Sensor grouping design method of electronic frequency hopping ultrasonic mouse repeller

Abstract

The invention provides a sensor grouping design method for an electronic frequency hopping ultrasonic mouse repeller, which comprises the following steps: acquiring various types of sensors which at least comprise an infrared sensor, a vibration sensor and a sound sensor; acquiring the working frequency and the signal acquisition mode of each type of sensor; judging whether signal interference exists among different types of sensors according to the working frequency and the signal acquisition mode; if the signal interference exists, adjusting the arrangement positions of the sensors until the signal interference does not exist among different types of sensors; acquiring response time of different types of sensors, and determining a data acquisition time window of each type of sensor so as to realize synchronous acquisition of sensor data; according to the power consumption of each type of sensor, the working mode of the sensor is optimized, and the overall power consumption is reduced while the monitoring requirement is met.

Description

Technical Field

[0001] The invention relates to the field of information technology, and in particular to a sensor grouping design method for an electronic frequency-hopping ultrasonic mouse repeller. Background Art

[0002] The core technical challenge faced by the intelligent mouse repellent system is how to accurately identify and locate rodent activities in complex environments. Traditional single sensor solutions have problems such as detection blind spots and high false alarm rates. Although multi-sensor collaboration can improve the comprehensiveness of detection, it has caused new technical contradictions. First, there is signal interference between different types of sensors (such as infrared, vibration, and sound), which affects the detection accuracy. Secondly, the asynchronous nature of multi-sensor data acquisition leads to inconsistent information, which reduces the accuracy of system judgment. Furthermore, the power consumption of sensors varies greatly, and how to optimize the overall energy consumption while ensuring the detection effect becomes a problem. In addition, in complex indoor electromagnetic environments, sensors are susceptible to interference and produce false alarms. These problems are interrelated and jointly affect the recognition accuracy, positioning accuracy and service life of the system. How to overcome the technical contradictions brought about by multi-sensor collaboration while ensuring the comprehensiveness of detection and realize efficient, low-power, and anti-interference intelligent mouse repellent is the key technical challenge faced by the system. This challenge involves multiple aspects such as sensor layout, data synchronization, and power consumption management, and requires comprehensive consideration of various complex factors in actual application scenarios. Summary of the invention

[0003] The present invention provides a sensor grouping design method for an electronic frequency-hopping ultrasonic mouse repeller, which mainly includes: Acquire multiple types of sensors, the multiple types of sensors at least include infrared sensors, vibration sensors and sound sensors; for each type of sensor, acquire its operating frequency and signal acquisition mode; determine whether there is signal interference between different types of sensors according to the operating frequency and signal acquisition mode; if there is signal interference, adjust the arrangement position of the sensor until there is no signal interference between different types of sensors; obtain the response time of different types of sensors, and determine the data acquisition time window of each type of sensor to achieve synchronous acquisition of sensor data; optimize the working mode of the sensor according to the power consumption of each type of sensor, and reduce the overall power consumption while meeting the monitoring requirements. Further, the acquisition of multiple types of sensors includes: acquiring an infrared sensor for detecting changes in thermal radiation of rodents; acquiring a vibration sensor for detecting physical vibrations of rodents; acquiring a sound sensor for capturing specific frequency sound waves emitted by rodents. Further, the determination of whether there is signal interference between different types of sensors includes: acquiring electromagnetic interference information and noise information in the environment; combining the electromagnetic interference information and noise information, determining whether the signal acquisition of different types of sensors is interfered. Further, the adjustment of the arrangement position of the sensor includes: changing the spatial layout of the sensor, increasing the distance between sensors that are prone to signal interference; and blocking signal crosstalk between different sensors through isolation measures. Further, the acquisition of the response time of different types of sensors includes: setting different data acquisition frequencies for each type of sensor; determining the sampling period of each type of sensor according to the data acquisition frequency; and using the sampling period of the sensor with the longest sampling period as the unified data acquisition time window of each sensor. Further, the optimization of the working mode of the sensor includes: acquiring the power consumption parameters of each type of sensor; determining the optimal working mode of the sensor according to the power consumption parameters, including continuous work, intermittent work and event-triggered work; and configuring the sleep duration and wake-up frequency of the sensor for different working modes. Further, the method also includes: applying the group design scheme of the sensor to the electronic frequency-hopping ultrasonic mouse repellent; realizing accurate identification and positioning of rodent activities by integrating multi-sensor information, improving the working efficiency of the mouse repellent, and extending its service life.

[0004] The technical solution provided by the embodiment of the present invention may have the following beneficial effects: The present invention discloses an intelligent mouse repellent system based on multi-sensor collaboration. The system integrates infrared, vibration and sound sensors for comprehensive detection of rodent activities. In order to solve the problem of signal interference that may exist between multiple sensors, the present invention eliminates interference by analyzing environmental electromagnetic interference and noise information, optimizing the spatial layout of sensors and adopting isolation measures. At the same time, a unified data acquisition time window is designed based on the response characteristics of different sensors to achieve synchronous data acquisition. In addition, the present invention also formulates the best working mode according to the power consumption parameters of each sensor, including continuous, intermittent and event-triggered operation, and configures corresponding sleep and wake-up strategies to reduce overall power consumption. This multi-sensor collaborative solution is applied to electronic frequency-hopping ultrasonic mouse repellents, which significantly improves the recognition accuracy and positioning accuracy of rodent activities, thereby improving the efficiency of mouse repellent and extending the service life of the equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0005] Figure 1 The present invention is a flow chart of a sensor grouping design method for an electronic frequency-hopping ultrasonic mouse repeller. DETAILED DESCRIPTION In order to enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this specification, not all of the embodiments. Based on the embodiments in this specification, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of this specification.

[0006] like Figure 1 The sensor grouping design method of an electronic frequency-hopping ultrasonic mouse repeller in this embodiment may specifically include: Step S101: Acquire multiple types of sensors, where the multiple types of sensors at least include infrared sensors, vibration sensors, and sound sensors.

[0007] To obtain various types of sensors, we first need to clarify the working principle and application scenarios of each sensor. Infrared sensors sense the presence of target objects by detecting the thermal radiation emitted by them, and are often used in security monitoring and human detection. Its response time is usually in the millisecond level, which is suitable for quickly capturing dynamic changes. For example, in smart homes, infrared sensors can be used to detect human movement to trigger lights or alarm systems. Vibration sensors detect targets by sensing physical vibrations, and are often used for structural health monitoring and equipment fault diagnosis. Its data acquisition requires a longer stabilization time, usually in the second level, to ensure that the complete vibration signal is captured. For example, in bridge monitoring, vibration sensors can be used to detect the vibration frequency of the bridge to assess its structural safety. Sound sensors sense environmental sounds by capturing sound waves of specific frequencies, and are often used for voice recognition and noise monitoring. Its response time depends on the frequency and intensity of the sound waves, usually between microseconds and milliseconds. For example, in smart speakers, sound sensors can be used to capture user voice commands to achieve voice control functions. In specific implementation, the selection of infrared sensors needs to consider their detection distance and sensitivity. For example, in security monitoring, infrared sensors with longer detection ranges can cover larger areas, but sensors with lower sensitivity may not be able to detect smaller targets. The selection of vibration sensors needs to consider their frequency response range and anti-interference ability. For example, in industrial equipment monitoring, vibration sensors with a wider frequency response range can capture more types of vibration signals, but sensors with weaker anti-interference ability may be affected by environmental noise. The selection of sound sensors needs to consider their frequency resolution and signal-to-noise ratio. For example, in speech recognition, sound sensors with higher frequency resolution can more accurately recognize voice commands, but sensors with lower signal-to-noise ratio may be interfered by background noise. By acquiring multiple types of sensors, multi-dimensional monitoring of targets can be achieved. For example, in smart homes, infrared sensors can be used to detect human movement, vibration sensors can be used to detect door and window switches, and sound sensors can be used to detect abnormal sounds, thereby achieving comprehensive monitoring of home security. This multi-sensor fusion approach can improve the accuracy and reliability of monitoring and reduce false positives and false negatives. In addition, the complementarity of different sensors can also make up for the limitations of a single sensor. For example, infrared sensors may fail in an environment with insufficient light, but vibration sensors and sound sensors can still work normally, thus ensuring the continuity of monitoring. In practical applications, obtaining multiple types of sensors also requires considering their power consumption and cost. For example, infrared sensors have low power consumption and are suitable for long-term operation, but the cost is high; vibration sensors have high power consumption but low cost; and the power consumption and cost of sound sensors are between the two. Therefore, in the design, it is necessary to make trade-offs according to specific needs to achieve the best balance between performance and cost. In addition, the installation location and layout of the sensors also need to be carefully designed.For example, infrared sensors should be installed on the path of target movement, vibration sensors should be installed near the vibration source, and sound sensors should be installed near the noise source to ensure that they can effectively capture the target signal. By acquiring multiple types of sensors, the comprehensiveness and accuracy of monitoring can be improved. For example, in industrial equipment monitoring, infrared sensors can be used to detect temperature changes of equipment, vibration sensors can be used to detect vibration frequencies of equipment, and sound sensors can be used to detect noise levels of equipment, thereby achieving a comprehensive assessment of the operating status of the equipment. This multi-sensor fusion method can detect potential fault hazards in advance and avoid sudden equipment shutdowns, thereby improving production efficiency and safety. In addition, data from different sensors can also be comprehensively analyzed to provide a more comprehensive monitoring report. For example, the temperature data of the infrared sensor and the vibration data of the vibration sensor can be combined for analysis to determine whether the operating status of the equipment is normal, thereby providing a scientific basis for equipment maintenance and maintenance.

[0008] Step S102: for each type of sensor, obtain its operating frequency and signal acquisition mode; and determine whether there is signal interference between sensors of different types according to the operating frequency and signal acquisition mode.

[0009] Infrared sensors usually operate in the mid- and far-infrared bands, with a frequency range of 300 GHz to 400 THz, and their signal acquisition method is to detect the thermal radiation emitted by the target object. The operating frequency of vibration sensors usually ranges from a few Hz to a few kilohertz, and their signal acquisition method is to detect the mechanical displacement or acceleration caused by the vibration of the object. The operating frequency range of sound sensors is between 20 Hz and 20 kHz, and their signal acquisition method is to capture sound waves propagating in the air. According to these operating frequencies and signal acquisition methods, there is almost no overlap in the operating frequencies of infrared sensors and vibration sensors, so it is unlikely that signal interference will occur between them. However, there is a partial overlap between the operating frequency range of sound sensors and the frequency range of vibration sensors, especially in the low-frequency region, which may cause signal interference between sound sensors and vibration sensors. For example, when a sound sensor captures a low-frequency sound wave, it may be mistaken for a low-frequency vibration signal detected by a vibration sensor. To avoid this interference, filtering technology can be used in the signal processing stage to limit the signal of the sound sensor to a higher frequency range and the signal of the vibration sensor to a lower frequency range. In addition, there is almost no overlap in the operating frequencies between infrared sensors and sound sensors, so there is no signal interference between them. In this way, it can ensure that different types of sensors can accurately collect data when working at the same time, avoiding inaccurate data caused by signal interference.

[0010] Step S103, if there is signal interference, adjust the arrangement position of the sensor until there is no signal interference between sensors of different types; obtain the response time of sensors of different types, and determine the data collection time window of each type of sensor to achieve synchronous collection of sensor data.

[0011] If there is signal interference, adjust the sensor layout until there is no signal interference between different types of sensors. For example, in a scenario where infrared sensors and vibration sensors work at the same time, infrared sensors are sensitive to changes in thermal radiation, while vibration sensors are sensitive to physical vibrations. If the two are too close, the mechanical vibration of the vibration sensor may be transmitted through the air or the device structure, affecting the thermal radiation detection of the infrared sensor and causing data distortion. At this time, the vibration sensor can be moved to a position at least one meter away from the infrared sensor, and experimental tests can be performed to verify whether the interference is eliminated. In addition, if there is high-frequency signal interference from other electronic devices in the environment, the sensor can be placed in a shielding material (such as a metal casing) or the direction of the sensor can be adjusted to avoid direct signal coupling. Through these adjustments, signal interference can be effectively reduced and the accuracy of data collection can be ensured. Obtain the response time of different types of sensors and determine the data collection time window for each type of sensor to achieve synchronous collection of sensor data. For example, the response time of an infrared sensor is usually short, which may be around 10 milliseconds, while the response time of a vibration sensor is longer, which may take 100 milliseconds to reach a stable state. In order to achieve synchronous acquisition, a 10 millisecond sampling window can be set for the infrared sensor to collect data every 100 milliseconds; and a 100 millisecond sampling window can be set for the vibration sensor to collect data every 100 milliseconds. In this way, the data acquisition time windows of the two can be aligned to ensure that the data acquired in the same time period has time consistency. In addition, for the sound sensor, its response time may be between the two, for example, 50 milliseconds, and it can be synchronized with the data acquisition of the infrared and vibration sensors by adjusting the sampling frequency and window size. By accurately controlling the sampling time window of each sensor, data misalignment or loss can be avoided, and the overall performance of the multi-sensor system can be improved.

[0012] Step S104, optimizing the working mode of the sensors according to the power consumption of each type of sensors, so as to reduce the overall power consumption while meeting the monitoring requirements.

[0013] According to the power consumption of various types of sensors, the working mode of the sensor can be optimized from multiple aspects. First, in view of the high power consumption characteristics of infrared sensors, the energy consumption can be reduced by adjusting the sampling frequency. For example, in a scenario where the monitored target moves slowly, the sampling frequency can be reduced from 10 times per second to 3 times per second, which can not only meet the monitoring needs but also reduce power consumption by 60%. At the same time, the working time of the infrared sensor can be dynamically adjusted according to the ambient light conditions, reducing the working time during the day when the light is sufficient, and increasing the sampling frequency at night or when the light is insufficient, so as to optimize the energy consumption without affecting the monitoring effect. Secondly, for vibration sensors, since their data collection requires a long stabilization time, the power consumption can be reduced by optimizing the trigger mechanism. For example, the trigger threshold of the vibration sensor is set, and data collection is started only when the vibration intensity exceeds a certain value, avoiding continuous operation when there is no vibration or the vibration is weak. In addition, the working time of the vibration sensor can be adjusted according to actual needs. For example, in a warehouse environment, the monitoring frequency can be reduced when there is less activity at night, and the sampling frequency can be increased when there is frequent activity during the day, so as to reduce power consumption while ensuring the monitoring effect. For ultrasonic sensors, due to their high power consumption and the need to work continuously, energy consumption can be optimized through intermittent working mode. For example, in the application of mouse repellers, ultrasonic sensors can work for 5 seconds every 30 seconds instead of working continuously. This intermittent working mode can reduce power consumption by more than 80% while still effectively repelling mice. In addition, the working frequency of ultrasonic sensors can be adjusted according to the laws of mouse activity, such as increasing the working frequency during the peak period of mouse activity and reducing the frequency during the trough period of activity, thereby further optimizing energy consumption. In addition, the overall power consumption can be reduced through the collaborative work between sensors. For example, infrared sensors and vibration sensors can cooperate with each other, and only when the infrared sensor detects the movement of the target will the vibration sensor be triggered to start data collection. This collaborative working mechanism can reduce the ineffective working time of the vibration sensor, thereby reducing the overall power consumption. At the same time, the ultrasonic sensor can adjust the working mode according to the monitoring results of the infrared sensor and the vibration sensor, such as entering a dormant state when no target is detected, and starting to work when a target is detected, thereby further optimizing energy consumption. Finally, the power supply method of the sensor can also be optimized. For example, in scenarios such as warehouses or farmland, sensors can be equipped with solar panels or energy harvesting devices to use light or mechanical energy in the environment to power the sensors, reducing reliance on batteries. In addition, the sensor's power management module can further reduce energy consumption by optimizing the voltage and current regulation methods, such as reducing the supply voltage when the sensor is in low-power mode, thereby reducing energy loss. Through the above optimization methods, the overall power consumption of the sensor can be significantly reduced while meeting monitoring needs, thereby extending the use time of the device, reducing the frequency of battery replacement or charging, and improving the continuity and reliability of the device.

[0014] Step S105, the obtaining of multiple types of sensors includes: obtaining an infrared sensor for detecting changes in thermal radiation of rodents; obtaining a vibration sensor for detecting physical vibrations of rodents; and obtaining a sound sensor for capturing specific frequency sound waves emitted by rodents.

[0015] Infrared sensors monitor rodents by detecting changes in thermal radiation emitted by their bodies. When rodents are active, their body temperature will differ from the ambient temperature, and this difference will be captured by infrared sensors. For example, when mice are active near equipment, their body temperature is usually around 37 degrees Celsius, while the ambient temperature may be 25 degrees Celsius. Infrared sensors can detect this 12-degree temperature difference and trigger a response. This technology relies on the infrared band of thermal radiation, usually in the wavelength range of 8 to 14 microns. The sensor determines whether the target exists by receiving the thermal radiation signal in this band. The advantage of infrared sensors is their non-contact detection capability, which can capture targets at a long distance and is suitable for scenes with high requirements for concealment. Vibration sensors monitor rodents by detecting physical vibrations generated when they are active. When mice walk or gnaw objects near equipment, they generate tiny vibration signals. Vibration sensors convert these mechanical vibrations into electrical signals through piezoelectric effect or capacitive effect. For example, the vibration frequency generated by mice walking is usually in the range of 10 to 100 Hz. Vibration sensors can identify signals in this specific frequency range through filtering and amplification technology. The sensitivity of the vibration sensor can be optimized by adjusting its internal circuit parameters, such as setting the detection threshold to 0.1 m / s² to ensure that weak vibration signals can be captured. This sensor is particularly suitable for monitoring the frequency of rodent activity in a specific area, such as the storage area of ​​goods in a warehouse. Sound sensors achieve monitoring by capturing specific frequency sound waves emitted by rodents. When mice are active, they make high-frequency calls or friction sounds when gnawing on objects. The frequency of these sounds is usually in the range of 20 kHz to 50 kHz. The sound sensor converts the sound wave signal into an electrical signal through a microphone element and analyzes the signal through digital signal processing technology. For example, the sensor can decompose the sound signal into different frequency components through fast Fourier transform and extract the characteristic frequency related to mouse activity. The advantage of the sound sensor is that it can capture the specific sounds emitted by rodents, such as the high-frequency friction sound emitted when mice gnaw on cables, so as to achieve accurate identification of the target behavior. This sensor is particularly suitable for scenarios where it is necessary to distinguish the activities of different types of rodents.

[0016] Step S106, determining whether there is signal interference between sensors of different types, includes: acquiring electromagnetic interference information and noise information in the environment; and determining whether signal collection of sensors of different types is interfered with based on the electromagnetic interference information and noise information.

[0017] When obtaining electromagnetic interference information and noise information in the environment, it is first necessary to identify the sources of electromagnetic interference and noise. Electromagnetic interference may come from surrounding electronic devices, wireless signals or other high-frequency signal sources, while noise may be caused by background sound, mechanical vibration or other non-target signals in the environment. For example, in an industrial environment, motors, transformers and wireless communication equipment may generate high-frequency electromagnetic interference, while the operation of mechanical equipment may also generate low-frequency vibration noise. In order to obtain this information, a spectrum analyzer can be used to scan the electromagnetic spectrum of the environment to determine the frequency range and intensity of the interference signal. At the same time, the noise signal in the environment is collected by a microphone or vibration sensor, and its spectrum characteristics are analyzed using signal processing technology to distinguish the target signal from the noise. In combination with the obtained electromagnetic interference information and noise information, it is necessary to analyze the overlap between these interference signals and the operating frequency of the sensor to determine whether the signal acquisition of different types of sensors is interfered. For example, infrared sensors usually work within a specific wavelength range. If there are electromagnetic signals close to the wavelength in the environment, the signal acquisition of the infrared sensor may be interfered. Similarly, if the operating frequency of the vibration sensor is close to the vibration frequency of the mechanical equipment, it may not be able to accurately distinguish the target vibration from the background noise. In order to judge the degree of interference, the spectrum of the interference signal can be compared with the operating frequency of the sensor to analyze whether there is overlap or harmonic relationship. For example, when it is found that there is an electromagnetic signal in the environment that is close to the operating frequency of the infrared sensor, it can be preliminarily judged that the infrared sensor may be interfered with, and the intensity and impact of the interference can be further verified by experiments. Through the above analysis, targeted measures can be taken to reduce interference. For example, for electromagnetic interference, the influence of interference signals can be avoided by shielding the sensor or adjusting the operating frequency of the sensor. For noise interference, the noise signal can be separated from the target signal by filtering technology. For example, a bandpass filter is used to filter out noise signals that are not related to the operating frequency of the vibration sensor, thereby improving the accuracy of the sensor. This step-by-step analysis and processing method can not only effectively identify the source of interference, but also provide an optimization basis for system design, thereby improving the comprehensive performance of the multi-sensor system.

[0018] Step S107, adjusting the arrangement of the sensors includes: changing the spatial layout of the sensors to increase the distance between sensors that are prone to signal interference; and blocking signal crosstalk between different sensors through isolation measures.

[0019] Changing the spatial layout of sensors can be achieved by rationally planning the installation locations of sensors. For example, in warehouse environment monitoring, infrared sensors and vibration sensors may work at the same time. If the distance between the two is too close, the thermal radiation signal of the infrared sensor may be interfered by the mechanical vibration of the vibration sensor, resulting in inaccurate data. To avoid this, the infrared sensor can be installed on the top of the warehouse to monitor temperature changes, while the vibration sensor can be installed at the bottom of the shelf to monitor the movement of goods. In this way, the working areas of the two are completely separated, avoiding signal interference. In addition, in farmland monitoring, sound sensors and infrared sensors may need to work at the same time. The sound sensor is used to capture the sound of insect activities, while the infrared sensor is used to monitor soil temperature. If the two are installed in the same position, the sound wave signal of the sound sensor may be interfered by the thermal radiation of the infrared sensor. Therefore, the sound sensor can be installed above the crop, and the infrared sensor can be buried in the soil, reducing the possibility of signal crosstalk through spatial separation. Physical isolation or signal filtering technology can be used to block signal crosstalk between different sensors through isolation measures. For example, in industrial equipment monitoring, vibration sensors and sound sensors may work at the same time. The vibration sensor is used to monitor the mechanical vibration of the equipment, while the sound sensor is used to capture the noise of equipment operation. If the distance between the two is close, the mechanical vibration signal of the vibration sensor may be transmitted through the air and mistakenly regarded as a noise signal by the sound sensor. To avoid this, sound insulation materials such as foam or rubber pads can be installed between the two to block the propagation path of the mechanical vibration signal. In addition, in a smart home environment, infrared sensors and sound sensors may work at the same time. Infrared sensors are used to monitor human activities, while sound sensors are used to capture voice commands. If the distance between the two is too close, the thermal radiation signal of the infrared sensor may be mistaken for noise by the sound sensor. To avoid this, a metal shielding plate can be installed between the two to block the propagation of thermal radiation signals. Through isolation measures, the signal crosstalk between different sensors can be effectively reduced and the accuracy of data collection can be improved.

[0020] Step S108, obtaining the response time of different types of sensors, includes: setting different data acquisition frequencies for each type of sensor; determining the sampling period of each type of sensor according to the data acquisition frequency; and using the sampling period of the sensor with the longest sampling period as the unified data acquisition time window of each sensor.

[0021] For the response time of different types of sensors, we first need to set different data collection frequencies according to their physical characteristics and application scenarios. For example, infrared sensors usually require a higher data collection frequency to ensure timely capture of temperature changes due to their high sensitivity to changes in thermal radiation, and may collect data dozens or even hundreds of times per second. Vibration sensors, on the other hand, may require a lower collection frequency, such as a few times per second, because they require a longer stabilization time to accurately detect physical vibrations. Sound sensors may require a moderate collection frequency, such as dozens of times per second, depending on the frequency range of sound waves they capture. Based on the data collection frequency, the sampling period of each type of sensor can be further determined. The sampling period refers to the time required for the sensor to complete a complete data collection. For infrared sensors that collect data at a high frequency, the sampling period may be extremely short, such as a few milliseconds. For vibration sensors that collect data at a low frequency, the sampling period may be longer, such as hundreds of milliseconds. The sampling period of sound sensors is between the two, such as tens of milliseconds. By setting the sampling period reasonably, we can ensure that each sensor can collect data in its best working state and avoid data distortion or omission caused by sampling too fast or too slow. The sampling period of the sensor with the longest sampling period is used as the unified data collection time window for each sensor in order to achieve synchronous data collection of multiple sensors. For example, in a system that includes infrared, vibration and sound sensors, if the sampling period of the vibration sensor is the longest, such as 500 milliseconds, then the system will use this as a unified time window to ensure that all sensors complete their respective data collection within these 500 milliseconds. The advantage of doing this is that it can simplify the data processing process, avoid data synchronization problems caused by inconsistent data collection times of different sensors, and effectively reduce the power consumption and computing burden of the system. Through this unified time window setting, the system can improve the overall operating efficiency while ensuring data accuracy.

[0022] Step S109, the optimization of the working mode of the sensor includes: obtaining power consumption parameters of each type of sensor; determining the optimal working mode of the sensor according to the power consumption parameters, including continuous operation, intermittent operation and event-triggered operation; and configuring the sleep duration and wake-up frequency of the sensor for different working modes.

[0023] Obtaining the power consumption parameters of each type of sensor is the first step to achieve optimization, which requires detailed power consumption testing of the sensor in the actual working environment. For example, the power consumption of an infrared sensor in continuous working mode may be 50 milliwatts, while it is only 1 milliwatt in sleep mode. The power consumption of a vibration sensor may be 30 milliwatts when collecting data, and it may drop to 0.5 milliwatts in standby mode. The power consumption of a sound sensor may be 40 milliwatts when capturing high-frequency sound waves, while the power consumption under low-frequency monitoring may be only 20 milliwatts. By accurately measuring these parameters, a reliable basis can be provided for the subsequent selection of working modes. Determining the best working mode of the sensor based on the power consumption parameters is the core of optimization. For infrared sensors, since they need to quickly capture changes in thermal radiation, an intermittent working mode can be adopted, such as waking up once every 100 milliseconds, and each sampling time is 10 milliseconds. This can not only meet the data collection requirements, but also reduce the power consumption to one-third of the continuous working mode. For vibration sensors, since their data collection requires a long stabilization time, an event-triggered working mode can be adopted, and sampling is started only when a vibration of a certain intensity is detected, thereby significantly reducing unnecessary power consumption. Sound sensors can dynamically adjust their working modes according to the ambient noise level, such as using intermittent working mode in quiet environments and switching to continuous working mode in noisy environments to capture more complete sound information. Configuring the sleep duration and wake-up frequency of sensors for different working modes is the key to further optimizing power consumption. For example, in intermittent working mode, the infrared sensor can set the sleep duration to 90 milliseconds and the wake-up frequency to 10 times per second, which can ensure the timeliness of data collection and keep power consumption at a low level. In event-triggered working mode, the vibration sensor can set the sleep duration to 1 second and the wake-up frequency to once per minute to maximize energy saving when there is no vibration event. Sound sensors can dynamically adjust their configuration according to the ambient noise level, such as extending the sleep duration to 5 seconds and reducing the wake-up frequency to 12 times per minute at night, and shortening the sleep duration to 1 second and increasing the wake-up frequency to 60 times per minute during the day. This flexible configuration method can significantly reduce overall energy consumption while ensuring that the sensor's functionality is not affected.

[0024] Step S1010, the method further includes: applying the sensor grouping design scheme to the electronic frequency-hopping ultrasonic mouse repeller; integrating multi-sensor information to achieve accurate identification and positioning of rodent activities, thereby improving the working efficiency of the mouse repeller and extending its service life.

[0025] Applying the sensor grouping design scheme to the electronic frequency-hopping ultrasonic rodent repeller can improve the comprehensiveness and accuracy of monitoring through the collaborative work of multiple sensors. For example, multiple ultrasonic sensors are arranged in the rodent repeller, and these sensors are distributed in different positions of the device in the form of groups, and each sensor is responsible for monitoring the activities of rodents in a specific area. Through this design, the sensor group can cover a wider range and avoid the missed detection of a single sensor due to the limitation of viewing angle or distance. At the same time, the grouping design can also reduce the false alarm rate through the information complementarity between sensors. For example, when a sensor detects suspected rodent activity, other sensors can simultaneously verify the authenticity of the signal to ensure the accuracy of the judgment. This design not only improves the reliability of monitoring, but also provides more complete data support for subsequent accurate identification and positioning. By integrating multi-sensor information to achieve accurate identification and positioning of rodent activities, the working efficiency of the rodent repeller can be significantly improved. For example, ultrasonic sensors, infrared sensors and vibration sensors are integrated in the rodent repeller, and these sensors capture the activity characteristics of rodents from different dimensions. Ultrasonic sensors can detect ultrasonic signals emitted by rodents, infrared sensors can sense changes in their body temperature, and vibration sensors can capture vibration signals when they move. By fusing and analyzing the data from these sensors, the system can more accurately determine the location and activity status of rodents. For example, when the ultrasonic sensor and the infrared sensor detect the target signal at the same time, the system can determine the specific location of the target and start the rodent repellent program. This multi-sensor information integration method not only improves the accuracy of recognition, but also reduces the misjudgment of a single sensor due to environmental interference. Improving the efficiency of the rodent repellent can be achieved by optimizing the data processing of the sensor and the rodent repellent strategy. For example, an intelligent algorithm is introduced into the rodent repellent to analyze the data collected by multiple sensors in real time and dynamically adjust the rodent repellent strategy according to the activity pattern of rodents. When the system detects an area where rodents frequently move, it can concentrate rodent repellent resources to drive the area with high-intensity ultrasonic repellent, while reducing the intensity of repellent in areas with less activity, thereby saving energy. In addition, the rodent repellent can also predict the activity trend of rodents based on the sensor data, take repellent measures in advance, and further improve the efficiency of repellent. This intelligent repellent strategy based on sensor data can not only improve the repellent effect, but also extend the service life of the equipment. The key to extending the service life lies in optimizing the energy consumption management of the sensor and the operation mode of the equipment. For example, low-power sensors are used in mouse repellers, and the sensors can be grouped to work in rotation. When a sensor detects rodent activity, other sensors can temporarily go into sleep mode, thereby reducing overall energy consumption. In addition, mouse repellers can automatically adjust the operating frequency and intensity according to environmental conditions. For example, when rodents are active at night, the mouse repeller can increase the operating frequency, and when rodents are less active during the day, the frequency can be reduced to reduce energy consumption.Through this intelligent energy management, the mouse repeller can maximize the service life of the equipment while ensuring the mouse repelling effect.

[0026] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A sensor grouping design method for an electronic frequency-hopping ultrasonic mouse repeller, characterized in that: include: Acquire multiple types of sensors, the multiple types of sensors including at least infrared sensors, vibration sensors and sound sensors; for each type of sensor, acquire its operating frequency and signal acquisition method; determine whether there is signal interference between different types of sensors based on the operating frequency and signal acquisition method; if there is signal interference, adjust the arrangement position of the sensor until there is no signal interference between different types of sensors; acquire the response time of different types of sensors, and determine the data acquisition time window of each type of sensor to achieve synchronous acquisition of sensor data; optimize the working mode of the sensor based on the power consumption of each type of sensor to reduce the overall power consumption while meeting the monitoring needs.

2. The sensor grouping design method according to claim 1, wherein: The method of obtaining multiple types of sensors includes: obtaining an infrared sensor for detecting changes in thermal radiation of rodents; obtaining a vibration sensor for detecting physical vibrations of rodents; and obtaining a sound sensor for capturing sound waves of specific frequencies emitted by rodents.

3. The sensor grouping design method according to claim 1, wherein: The determining whether there is signal interference between sensors of different types includes: acquiring electromagnetic interference information and noise information in the environment; and determining whether signal collection of sensors of different types is interfered with based on the electromagnetic interference information and noise information.

4. The sensor grouping design method according to claim 1, wherein: The adjusting of the arrangement position of the sensors includes: changing the spatial layout of the sensors to increase the distance between sensors that are prone to signal interference; and blocking signal crosstalk between different sensors through isolation measures.

5. The sensor grouping design method according to claim 1, wherein: The method of obtaining the response time of different types of sensors includes: setting different data acquisition frequencies for each type of sensor; determining the sampling period of each type of sensor according to the data acquisition frequency; and using the sampling period of the sensor with the longest sampling period as the unified data acquisition time window of each sensor.

6. The sensor grouping design method according to claim 1, wherein: The method of optimizing the working mode of the sensor includes: obtaining power consumption parameters of each type of sensor; determining the best working mode of the sensor according to the power consumption parameters, including continuous operation, intermittent operation and event-triggered operation; and configuring the sleep duration and wake-up frequency of the sensor for different working modes.

7. The sensor grouping design method according to any one of claims 1 to 6, characterized in that: The method also includes: applying the sensor grouping design scheme to the electronic frequency-hopping ultrasonic mouse repeller; realizing accurate identification and positioning of rodent activities by integrating multi-sensor information, thereby improving the working efficiency of the mouse repeller and extending its service life.

Images