Intelligent pet necklace and insect expelling method

Abstract

The invention discloses an intelligent pet necklace and a desinsectization method. The intelligent pet necklace comprises a necklace belt, a shell cover, a rope penetrating opening, a multi-sensor fusion module, an intelligent self-adaptive learning module, a multi-frequency dynamic control module, a wireless communication and cloud interaction module and an energy management module. Rope penetrating openings are formed in the two ends of the shell cover and used for fixing one ends of the two necklace belts, and the other ends of the two necklace belts are connected through a buckle; the multi-frequency dynamic control module, the intelligent adaptive learning module, the multi-sensor fusion module, the wireless communication and cloud interaction module and the energy management module are located in the shell cover. According to different parasite types, individual physiological states of pets and environmental changes, a real-time self-adaptive parasite expelling strategy can be realized, parasite expelling parameters can be continuously optimized, the parasite expelling efficiency is improved, the energy consumption is reduced, and the intelligent and personalized level is remarkably enhanced.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence technology, and in particular to a pet smart collar and an insect repellent method. Background Art

[0002] Many ultrasonic insect repellent collars on the current market generally use fixed frequencies or are limited to a few frequency switching methods. Such solutions are difficult to effectively respond to different parasite frequency sensitivities and changing environmental conditions, resulting in unstable insect repellent effects or even failure under certain conditions. In addition, traditional collars lack comprehensive perception of pets' physiological states (such as heart rate, blood oxygen changes) and environmental parameters (such as temperature, humidity, and light intensity), and cannot achieve personalized and dynamic strategy adjustments. Although some insect repellent collars provide Bluetooth or APP control, they are often limited to manual adjustment and preset modes, and lack the ability of deep learning and adaptive optimization.

[0003] The Chinese invention patent application with publication number CN113225718A and the name of the invention is a Bluetooth pet mosquito repellent and anti-lost wearable device system. It can only be controlled by Bluetooth, has less frequency switching, lacks deep learning, and has high energy consumption. The Chinese utility model patent with publication number CN214853596U and the name of the invention is a pet collar with break protection and insect repellent effect. It can only use fixed frequencies and cannot achieve dynamic modulation. It lacks intelligent control, sensor fusion and cloud analysis, and has a single function.

[0004] Therefore, there is an urgent need for an ultrasonic pet deworming collar that can achieve wideband dynamic control, intelligent adaptive learning, and multi-dimensional perception to cope with a variety of parasites, complex environments, and individual differences in pets. Summary of the invention

[0005] The purpose of the present invention is to provide a pet smart collar and deworming method, which can adaptively adjust the ultrasonic frequency, waveform and output intensity according to different parasite types, the pet's own physiological state and environmental conditions through broad-spectrum ultrasonic modulation technology, deep learning algorithm, cloud big data and multi-sensor fusion, so as to achieve a highly personalized and efficient deworming solution.

[0006] The present invention adopts the following technical solution: a pet smart collar, comprising:

[0007] Collar, outer shell, rope threading port, multi-sensor fusion module, intelligent adaptive learning module, multi-frequency dynamic control module, wireless communication and cloud interaction module and energy management module.

[0008] Rope threading openings are arranged at both ends of the outer shell cover for fixing one end of two collar straps, and the other ends of the two collar straps are connected by buckles.

[0009] The multi-frequency dynamic control module, the intelligent adaptive learning module, the multi-sensor fusion module, the wireless communication and cloud interaction module and the energy management module are located inside the outer shell.

[0010] The multi-frequency dynamic control module and the intelligent adaptive learning module transmit data wirelessly, the intelligent adaptive learning module and the multi-sensor fusion module transmit data wirelessly, and the wireless communication and cloud interaction module transmit data wirelessly with the multi-frequency dynamic control module, the intelligent adaptive learning module, and the multi-sensor fusion module respectively.

[0011] The energy management module is used to provide power and monitor the battery status in real time.

[0012] Furthermore, the multi-sensor fusion module includes physiological sensors, environmental sensors, motion and position sensors and edge computing units to integrate the sensors; the physiological sensors are used to obtain the pet's physiological state parameters in real time, the environmental sensors are used to collect the pet's environmental parameters, the motion and position sensors are used to monitor the pet's activity range, exercise intensity and position, and the edge computing unit is used to pre-process the data.

[0013] Furthermore, physiological sensors include heart rate sensors, blood oxygen sensors and body temperature sensors; environmental sensors include temperature sensors, humidity sensors, light sensors and air pressure sensors; motion and position sensors include GPS sensors and accelerometers.

[0014] Furthermore, the collar strap is made of a flexible waterproof and tear-resistant material; and the outer shell cover is made of an impact-resistant material.

[0015] Furthermore, the wireless method is Bluetooth, Wi-Fi or NB-IoT.

[0016] Furthermore, the present invention also proposes a method for repelling insects using a pet smart collar, comprising:

[0017] S1. Use a multi-sensor fusion module to monitor the physiological and behavioral parameters of the pet and the environmental parameters of the pet, and pre-process the parameters to obtain pre-processed parameters.

[0018] S2. Process the preprocessed parameters using an intelligent adaptive learning module to obtain an insect repellent strategy, which includes an insect repellent demand level and optimal insect repellent parameters, and send an insect repellent parameter adjustment instruction to the multi-frequency dynamic control module.

[0019] S3. Based on the deworming parameter adjustment instruction, the deworming parameters are adjusted according to the parasite types and environmental conditions within the frequency range of 10kHz to 120kHz using a multi-frequency dynamic control module to achieve deworming of pets.

[0020] The information obtained from S4, the multi-sensor fusion module, the intelligent adaptive learning module and the multi-frequency dynamic control module is transmitted to the wireless communication and cloud interaction module, and interacts with the cloud platform and mobile applications in real time through wireless means to realize data uploading, analysis and remote control, update the insect repellent strategy and transmit it to the intelligent adaptive learning module and the multi-frequency dynamic control module.

[0021] Furthermore, in step S1, preprocessing includes signal filtering, data cleaning, normalization and feature extraction.

[0022] Furthermore, in step S2, within a set time period, cloud big data is used to train the parameters of the deep learning model, which parameters include weights and hyperparameters, and the obtained insect repellent strategy is compared with the actual effect, the model parameters are optimized, and a trained deep learning model is obtained.

[0023] The preprocessed parameters are input into the trained deep learning model, and the parameters are transmitted to the hidden layer through the input layer to learn the relationship between the parameters. The activation function is used for nonlinear mapping, and the insect repellent strategy is obtained through the output layer.

[0024] Among them, the optimal insect repellent parameters include ultrasonic frequency, waveform and output intensity; the waveform includes continuous wave, pulse wave and mixed wave, and the output intensity includes 1 to 5 levels.

[0025] Furthermore, in step S3, in the multi-frequency dynamic control module, frequency sweeping, frequency hopping, pulse modulation and waveform superposition techniques are used to cover the parasite's sensitive spectrum.

[0026] When the sensitive frequency corresponding to the parasite is between 30kHz and 60kHz, frequency sweeping or frequency hopping is performed within this range; when the pet is in the grass, pulse modulation and waveform superposition technology are used.

[0027] Furthermore, in step S4, the deworming parameters adjusted in the multi-frequency dynamic control module and the pet's physiological parameters and the pet's environmental parameters obtained by the multi-sensor fusion module are compared and evaluated using the cloud function. When the pet's heart rate is >160 beats / minute or <50 beats / minute, it indicates that the heart rate is abnormal and the output intensity is adjusted; when the ambient temperature of the pet's environment is >30°C or <10°C and the ambient humidity is >60% or <30%, it indicates that the ambient temperature and humidity have changed significantly and the ultrasonic frequency is adjusted; and then the deworming strategy is updated.

[0028] Compared with the prior art, the present invention adopts the above technical solution and has the following technical effects:

[0029] 1. The present invention improves the anthelmintic efficiency of specific parasites by about 20%-30%.

[0030] 2. The present invention utilizes intelligent adaptive learning and low power consumption strategies to reduce energy consumption by about 10%-15% within the same working time and extend the battery life of the device.

[0031] 3. The multi-sensor fusion proposed by the present invention can stabilize the insect repellent effect under variable climate and environmental conditions without frequent user intervention. At the same time, the present invention combines the physiological condition and environmental parameters of pets with insect repellent for the first time, which not only ensures the best personalized effect of expelling various pests, but also ensures that the pet's physical health is in the best state. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 It is the overall structure diagram of the pet smart collar of the present invention.

[0033] Figure 2 1 is an internal module diagram of the pet smart collar of the present invention.

[0034] Figure 3 It is an overall implementation flow chart of the present invention. DETAILED DESCRIPTION

[0035] The present invention will be further described below in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and cannot be used to limit the protection scope of the present invention.

[0036] To achieve the above object, the present invention provides a pet smart collar, such as Figure 1 As shown, including:

[0037] Collar, outer shell, rope threading port, multi-sensor fusion module, intelligent adaptive learning module, multi-frequency dynamic control module, wireless communication and cloud interaction module and energy management module.

[0038] Rope threading openings are arranged at both ends of the outer shell cover for fixing one end of two collar straps, and the other ends of the two collar straps are connected by buckles.

[0039] The multi-frequency dynamic control module, the intelligent adaptive learning module, the multi-sensor fusion module, the wireless communication and cloud interaction module and the energy management module are located inside the outer shell.

[0040] like Figure 2 As shown, the multi-frequency dynamic control module and the intelligent adaptive learning module transmit data wirelessly, the intelligent adaptive learning module and the multi-sensor fusion module transmit data wirelessly, and the wireless communication and cloud interaction module transmit data wirelessly with the multi-frequency dynamic control module, the intelligent adaptive learning module, and the multi-sensor fusion module respectively.

[0041] The energy management module is used to provide power and monitor the battery status in real time.

[0042] The multi-sensor fusion module includes physiological sensors, environmental sensors, motion and position sensors, and edge computing units to integrate the sensors; physiological sensors are used to obtain the pet's physiological state parameters in real time, environmental sensors are used to collect the pet's environmental parameters, motion and position sensors are used to monitor the pet's activity range, exercise intensity and position, and the edge computing unit is used to pre-process the data.

[0043] Physiological sensors include heart rate sensors, blood oxygen sensors and body temperature sensors; environmental sensors include temperature sensors, humidity sensors, light sensors and air pressure sensors; motion and position sensors include GPS sensors and accelerometers.

[0044] The collar strap is made of flexible, waterproof, tear-resistant material; the outer shell cover is made of impact-resistant material.

[0045] The wireless method is Bluetooth, Wi-Fi or NB-IoT.

[0046] Method for achieving deworming using a pet smart collar Figure 3 As shown, including:

[0047] S1. Use a multi-sensor fusion module to comprehensively monitor the physiological and behavioral parameters of the pet and the environmental parameters of the pet, and pre-process the parameters to obtain pre-processed parameters.

[0048] Edge computing units are used to pre-process the parameters monitored by sensors to reduce data latency and improve decision response speed.

[0049] The preprocessing includes signal filtering, data cleaning, normalization and simple feature extraction.

[0050] Signal filtering is used to eliminate electromagnetic interference or invalid noise; data cleaning is used to eliminate obviously unreasonable or missing data; normalization is used to express data such as temperature and humidity in the same dimension or relative proportion to facilitate subsequent model calculations; simple feature extraction is used to extract corresponding features to determine the current state, such as extracting acceleration peaks or frequency components from motion sensor data to determine activity.

[0051] S2. Use the intelligent adaptive learning module to process the pre-processed parameters to obtain an insect repellent strategy, which includes the insect repellent demand level and the optimal insect repellent parameters, and send an insect repellent parameter adjustment instruction to the multi-frequency dynamic control module. The specific content is:

[0052] Within a set time period (such as every day or every week), the cloud big data is used to train the parameters of the deep learning model to make the deworming strategy more suitable for the current pet's habits and environment. The parameters include weights and hyperparameters. The obtained deworming strategy is compared with the actual effect, the model parameters are optimized, and a trained deep learning model is obtained.

[0053] The preprocessed parameters are input into the trained deep learning model, and the parameters are transmitted to the hidden layer through the input layer to learn the relationship between the parameters. The activation function is used for nonlinear mapping, and the insect repellent strategy is obtained through the output layer.

[0054] Among them, the best insect repellent parameters include ultrasonic frequency (such as 25kHz, 50kHz, 80kHz, etc.), waveform (continuous wave, pulse wave, mixed wave, etc.) and output intensity (such as 1 to 5 levels adjustable).

[0055] As usage time accumulates, the system can continuously update its own strategies, achieve progressive optimization from the initial plan to the personalized customized plan, and improve the accuracy and energy efficiency of insect repellent.

[0056] S3. Based on the deworming parameter adjustment instruction, the deworming parameters are adjusted according to the parasite types and environmental conditions within the frequency range of 10kHz to 120kHz using a multi-frequency dynamic control module to achieve deworming of pets.

[0057] Frequency sweeping, frequency hopping, pulse modulation and waveform superposition technologies are used to cover the sensitive spectrum of parasites to adapt to the sensitive frequency bands of various parasites and continuously optimize the deworming efficiency.

[0058] When the sensitive frequency corresponding to the parasite is between 30kHz and 60kHz, frequency sweep or frequency hopping is performed within this range; when the pet is in the grass or wet area, the output intensity is appropriately increased and pulse modulation and waveform superposition technology are used. The combination of different waveforms and frequency sweeping methods can achieve high-efficiency interference and expulsion of the parasite's auditory or sensory organs.

[0059] When the pet's stress response is large, the output intensity can be automatically reduced or the working cycle can be shortened to balance the pet's comfort and the deworming effect. Based on the periodic detection data, the frequency, waveform and intensity are continuously fine-tuned to ensure the best deworming effect in different time periods and different environments.

[0060] The information obtained from S4, the multi-sensor fusion module, the intelligent adaptive learning module and the multi-frequency dynamic control module is transmitted to the wireless communication and cloud interaction module, and interacts with the cloud platform and mobile applications in real time through wireless methods such as Bluetooth, Wi-Fi or NB-IoT to upload data. The cloud conducts a comprehensive analysis of the overall pet health, environmental risks, parasite outbreak trends, etc., and combines the biological characteristics of parasites and individual differences of pets to update the deworming strategy and transmit it to the intelligent adaptive learning module and the multi-frequency dynamic control module.

[0061] Among them, the insect repellent parameters adjusted in the multi-frequency dynamic control module and the pet's physiological parameters and pet's environmental parameters obtained by the multi-sensor fusion module are compared and evaluated in the cloud, and then the insect repellent strategy is updated. The frequency and power output can be flexibly changed in different environments and pet states, effectively improving the insect repellent effect.

[0062] Utilize the computing power of the cloud platform (such as big data analysis, model training and prediction, etc.) to conduct comprehensive analysis, the specific contents are as follows:

[0063] Behavioral pattern recognition: Identify the pet’s activity patterns through multi-dimensional data, such as whether it is still, moving, resting, etc.

[0064] Health status assessment: Based on physiological data (such as heart rate, blood oxygen level, etc.), determine whether the pet is in a state of health risk or whether there is stress caused by environmental changes.

[0065] Environmental adaptability analysis: Analyze whether the pet is currently in a suitable deworming environment through real-time environmental data such as temperature and humidity. The process of updating the deworming strategy includes:

[0066] Changes in the pet's physiological state: If the heart rate, blood oxygen and other data are abnormal (for example, heart rate >160 beats / minute or <50 beats / minute), the cloud will assess whether the pet has health problems (such as excessive stress, fever, etc.) and adjust the deworming strategy accordingly. For example, when the pet is anxious or stressed, the intensity of the ultrasound can be reduced to avoid overstimulation.

[0067] Environmental changes: If the ambient temperature and humidity change significantly (for example, the ambient temperature is >30℃ or <10℃, the ambient humidity is >60% or <30%), this may affect the activity and sensitivity of parasites. The cloud will determine the appropriate deworming frequency range in the current environment based on the environmental data and transmit the new frequency setting to the collar.

[0068] Insufficient deworming effect: If the feedback data received by the cloud platform indicates that the current frequency range cannot effectively expel the target parasites (for example, poor feedback on insect swarm detection or behavior patterns), the deworming parameters (such as frequency, pulse mode, etc.) will be optimized through a deep learning model to ensure optimal deworming effect.

[0069] External interference: If special circumstances occur in the environment or pets (such as high temperature, sudden intense exercise of the pet, etc.), the deworming effect may be affected. At this time, the cloud platform will dynamically adjust the deworming strategy to maintain the deworming effect.

[0070] Frequency adjustment: According to the analysis of the cloud platform, the frequency range of ultrasonic insect repellent is automatically adjusted, for example, the optimal frequency is selected between 80kHz and 120kHz.

[0071] Pulse mode adjustment: When it is found that the deworming effect is insufficient, the frequency switching may be increased, or the intensity of pulse modulation may be increased to enhance the interference effect on parasites.

[0072] Deworming intensity adjustment: Automatically adjust deworming intensity according to the pet's health status and environmental changes to avoid causing discomfort to the pet.

[0073] The pet collar continuously uploads real-time data to the cloud, which continuously adjusts its strategy based on this data. For example, if the cloud finds that the pet's health has changed, it may need to re-evaluate the deworming frequency and parameters and make adjustments in real time.

[0074] Through the mobile app, pet owners can manually control the collar, for example, adjust the frequency range, turn the insect repellent mode on or off, or view statistics on the insect repellent effect.

[0075] S5, the energy management module is used to supply power and monitor the battery status in real time. When the power is less than 20% or the voltage is less than 3.6V, the working cycle is automatically shortened.

[0076] Example:

[0077] When the temperature sensor and humidity sensor detect that the temperature is between 25 and 30 degrees Celsius and the humidity is between 60 and 80%, it is determined that the current environment is suitable for the breeding of fleas and ticks. The ultrasonic frequency is adjusted within the range of 30kHz to 50kHz, the output waveform adopts a pulse wave, and the output intensity is increased to level 3 to 4.

[0078] When the light sensor or GPS positioning determines that the pet is in an outdoor direct sunlight environment and the temperature is >35°C, the activity of some parasites may decrease. In this case, the output intensity will be appropriately reduced to level 1 to 2, or the frequency bandwidth will be lowered to reduce energy consumption.

[0079] If the deep learning analysis determines that the current multi-parameter comprehensive result has a high risk for a specific mite, the frequency sweep range will be concentrated to 80kHz to 100kHz, and the number of pulse modulation frequency switches will be increased to improve the interference effect on this type of parasite.

[0080] The specific contents with higher risks are:

[0081] A pet's heart rate that is persistently higher than normal (e.g., over 160 bpm) and accompanied by other health indicators (e.g., decreased blood oxygen concentration <90%) may indicate that the pet is in a state of health stress, at which time the risk of mites increases.

[0082] When blood oxygen levels are persistently below the normal range (e.g., below 90%) and the pet is inactive, this may indicate parasite infestation.

[0083] According to the living habits of parasites, mite parasites are usually more active in warm and humid environments. Therefore, when the temperature is close to above 25℃ and the humidity is above 60%, the risk of mites is higher.

[0084] Pets that spend a lot of time in grassy or wet areas and do not show significant changes in activity (low exercise) are at higher risk for mites.

[0085] If the risk is higher, add 2 pulse switches to ensure the frequency is effective against mites.

[0086] If the cloud analysis results indicate that the risk of mites is greater, 4 pulse frequency switches can be added to ensure that the frequency covers a wider range of mite frequencies.

[0087] Under extremely high risk of mites (such as the environment around the pet is suitable for the survival of mites and the pet is obviously unwell), the system can increase the frequency switching by 6 times to maximize the anti-insect effect.

[0088] The specific implementation of adjusting the number of pulses is automatically handled by the cloud platform based on real-time data feedback, ensuring effective interference with mites and reducing excessive stimulation to pets.

[0089] The pet smart collar of the present invention is fixed on the neck of the pet and is worn daily to prevent fleas, ticks, lice, demodex and other parasites.

[0090] When the pet is outdoors (such as in grasslands, woodlands, etc.) or comes into contact with other animals, the collar uses a multi-frequency dynamic control module to emit ultrasonic waves of different frequencies and waveforms to effectively interfere with and expel parasites.

[0091] At the same time, the multi-sensor fusion module inside the collar can monitor the pet's body temperature, heart rate, movement status, ambient temperature, humidity and other information in real time, and transmit the data to the intelligent adaptive learning module and the cloud for analysis, thereby dynamically and adaptively optimizing the deworming parameters.

[0092] The modular design of the present invention can reduce the equipment maintenance cost and provide a convenient way for the continuous iteration of future algorithms and hardware. The present invention adopts low-power chips, intelligent sleep mechanism and dynamic power allocation technology to maximize energy saving.

[0093] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A pet smart collar, characterized in that: include: Collar belt, outer shell, rope threading port, multi-sensor fusion module, intelligent adaptive learning module, multi-frequency dynamic control module, wireless communication and cloud interaction module and energy management module; Rope holes are arranged at both ends of the outer shell to fix one end of two collar straps, and the other ends of the two collar straps are connected by buckles; The multi-frequency dynamic control module, the intelligent adaptive learning module, the multi-sensor fusion module, the wireless communication and cloud interaction module and the energy management module are located inside the outer shell; The multi-frequency dynamic control module and the intelligent adaptive learning module transmit data wirelessly, the intelligent adaptive learning module and the multi-sensor fusion module transmit data wirelessly, and the wireless communication and cloud interaction module transmit data wirelessly with the multi-frequency dynamic control module, the intelligent adaptive learning module, and the multi-sensor fusion module respectively; The energy management module is used to provide power and monitor the battery status in real time.

2. The pet smart collar according to claim 1, characterized in that: The multi-sensor fusion module includes physiological sensors, environmental sensors, motion and position sensors, and edge computing units to integrate the sensors; physiological sensors are used to obtain the pet's physiological state parameters in real time, environmental sensors are used to collect the pet's environmental parameters, motion and position sensors are used to monitor the pet's activity range, exercise intensity and position, and the edge computing unit is used to pre-process the data.

3. The pet smart collar according to claim 2, characterized in that: Physiological sensors include heart rate sensors, blood oxygen sensors and body temperature sensors; environmental sensors include temperature sensors, humidity sensors, light sensors and air pressure sensors; motion and position sensors include GPS sensors and accelerometers.

4. The pet smart collar according to claim 1, characterized in that: The collar strap is made of flexible, waterproof, tear-resistant material; the outer shell cover is made of impact-resistant material.

5. The pet smart collar according to claim 1, characterized in that: The wireless method is Bluetooth, Wi-Fi or NB-IoT.

6. The insect repellent method applied to the pet smart collar according to claim 1, characterized in that: include: S1. Using a multi-sensor fusion module to monitor the physiological and behavioral parameters of the pet and the environmental parameters of the pet, and preprocessing the parameters to obtain preprocessed parameters; S2, using the intelligent adaptive learning module to process the pre-processed parameters to obtain an insect repellent strategy, which includes an insect repellent demand level and optimal insect repellent parameters, and sending an insect repellent parameter adjustment instruction to the multi-frequency dynamic control module; S3, based on the deworming parameter adjustment instruction, the deworming parameters are adjusted according to the parasite types and environmental conditions within the frequency range of 10kHz to 120kHz using a multi-frequency dynamic control module to achieve deworming of the pet; The information obtained from S4, the multi-sensor fusion module, the intelligent adaptive learning module and the multi-frequency dynamic control module is transmitted to the wireless communication and cloud interaction module, and interacts with the cloud platform and mobile applications in real time through wireless means to realize data uploading, analysis and remote control, update the insect repellent strategy and transmit it to the intelligent adaptive learning module and the multi-frequency dynamic control module.

7. The method for repelling insects using a pet smart collar according to claim 6, characterized in that: In step S1, preprocessing includes signal filtering, data cleaning, normalization and feature extraction.

8. The method for repelling insects using a pet smart collar according to claim 6, characterized in that: In step S2, within a set time period, the cloud big data is used to train the parameters of the deep learning model, the parameters including weights and hyperparameters, the obtained insect repellent strategy is compared with the actual effect, the model parameters are optimized, and a trained deep learning model is obtained; The preprocessed parameters are input into the trained deep learning model, and the parameters are transmitted to the hidden layer through the input layer to learn the relationship between the parameters, and the activation function is used for nonlinear mapping, and the insect repellent strategy is obtained through the output layer; Among them, the optimal insect repellent parameters include ultrasonic frequency, waveform and output intensity; the waveform includes continuous wave, pulse wave and mixed wave, and the output intensity includes 1 to 5 levels.

9. The method for repelling insects of a pet smart collar according to claim 8, characterized in that: In step S3, in the multi-frequency dynamic control module, frequency sweeping, frequency hopping, pulse modulation and waveform superposition techniques are used to cover the parasite's sensitive spectrum; When the sensitive frequency corresponding to the parasite is between 30kHz and 60kHz, frequency sweep or frequency hopping is performed within this range; When the pet is in the grass, pulse modulation and waveform superposition technology are used.

10. The method for repelling insects of a pet smart collar according to claim 8, characterized in that: In step S4, the deworming parameters adjusted in the multi-frequency dynamic control module and the pet's physiological parameters and the pet's environmental parameters obtained by the multi-sensor fusion module are compared and evaluated using the cloud function. When the pet's heart rate is >160 beats / minute or <50 beats / minute, it indicates that the heart rate is abnormal and the output intensity is adjusted; when the ambient temperature of the pet's environment is >30°C or <10°C and the ambient humidity is >60% or <30%, it indicates that the ambient temperature and humidity have changed significantly and the ultrasonic frequency is adjusted; and then the deworming strategy is updated.

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