various scenarios.

[0066] Preferably, 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: S31: deploying a network data interface for each of the intelligent control devices, so that each of the intelligent control devices is in data connection with a designated control center unit through the deployed network data interface; S32: collecting working parameters of each intelligent control device in real time through the control center unit in data communication with each intelligent control device, so as to obtain the real-time working parameters of each intelligent control device; 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 scene.

[0067] Specifically, network data interfaces are deployed on each intelligent control device to ensure that these devices can transmit and interact with the control center through the network. The choice of network data interfaces may include wireless or wired network interfaces, depending on the needs of the device and the environment, to ensure that each intelligent control device can establish data connectivity with the control center to achieve remote monitoring and data exchange.

[0068] More specifically, real-time working parameters are collected through the data connectivity established between the control central unit and each intelligent control device. The control central unit will obtain the real-time working data of each device through the network interface, including but not limited to the device's load, operating status, power consumption, sensor data, etc., and obtain the real-time working parameters of each intelligent control device. These parameters will provide basic data for subsequent real-time intelligent control feature analysis.

[0069] 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.

[0070] 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.

[0071] 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.

[0072] 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.

[0073] 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.

[0074] 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: S41: 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; S42: performing effect analysis and vector representation of each specific position of the designated work scene according to the collaborative work effect feature to obtain scene real-time vectors of each specific position of the designated work scene, and combining the scene real-time vectors of each specific position of the designated work scene to obtain a scene real-time information matrix of the designated work scene; S43: 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; S44: Performing perceptual interference analysis and vector representation of each specific location of the designated work scene according to the perceptual interference condition characteristics to obtain perceptual interference vectors of each specific location of the designated work scene, and combining the perceptual interference vectors of each specific location of the designated work scene to obtain a perceptual interference information matrix of the designated work scene.

[0075] Specifically, the intelligent control features collected in real time are substituted into the scene real-time simulation framework, which will map these features and focus on factors affecting collaborative work effects, such as interaction between devices, resource allocation, work efficiency, etc. Through this process, the simulation framework generates collaborative work effect characteristics under specified work scenarios based on real-time intelligent control features, understands and quantifies the collaborative work effects of each device or system in a specific working environment, such as how multiple smart devices coordinate to achieve optimal performance.

[0076] More specifically, based on the characteristics of collaborative work effects, the effects of each specific location in the specified work scene are analyzed, and the analysis results are converted into vector representations. These vectors represent the working status of different locations or areas, reflecting the work effectiveness or collaborative efficiency of each location in the scene, and generating a scene real-time vector containing the working status of each location, providing a data basis for subsequent scene analysis.

[0077] More specifically, the scene live vectors of all specific locations are combined to form a comprehensive scene live information matrix. This matrix can express the comprehensive working status of each location in the specified work scene, including information such as location, equipment status, and collaborative work efficiency. By obtaining a complete scene live information matrix, it can present the real-time dynamic status of the work scene in all aspects.

[0078] More specifically, the real-time intelligent control features are substituted into the perception interference analysis framework. The perception interference analysis framework maps these features and focuses on possible interference factors, such as communication signal interference, equipment failure, external environment interference, etc. The framework evaluates how these interference factors affect the perception performance of the entire work scene, quantifies the impact of perception interference on the specified work scene, and generates relevant perception interference status features.

[0079] More specifically, based on the characteristics of the perceived interference conditions, interference analysis is performed on each location of the specified work scene, and these analysis results are converted into perceived interference vectors. Each perceived interference vector represents the intensity and nature of the interference in a specific location or area. A detailed analysis of the perceived interference is obtained, and these interferences are expressed in vector form to facilitate subsequent analysis and processing.

[0080] More specifically, the perceived interference vectors of all positions are combined to form a perceived interference information matrix. This matrix reflects the distribution of perceived interference in different working positions or areas, and can provide detailed data support for subsequent optimization and interference management. It completes the generation of the perceived interference information matrix and presents the interference status of each position in a specified working scenario.

[0081] It is understandable that by mapping the intelligent control features through the scene real-time simulation framework, the collaborative work effect in the specified work scene can be fully simulated and analyzed. This can not only evaluate the working status of each device in different positions, but also reveal the collaborative effect between devices and optimize resource allocation. Vector representation and effect analysis refine the working effect of the scene to each specific location or area. This refined representation provides a more accurate data basis for optimizing workflows and adjusting strategies. Through the interference mapping of the perception interference analysis framework, the impact of various types of interference can be quantified in real time. By generating perception interference vectors and information matrices, the interference risks of different work positions can be effectively managed and identified to ensure the stable operation of the system.

[0082] More specifically, the generated perception interference information matrix provides dynamic monitoring capabilities for global and local interference conditions. This matrix can help identify potential interference problems and provide decision support for the location and optimization of interference sources. After obtaining the scene real-time information matrix and the perception interference information matrix, the system can provide real-time feedback and intelligent optimization based on this information, adjust the working status of the equipment, collaborative strategy or interference management strategy to improve the overall system performance, and accurate interference analysis and collaborative work effect mapping will help improve the stability and robustness of the system in a changing environment. It can predict and respond to various interference situations to keep the system running efficiently.

[0083] Preferably, the step of performing collaborative countermeasure analysis of perceptual interference removal on 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 perceptual collaborative countermeasures for each intelligent control device of the specified working scene includes: S51: 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; S52: performing vector cluster mapping of the perceived interference status of each intelligent control device of the specified working scene based on the perceived interference vector cluster set, so as to obtain a perceived interference index of each intelligent control device of the specified working scene corresponding to the perceived interference vector cluster set; S53: performing a scene overall effect evaluation on the designated work scene based on the scene actual vector cluster set to obtain a scene overall effect index of the designated work scene; S54: 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; S55: analyzing the working parameter adjustment requirements of the intelligent control device in the scenario collaborative intelligent control model based on various perception interference vector cluster adjustment forms, so as to obtain device working parameter adjustment features corresponding to various perception interference vector cluster adjustment forms; S56: performing corresponding correction analysis on the overall effect index of the scene according to the device working 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; S57: 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 a perceptual interference vector cluster adjustment form for actual application according to the evaluation result; S58: Analyze and combine the working parameters and the perception parameters of each of the intelligent control devices according to the perception interference vector cluster adjustment form determined for actual application, and obtain the perception coordination countermeasures of each of the intelligent control devices.

[0084] Specifically, a vector clustering operation is performed on the scene reality information matrix and the perception interference information matrix to generate a scene reality vector cluster set and a perception interference vector cluster set. This operation clusters multiple data points to group similar scene reality and interference conditions into a group for easy subsequent analysis and processing, and simplifies complex scene reality and perception interference information into fewer cluster sets for effective analysis and processing of this information.

[0085] More specifically, based on the perceptual interference vector cluster set, the perceptual interference condition of each intelligent control device is mapped to the corresponding perceptual interference vector cluster set, thereby obtaining the perceptual interference index of each device. This step helps to quantify the perceptual performance of each device under different interference conditions, evaluate the perceptual interference impact of each intelligent control device, and obtain its perceptual interference index for further processing and optimization.

[0086] More specifically, based on the set of scene real-time vector clusters, the overall scene effect evaluation is performed, and the scene overall effect index of the entire work scene is calculated. This index comprehensively reflects the effect of the entire work scene, including the performance in aspects such as equipment collaboration and resource allocation. The overall effect evaluation result of the work scene is obtained to provide guidance for subsequent optimization.

[0087] More specifically, the perceived interference index of each intelligent control device is reduced, which means that by optimizing the working state, adjusting the control strategy and other means, the negative impact of interference on device perception is reduced, the perceived interference index of the device is reduced, and the perception ability and working efficiency of the device are improved.

[0088] More specifically, according to the perceived interference index after the descent processing, the set of perceived interference vector clusters is reversely fed back to adjust the expression form of the perceived interference vector clusters. This means that during the optimization process, the perceived interference status will be re-evaluated to form several new adjustment forms of the perceived interference vector clusters, thereby optimizing the performance of the perceived interference vector clusters and enabling them to better adapt to the needs of intelligent control devices, thereby further improving the performance of the devices.

[0089] More specifically, based on the adjustment form of the perceptual interference vector cluster, the adjustment requirements of the working parameters of the intelligent control equipment are analyzed. This step aims to analyze and calculate the requirements of different adjustment forms for the equipment working parameters, find the most appropriate adjustment plan, determine the equipment working parameters that need to be adjusted, and ensure that the equipment can better adapt to the working state after the perceptual interference is removed.

[0090] More specifically, based on the adjustment characteristics of the equipment operating parameters, a correction analysis is performed on the overall effect index of the scene. This means that after adjusting the equipment operating parameters, it is necessary to evaluate whether the overall effect of the scene has been optimized and calculate the predicted correction effect index. Through correction analysis, the impact of different adjustments on the overall effect of the scene is quantified to ensure the effectiveness of the adjustment plan.

[0091] More specifically, the predicted correction effect index is used as a supervision condition to evaluate the feasibility of various perceptual interference vector cluster adjustment forms. This evaluation helps to determine which adjustment schemes can effectively remove interference and provide feasible solutions for practical applications, determine the most effective perceptual interference removal scheme, and ensure the feasibility of interference removal.

[0092] More specifically, based on the determined actual application perception interference vector cluster adjustment form, the working parameters and perception parameters of each intelligent control device are analyzed and combined. This is to ensure that the equipment can maintain the best perception ability and working state during engineering implementation. Through the combination of working parameters and perception parameters, specific perception coordination countermeasures are formed to ensure that the equipment can effectively reduce perception interference in actual applications.

[0093] More specifically, by integrating the engineering and perception parameter analysis results of each device, specific perception coordination countermeasures are formulated. These countermeasures can help devices collaborate more effectively in complex working environments, reduce perception interference, and optimize overall working results, ensuring that each device can work in coordination with other devices to maximize the overall performance of the system, while eliminating perception interference and improving the stability and efficiency of the system.

[0094] It is understandable that through vector clustering operations and perceptual 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 perceptual interference index, the negative impact of perceptual interference on device performance can be significantly reduced. The optimized perceptual interference removal solution can improve the device's perception ability and work efficiency.

[0095] More specifically, the correction analysis of the scene's overall effect index and the evaluation of the feasibility of perceptual interference removal provide a dynamic feedback mechanism for optimization, which can timely adjust the optimization plan according to the actual effect to ensure that the system's performance continues to improve. Through the combined analysis of working parameters and perceptual parameters, the perceptual coordination countermeasures formulated can ensure efficient collaboration between devices in complex scenarios. This improves the stability and robustness of the system and ensures efficient operation in uncertain environments. The final perceptual interference vector cluster adjustment form not only removes interference, but also optimizes the working parameters of the device to ensure that the system can achieve optimal performance under the condition of minimizing interference.

[0096] Preferably, the step 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 includes: S61: adjusting the working parameters of each of the intelligent control devices according to the perception coordination strategy, 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 perception interference of the designated working scene; S62: Perception-driven perception parameters of each of the intelligent control devices are implemented according to the perception coordination strategy, so that each of the intelligent control devices performs signal collection and processing on a designated working scene in a perception interference removal state, so as to realize perception interference removal signal collection of the intelligent control device on the designated working scene.

[0097] Specifically, the working parameters of the intelligent control device are adjusted. According to the perception coordination strategy, the working parameters of each intelligent control device are adjusted. This adjustment is intended to enable the device to exert appropriate influence in a given working scenario to achieve the goal of removing perceptual interference. For example, the control strategy, power, output signal strength, etc. of the device are adjusted. By optimizing the working parameters of the device, it can more effectively exert influence on the working scenario, thereby reducing or removing perceptual interference.

[0098] More specifically, after the working parameters are adjusted, each intelligent control device exerts a corresponding influence on the working scene according to the adjusted working parameters. The device interacts with the scene through the adjusted working mode, thereby reducing or removing perceptual interference, ensuring that perceptual interference in the working scene is removed, and enabling the intelligent control device to collect and process signals more accurately.

[0099] More specifically, according to the perception coordination strategy, the perception parameters of each intelligent control device are driven to ensure that the device has the appropriate perception capability to collect scene signals after interference is removed. This may involve adjusting the sensor sensitivity of the device, optimizing the perception algorithm, etc., to improve the perception capability of the device in the scene after removing the perception interference, ensure that they can accurately collect scene signals, and provide reliable data for subsequent analysis and processing.

[0100] More specifically, each intelligent control device performs signal collection and processing after the perception interference is removed. At this time, the perception parameters of the device have been adjusted, and can effectively collect and process signals in the working scene to remove the perception interference, ensuring that the device can efficiently collect and process the signal after the interference is removed, and further optimizing the perception capability and performance of the entire system.

[0101] More specifically, the device provides signals after removing perceived interference through signal acquisition processing. These signals can be used in system optimization, environmental monitoring, equipment adjustment, etc., thereby enhancing the effect of removing perceived interference of intelligent control devices on specified working scenes, realizing the removal of perceived interference of intelligent control devices on working scenes, and ensuring the device's optimal signal acquisition capability with minimized interference.

[0102] More specifically, by adjusting the working parameters of the intelligent control device, the adaptability of the device in complex environments can be significantly improved. By optimizing the working parameters, the device can more effectively control and reduce interference when interference exists, thereby improving the overall performance of the device. By driving the perception parameters, the perception ability of the device is enhanced. The adjusted perception parameters enable the device to more accurately identify and process signals after interference removal, ensuring that the collected signals are of higher quality, thereby improving the perception efficiency of the system.

[0103] More specifically, through fine adjustment of working parameters and perception parameters, the device can effectively remove perception interference and reduce the impact of external noise on device perception, which enables intelligent control devices to work in a clearer and more reliable environment, improving the stability and accuracy of the system. The optimization of device signal acquisition and processing ensures that the signal 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.

[0104] More specifically, through the coordinated adjustment of the perception and working parameters of each device, intelligent control devices can work together more efficiently, and the working and perception capabilities of each device are optimized synchronously, thereby improving the collaborative efficiency and overall performance of the entire system in complex environments.

[0105] In a second aspect, the present invention provides a device for collecting signals of an intelligent control device, which is used to implement a method for collecting signals of an intelligent control device as described in any one of the first aspects.

[0106] In a third aspect, the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program executable on the processor, and when the processor executes the computer program, the method for collecting signals of an intelligent control device as described in any one of the first aspects is implemented.

[0107] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the processor executes a signal acquisition method for an intelligent control device as described in any one of the first aspects.

[0108] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in 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 perception interference removal signal collection of the intelligent control device for the designated working scene.

2. The intelligent control device signal acquisition method according to claim 1, characterized in that: 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; The basic simulation units are integrated according to the device setting position data of each intelligent control device, and the range of working performance and perception performance of each basic simulation unit after the overall integration is adjusted according to the device setting position data of each intelligent control device to obtain a scene collaborative intelligent control model.

3. The intelligent control device signal acquisition method according to claim 2, characterized in that: 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, the factors causing the perceived interference are reversely analyzed and integrated in terms of correlation for each of the perceived interference state curves, so as to obtain a perceived interference analysis framework for the scene collaborative intelligent control model.

4. 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.

5. The intelligent control device signal acquisition method according to claim 3, characterized in that: 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.

6. The intelligent control device signal acquisition method according to claim 5, 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.

7. 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.

8. 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 1-7.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program executable on the processor, wherein: When the processor executes the computer program, the method for collecting signals of an intelligent control device as described in any one of claims 1 to 7 is implemented.

10. 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 7.

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