Multi-mode intelligent control system of pet nursing bathing equipment

Abstract

The invention discloses a multi-mode intelligent control system for pet nursing and bathing equipment, and the system comprises a cleaning and nursing parameter setting module which is used for determining the cleaning and nursing parameters of each pet during bathing according to the basic information, hair features and skin states of the pets; the cleaning parameter adjusting module is used for identifying the emotional state of the pet and adjusting cleaning parameters in the bathing process; the pet activity level evaluation module is used for evaluating the activity level of the pet and adjusting the water flow mode; the hair drying parameter setting module is used for constructing a drying parameter setting model and determining a drying parameter combination of the hair drier; and the pet washing and care record and demand prediction module is used for generating a pet washing and care log and predicting the bath period and the care demand of the pet. According to the multi-mode intelligent control method, the defects of existing pet bathing equipment in the aspects of personalized nursing, emotion monitoring, activity adjustment and the like are overcome, and more efficient, safer and personalized pet bathing experience is provided.

Description

Technical Field

[0001] The present invention relates to the field of model operation and maintenance management, and particularly to a multi-mode intelligent control system for a pet care bathing device. Background Art

[0002] With the booming development of the pet industry, more and more pet owners have begun to pay attention to the daily care of their pets, especially the bathing process. Pet bathing is not only a basic need for cleanliness and hygiene, but also an important part of improving the health and well-being of pets. However, there are many problems with traditional pet bathing devices and care methods, which urgently need to be optimized and improved. First of all, the standardized operation of pet care devices is a major problem in the prior art. Most traditional pet bathing devices use a unified water flow rate, water temperature, and cleaning product ratio. This standardized method cannot meet the personalized needs of different pets. Each pet has different hair types, hair lengths, skin conditions, and body sizes, and the emotional state of the pet may also fluctuate during the bathing process. If the device cannot flexibly adjust relevant parameters according to the individual characteristics of the pet, the bathing experience of the pet will be limited, such as resulting in an unsatisfactory bathing effect and even possibly causing damage to the pet's skin and hair. Secondly, the monitoring of pet emotions and behaviors lacks real-time feedback. Existing pet bathing devices usually cannot analyze the emotional and behavioral states of pets during the bathing process in real time. The emotional changes of pets play a crucial role during the bathing process. For example, a pet may feel uneasy or anxious due to excessive water flow, too high water temperature, or other reasons. At this time, if the device cannot promptly sense the discomfort of the pet and adjust the bathing rhythm or water flow intensity, it may exacerbate the discomfort of the pet and even cause emotional fluctuations. In addition, the monitoring and adjustment of the pet's activity level are also another difficult point in the current technology. The activity level of the pet during the bathing process directly affects the efficiency and comfort of the bathing process. Some active pets may move frequently during the bathing process. At this time, it is necessary to appropriately adjust the water flow rate or water temperature to cope with the activity state of the pet, thereby protecting the health of the pet. However, existing devices usually cannot accurately monitor the activity level of the pet and dynamically adjust the bathing parameters according to the changes in the activity level. Therefore, there are many deficiencies in existing pet bathing devices, including the inability to individually adjust care parameters during the bathing process, the lack of real-time monitoring of pet emotions and behaviors, and the inability to dynamically adjust the washing and care plan when the activity level changes. The existence of the above problems results in the fact that the comfort, safety, and efficiency during the pet bathing process cannot be fully guaranteed, and technological innovation is urgently needed to meet the needs of pet owners for personalized, efficient, and safe washing and care. Summary of the Invention

[0003] In view of the problems existing in the above prior art, the present invention provides a multi-mode intelligent control system for a pet care bathing device, which mainly includes:

[0004] A washing and care parameter setting module, which is used to determine the washing and care parameters for each pet during bathing according to the pet's basic information, hair characteristics and skin condition;

[0005] A cleaning parameter adjustment module, which is used to obtain the pet behavior monitoring images during the pet bathing process, identify the pet's facial expressions, body posture changes and body movements, judge the pet's emotional state, and adjust the cleaning parameters during the bathing process based on the pet's emotional state;

[0006] A pet activity level assessment module, which is used to identify the action frequency and amplitude of each action of the pet during bathing according to the pet behavior monitoring images, evaluate the pet's activity level, and adjust the water flow pattern based on the pet's activity level during bathing;

[0007] A hair drying parameter setting module, which is used to construct a drying parameter setting model according to the humidity value of the pet's hair after bathing, combined with the pet's hair type and length, and determine the drying parameter combination of the hair dryer;

[0008] A pet washing and care record and demand prediction module, which is used to generate a pet washing and care log according to the washing and care parameters, drying parameters, special needs and emotional feedback in the pet washing and care process, and predict the pet's bathing cycle and care needs.

[0009] Furthermore, the washing and care parameter setting module is used to determine the washing and care parameters for each pet during bathing according to the pet's basic information, hair characteristics and skin condition, including:

[0010] Obtain the pet's basic information through the pet information registration form, including the pet's breed, age, weight and health status; obtain real-time pet pictures through a video sensor, use a convolutional neural network for model training, construct a pet hair characteristic and skin condition recognition model, and identify the pet's hair characteristics and skin condition, including hair length, hair type and the type of dirt on the hair, and the hair type includes but is not limited to short hair, long hair, curly hair; store the pet's breed, age, weight, hair characteristics, skin condition, and the washing and care parameters suitable for the pet in the pet washing and care database, and the washing and care parameters include water flow speed, water temperature, foam generation amount, cleaning product ratio, washing and care duration and hair care procedures, and the hair washing procedures include foam generation, cleaning rhythm and intensity; use a recurrent neural network for model training according to the pet's breed, age, weight, hair characteristics, skin condition, and the washing and care parameters suitable for the pet to determine the initial settings of the pet care bathing equipment for the water flow speed, cleaning product ratio and hair washing procedures for each pet during bathing.

[0011] Further, the cleaning parameter adjustment module is configured to obtain the pet behavior monitoring images during the pet bathing process, identify the facial expressions, body postures, and body movements of the pet, determine the emotional state of the pet, and adjust the cleaning parameters during the bathing process based on the emotional state of the pet, including:

[0012] Based on the initial settings of the pet care bathing device, use the pet care bathing device to wash and care for the pet, and obtain the pet behavior monitoring images during the pet bathing process in real time through the video sensor in the pet care bathing device; use the pet behavior monitoring images with labeled pet facial expressions and body postures to train the model using a convolutional neural network to construct a pet behavior recognition model to identify the facial expressions, body postures, and body movements of the pet, and the body movements include but are not limited to limb movements, head movements, and tail swings; according to the facial expressions, body postures, and body movements of the pet, use the decision tree algorithm to train the model to construct a pet emotion recognition model to identify the emotional state of the pet, and the emotional state includes but is not limited to anxiety, fear, and relaxation; according to the emotional state, adjust the cleaning parameters during the bathing process, including the cleaning rhythm, intensity, and water flow rate.

[0013] Further, the pet activity level assessment module is configured to identify the action frequency and the amplitude of each action of the pet during the bathing process according to the pet behavior monitoring images, evaluate the activity level of the pet, and adjust the water flow pattern based on the activity level of the pet during bathing, including:

[0014] Based on the pet behavior monitoring images obtained during multiple consecutive bathing processes, identify the action frequency and the amplitude of each action of the pet during the bathing process, and obtain the timestamp of each action; through a preset action threshold, divide the actions into activities of different intensities, and according to the intensity of each action, use the activity level assessment formula to evaluate the activity level A of the pet, where different intensities include low intensity, medium intensity, and high intensity, n is the number of actions detected during the bathing process, S i represents the intensity score of the i-th action, low intensity = 1, medium intensity = 2, high intensity = 3, T i represents the duration of the i-th action; through the pet activity level threshold adjustment unit, according to the basic information of the pet, construct a pet activity level threshold adjustment model to determine the first activity level threshold and the second activity level threshold for different pets; if the activity level of the pet is greater than the preset first activity level threshold, then adjust the water flow pattern according to the activity level of the pet during bathing, and the water flow pattern includes the water flow rate and the water temperature; if the overall activity level of the pet is greater than the preset second activity level threshold, then transfer the pet to the separated dry-wet area of the pet care bathing device and use the dry-wet alternating washing and care method to complete the washing and care of the pet.

[0015] It further includes a pet activity threshold adjustment unit, which is used to construct a pet activity threshold adjustment model according to the basic pet information, and determine the first activity threshold and the second activity threshold for different pets. Specifically, it includes:

[0016] Obtain the basic pet information through the historical grooming records in the pet grooming database, and mark the benchmark first activity threshold and the benchmark second activity threshold corresponding to the basic pet information. Use the random forest regression algorithm for model training to construct a pet activity threshold adjustment model; according to the real-time obtained basic pet information, use the activity threshold adjustment model to determine the first activity threshold and the second activity threshold for different pets.

[0017] Furthermore, the hair drying parameter setting module is used to construct a drying parameter setting model according to the humidity value of the pet's hair after bathing, combined with the hair type and length of the pet, and determine the drying parameter combination of the hair dryer, including:

[0018] Obtain the humidity value of the pet's hair after bathing through a humidity sensor, combined with the hair type and length of the pet, and obtain the corresponding drying parameters for each pet. Use a recurrent neural network for model training to construct a drying parameter setting model. The drying parameters include temperature, wind force, and blowing duration; according to the real-time obtained humidity value, hair type, and length of the pet's hair, use the drying parameter setting model to determine the drying parameter combination of the hair dryer; use the humidity sensor to monitor the drying process in real time, continuously feedback the humidity value of the pet's hair. If the humidity does not reach the expected value during the drying process, adjust the drying parameters in real time; if the pet's hair is not evenly dried, increase the wind force or temperature in the area where the humidity does not reach the expected value.

[0019] Furthermore, the pet grooming record and demand prediction module is used to generate a pet grooming log according to the grooming parameters, drying parameters, special needs, and emotional feedback in the pet grooming process, and predict the pet's bathing cycle and care needs, including:

[0020] Obtain the washing and drying parameters of the pet washing and care process through sensors, record the bathing preferences and special needs of each pet, and upload them to the pet washing and care database; obtain the basic information of the pet, changes in emotional state, action frequency during bathing, and the amplitude of each action. Based on the pet's emotional state, action frequency during bathing, and the amplitude of each action, obtain the pet's emotional feedback to different bathing rhythms and intensities during bathing. Combine the washing and care parameters, drying parameters, bathing preferences, and special needs, and save them as pet washing and care logs; obtain the pet washing and care frequency through historical pet washing and care records, combine the health status, emotional state, skin state, and hair characteristics, use a recurrent neural network for model training, construct a pet washing and care demand prediction model, predict the pet's bathing cycle and care needs, obtain the personalized care plan for each pet, generate the pet's future bathing plan and product requirements, and prompt the pet owner through SMS or email.

[0021] The technical solutions provided by the embodiments of the present invention may include the following beneficial effects:

[0022] The present invention provides a multi-mode intelligent control system for a pet care bathing device. By automatically determining the washing and care parameters according to the pet's basic information, hair characteristics, and skin state, the present invention realizes personalized settings during the pet bathing process and avoids the adaptation problems of standardized operations for different pets. The present invention combines real-time emotion monitoring, analyzes the pet's facial expressions, body postures, and behavioral actions, and dynamically adjusts the cleaning parameters during bathing, such as water flow speed and intensity, effectively reducing the discomfort of the pet and improving the comfort during bathing. In addition, the present invention also accurately monitors the pet's activity level, adjusts the water flow mode in real time according to the pet's activity state, optimizes the bathing efficiency and ensures comfort. During the drying process, through the humidity sensor and the drying parameter setting model, the uniformity and efficiency of hair drying are ensured. Finally, the present invention helps the owner scientifically plan the pet's bathing time and care needs through the washing and care log record and bathing cycle prediction, realizing comprehensive personalized management. Overall, the present invention solves the deficiencies of existing pet bathing devices in aspects such as personalized care, emotion monitoring, and activity level adjustment, improves the safety, comfort, and efficiency of pet bathing, meets the needs of pet owners for high-quality and personalized washing and care, and promotes the technological innovation and progress of the pet care industry. Brief Description of the Drawings

[0023] Figure 1 It is a flowchart of a multi-mode intelligent control system for a pet care bathing device of the present invention;

[0024] Figure 2 It is a schematic diagram of a multi-mode intelligent control system for a pet care bathing device of the present invention;

[0025] Figure 3Another schematic diagram of the multi-mode intelligent control system of a pet care bathing device according to the present invention. Detailed implementation manners

[0026] In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0027] As Figures 1-3 , a multi-mode intelligent control system of a pet care bathing device in this embodiment may specifically include:

[0028] Step S101, a washing and care parameter setting module, configured to determine the washing and care parameters for each pet during bathing according to the basic information, hair characteristics and skin conditions of the pet.

[0029] Obtain the basic information of the pet through the pet information registration form, including the breed, age, weight and health status of the pet; obtain real-time pet pictures through a video sensor, use a convolutional neural network for model training, construct a pet hair characteristic and skin condition recognition model, and identify the hair characteristics and skin conditions of the pet, including hair length, hair type and dirt type on the hair, and the hair type includes but is not limited to short hair, long hair, curly hair; store the breed, age, weight, hair characteristics, skin conditions of the pet, and the washing and care parameters suitable for the pet in the pet washing and care database, and the washing and care parameters include water flow rate, water temperature, foam generation amount, cleaning product ratio, washing and care duration and hair care procedure, and the hair washing procedure includes foam generation, cleaning rhythm and intensity; according to the breed, age, weight, hair characteristics, skin conditions of the pet, and the washing and care parameters suitable for the pet, use a recurrent neural network for model training to determine the initial settings of the pet care bathing device for the water flow rate, cleaning product ratio and hair washing procedure when each pet takes a bath.

[0030] Exemplarily, in a pet washing and grooming system, when setting bath parameters for a pet named AB, the basic information of AB was first obtained from the pet information registration form. It was found that AB is a 2-year-old golden retriever, weighing approximately 30 kilograms, in good health, and having no history of skin diseases or allergies. Based on AB's breed and body size, it can be inferred that its hair is medium-length, slightly wavy long hair. The real-time image of AB was obtained through a video sensor, and a convolutional neural network was used to analyze and obtain its hair characteristics and skin condition. According to the image recognition results, AB's hair was classified as long hair, and there was some dust and slight dirt on the hair, which means it requires a stronger cleaning effect than a normal bath process. Based on AB's basic information and the hair and skin conditions identified by the neural network, all its data was stored in the pet washing and grooming database. In the database, appropriate washing and grooming parameters were set for AB. The parameters include water flow rate, suitable water temperature, foam generation amount, ratio of cleaning products, washing and grooming duration, and hair care procedures. Specifically, the suitable water flow rate for AB was set to a medium speed because of its large size and long hair, and a moderate water flow rate can better clean the hair. The water temperature was set to 37°C, slightly higher than the human body temperature, to ensure AB's comfort during the bath. The foam generation amount was the standard amount, and the ratio of cleaning products was slightly increased according to AB's hair length and dirtiness to ensure thorough cleaning of its hair. In terms of the hair washing procedure, since AB is a long-haired dog, the cleaning rhythm and intensity during the bath need to be relatively gentle, but the intensity is sufficient to remove the dust and slight dirt on the hair. Specifically, the speed of foam generation at the beginning is slow, and the amount of foam is gradually increased as the washing progresses to ensure thorough cleaning of the hair. The washing intensity was set to medium to avoid over-stimulating the skin. A recurrent neural network was used to train these data. Based on AB's specific hair characteristics, skin condition, and health information, the specific settings of the water flow rate, ratio of cleaning products, and hair washing procedure during its bath were determined. These settings were stored in the system. When AB enters the washing and grooming equipment, the equipment will automatically perform initial settings according to these preset parameters.

[0031] Step S102, a cleaning parameter adjustment module, is used to obtain the pet behavior monitoring images during the pet bath, identify the facial expressions, body posture changes, and body movements of the pet, judge the emotional state of the pet, and adjust the cleaning parameters during the bath based on the emotional state of the pet.

[0032] Based on the initial settings of the pet care bathing device, use the pet care bathing device to wash and care for the pet, and obtain real-time pet behavior monitoring images during the pet bathing process through the video sensor in the pet care bathing device; use the pet behavior monitoring images with marked pet facial expressions and postures to train the model using a convolutional neural network, construct a pet behavior recognition model to recognize the pet's facial expressions, posture changes, and body movements, and the body movements include but are not limited to limb movements, head movements, and tail swings; according to the pet's facial expressions, posture changes, and body movements, use the decision tree algorithm to train the model, construct a pet emotion recognition model to recognize the pet's emotional state, and the emotional state includes but is not limited to anxiety, fear, and relaxation; according to the emotional state, adjust the cleaning parameters during the bathing process, including the cleaning rhythm, intensity, and water flow rate.

[0033] Exemplarily, when bathing a pet named BC, first start the washing and care process according to its initial settings in the pet care bathing device. Among them, BC is a 3-year-old Golden Retriever, weighing about 28 kilograms, with long and relatively soft hair. During the bathing process, the video sensor in the device captured BC's behavior monitoring images in real time. Through these images, BC's facial expressions and posture changes were marked, especially paying attention to the movements of its eyes, ears, and tail, and detailed markings were made in combination with the activities of its limbs, head movements, and tail swings. Based on these data, we used a convolutional neural network to train the model to accurately identify BC's behavior patterns during the bathing process. When training the model, BC's facial expressions were marked, and her different reactions during the bathing process were recorded. For example, when the water flow rate is too high or the water temperature is too high, BC's facial expressions usually show tension, eyes widened, ears pulled back, and the tail may also show a rigid state, indicating that BC is in an anxious or uneasy emotional state. When the water flow is gentle and the temperature is appropriate, BC's facial expressions become relaxed, eyes slightly closed, tail gently swinging, and the expression appears more comfortable and relaxed. Through these marked data, the convolutional neural network can learn the connection between these facial expressions and posture changes and the pet's emotions. Through this process, use the decision tree algorithm to train the model, construct a pet emotion recognition model, and classify the pet's emotions. When the emotion recognition model determines that BC shows anxiety, the cleaning rhythm, intensity, and water flow rate during the bathing process are adjusted according to this emotional state. For example, when BC feels anxious, the water flow rate will be automatically reduced, the water temperature will be adjusted to 38°C, slightly higher than the normal body temperature, and the cleaning intensity and rhythm will be reduced to provide a more gentle and comfortable bathing experience. When BC shows a relaxed emotion, the water flow rate will be moderately increased, and some cleaning intensity will be added to improve the bathing efficiency, while maintaining an appropriate water temperature and comfort.

[0034] Step S103: The pet activity assessment module is used to identify the action frequency and the amplitude of each action of the pet during the bathing process based on the pet behavior monitoring images, evaluate the activity of the pet, and adjust the water flow pattern based on the activity of the pet during bathing.

[0035] Based on the pet behavior monitoring images obtained during multiple consecutive bathing processes, identify the action frequency and the amplitude of each action of the pet during the bathing process, and obtain the timestamp of each action; divide the actions into activities of different intensities through a preset action threshold, and according to the intensity of each action, use the activity assessment formula to evaluate the activity A of the pet, where the different intensities include low intensity, medium intensity, and high intensity, n is the number of actions detected during the bathing process, and S i represents the intensity score of the i-th action, low intensity = 1, medium intensity = 2, high intensity = 3, and T i represents the duration of the i-th action; through the pet activity threshold adjustment unit, construct a pet activity threshold adjustment model based on the basic information of the pet to determine the first activity threshold and the second activity threshold for different pets; if the activity of the pet is greater than the preset first activity threshold, then adjust the water flow pattern according to the activity of the pet during bathing, and the water flow pattern includes the water flow speed and the water temperature; if the overall activity of the pet is greater than the preset second activity threshold, then transfer the pet to the separated dry-wet area of the pet care bathing device and use the dry-wet alternating washing and care method to complete the pet washing and care.

[0036] Exemplarily, if a golden retriever named CD is being bathed, obtain the behavior image of CD through a video sensor, and through action recognition technology, the action frequency and the amplitude of each action of CD during the bathing process can be determined. If 10 actions are recognized during this bathing process, among which 5 actions are determined to be of low intensity with a score of 1, 3 actions are determined to be of medium intensity with a score of 2, and the remaining 2 actions are determined to be of high intensity with a score of 3. Record the timestamp of each action and the duration of each action, and the duration of each action is the duration represented by the timestamp. If the average duration of low-intensity actions is 2 seconds, the average duration of medium-intensity actions is 4 seconds, and the average duration of high-intensity actions is 5 seconds. Based on these data, use the activity assessment formula Calculate the activity amount A of CD, and we get A = (5×1×2)+(3×2×4)+(2×3×5) = 64. Therefore, the activity amount A of CD is 64. According to the basic information of CD, adjust the bathing parameters according to the preset activity amount threshold. If the first activity amount threshold is 50 and the second activity amount threshold is 80. When the overall activity amount of CD is greater than 50, the water flow mode will be automatically adjusted to increase the water flow speed or water temperature to make it more comfortable. Since the overall activity amount of CD is 64, which has exceeded the first activity amount threshold, the water flow mode will be automatically adjusted, such as increasing the water flow speed and water temperature, to make the bathing process more efficient. If the activity amount of CD exceeds 80, it will be transferred to the separate dry-wet area of the pet care bathing equipment, and the dry-wet alternating washing and care method will be used to complete the hair care to further improve the comfort and effect.

[0037] Among them, the pet activity amount threshold adjustment unit is used to construct a pet activity amount threshold adjustment model according to the pet basic information and determine the first activity amount threshold and the second activity amount threshold of different pets.

[0038] Obtain the pet basic information through the historical washing and care records of the pet washing and care database, and mark the benchmark first activity amount threshold and the benchmark second activity amount threshold corresponding to the pet basic information. Use the random forest regression algorithm for model training to construct a pet activity amount threshold adjustment model; according to the pet basic information obtained in real time, use the activity amount threshold adjustment model to determine the first activity amount threshold and the second activity amount threshold of different pets.

[0039] Exemplarily, when bathing a pet named DE, the basic information of DE was obtained according to the historical bathing records in the pet bathing and grooming database. It was found that DE is a 5-year-old Labrador retriever, weighing approximately 32 kilograms, with good health and short hair. Based on the breed, age, weight, and health condition information of DE, the baseline first activity threshold and second activity threshold were marked for it. In the historical bathing records, it was found that for pets of similar body size and age, the first activity threshold was usually set at 60 and the second activity threshold was set at 100. These baseline thresholds were obtained based on the activity data of pets of the same body size and age and were used to set the initial activity adjustment standard for each pet. The random forest regression algorithm was used to train a model with these historical data to establish a pet activity threshold adjustment model. Through this model, the first and second activity thresholds of each pet were dynamically adjusted according to the basic information of the pet. For DE, based on its basic information, the first activity threshold of DE was calculated to be 65 and the second activity threshold was 105 through the activity threshold adjustment model. This means that when bathing DE, if its overall activity exceeds 65, the water flow pattern will start to be adjusted, and if it exceeds 105, more advanced bathing and grooming measures will be automatically triggered, such as moving to a separate dry-wet area for alternating dry and wet bathing and grooming. By using the activity threshold adjustment model, a personalized bathing and grooming experience was provided for DE, ensuring that appropriate parameter adjustments were made according to the actual activity of DE during the bathing process. For example, after DE's activity reached the threshold, the water flow speed or water temperature would be increased in real time to ensure that DE remained comfortable during the bathing process and the bathing efficiency was improved.

[0040] Step S104, the hair drying parameter setting module is used to construct a drying parameter setting model based on the humidity value of the pet's hair after bathing, combined with the hair type and length of the pet, to determine the drying parameter combination of the hair dryer.

[0041] The humidity value of the pet's hair after bathing is obtained through a humidity sensor, combined with the hair type and length of the pet, and the corresponding drying parameters for each pet are obtained. A recurrent neural network is used for model training to construct a drying parameter setting model. The drying parameters include temperature, wind force, and blowing duration. According to the humidity value, hair type, and length of the pet's hair obtained in real time, the drying parameter setting model is used to determine the drying parameter combination of the hair dryer. The humidity sensor is used to monitor the drying process in real time and continuously feedback the humidity value of the pet's hair. If the humidity does not reach the expected value during the drying process, the drying parameters are adjusted in real time. If the pet's hair is unevenly dried, the wind force or temperature in the area where the humidity does not reach the expected value is increased.

[0042] Exemplarily, when drying a pet named EF, the humidity value of EF's hair was first obtained through a humidity sensor. After the bath, the hair humidity of EF was 80%. Among them, EF is a golden retriever, 4 years old, weighing about 30 kilograms, and has long hair. According to the hair type and length of EF, the corresponding drying parameters were obtained from the database. The drying parameters include temperature, wind force, and blowing duration. Based on historical data and EF's hair characteristics, the initial drying parameters were set, including a temperature of 38°C, a medium wind force, and a blowing duration of 15 minutes. Combining the humidity value, hair type, and length of EF's hair, a drying parameter setting model was constructed using a recurrent neural network model to determine the combination of drying parameters for the hair dryer. In the initial drying stage, the hair dryer started working according to the predicted drying parameters, maintaining the temperature at 38°C, setting the wind force to medium, and the blowing duration to 15 minutes to ensure that EF's hair could be effectively dried. However, during the drying process, the humidity sensor continuously fed back the humidity value of EF's hair. After about 5 minutes, the humidity value reported by the humidity sensor was still as high as 60%, indicating that some areas of EF's hair did not achieve the expected drying effect. Through real-time monitoring of the data, it was found that the humidity of the back part of the hair was relatively high and not sufficiently dried. Therefore, the drying parameters were immediately adjusted, increasing the wind force of the hair dryer to high and slightly raising the temperature to 40°C to accelerate the drying process in this area. After this adjustment, the humidity sensor showed that the humidity of the back part dropped to the expected value of 50%. At this time, continuous adjustment was made according to the real-time feedback during the drying process to ensure that the entire hair area could reach the ideal drying state within an appropriate time. Finally, EF's hair was completely dried within 20 minutes, and the entire drying process maintained high efficiency and comfort, ensuring EF's washing and grooming experience.

[0043] Step S105, the pet washing and grooming record and demand prediction module is used to generate a pet washing and grooming log based on the washing and grooming parameters, drying parameters, special needs, and emotional feedback in the pet washing and grooming process, and predict the pet's bathing cycle and care needs.

[0044] Obtain the washing and drying parameters of the pet washing and grooming process through sensors, record the bathing preferences and special needs of each pet, and upload them to the pet washing and grooming database; obtain the basic information of the pet, changes in emotional state, action frequency during bathing, and amplitude of each action. Based on the pet's emotional state, action frequency during bathing, and amplitude of each action, obtain the emotional feedback of the pet to different bathing rhythms and intensities during bathing. Combine the washing and drying parameters, bathing preferences, and special needs, and save them as pet washing and grooming logs; obtain the pet washing and grooming frequency through historical pet washing and grooming records, combine the health status, emotional state, skin state, and hair characteristics, use a recurrent neural network for model training, construct a pet washing and grooming demand prediction model, predict the bathing cycle and care needs of the pet, obtain the personalized care plan for each pet, generate the pet's future bathing plan and product requirements, and prompt the pet owner through SMS or email.

[0045] Exemplarily, when washing and grooming a pet named FG, the washing and grooming parameters and drying parameters of FG during the bath were obtained through sensors. Here, FG is a 3-year-old German Shepherd with a body weight of approximately 35 kg, medium-length and relatively thick hair. The sensors recorded that the water flow rate during FG's bath was medium, the water temperature was 37°C, the foam generation amount was the standard amount, the cleaning product ratio was 1:1, the washing and grooming duration was 20 minutes, and the drying parameters were medium wind force, temperature 38°C, and hair drying duration 15 minutes. In addition, FG's special needs included maintaining a gentle water flow rate and a lower water temperature during the bath because it felt uncomfortable with the impact of the water flow. Through the video sensor and the behavior monitoring system, the changes in FG's emotional state, action frequency, and amplitude of each action were obtained. For example, during the bath, FG's action frequency was low, mainly with slight swings of the head and tail, indicating that it showed a certain degree of nervousness throughout the process. This emotional feedback suggested that the intensity of the water flow and foam might be too strong, so the bath rhythm was adjusted, the speed of foam generation was reduced, and the intensity of the water flow was gradually adjusted during the subsequent bath to reduce FG's discomfort. Based on these emotional feedbacks and action data, the frequency and amplitude of each action, emotional feedback, and FG's bath preferences during the bath were saved as pet washing and grooming logs. These logs recorded FG's preferences, such as the appropriate water flow rate and water temperature, as well as its reactions to different bath rhythms and intensities. After each bath, this information was uploaded to the pet washing and grooming database for comparison and analysis of historical records. Combining FG's health status, emotional state, skin condition, and hair characteristics, by analyzing historical washing and grooming records and washing and grooming frequencies, a recurrent neural network was used for model training to construct a pet washing and grooming demand prediction model. Through these data, the pet washing and grooming demand prediction model predicted FG's bath cycle and care needs. For example, it was predicted that FG's bath cycle was once every three weeks, and an additional skin care was required during the next bath to address the dryness of its hair. Finally, FG's future bath plan and product requirements were generated, including the specific cleaning products to be used, washing and grooming frequencies, and possible additional care products within the next three weeks. These plans and requirements would be notified to FG's owner via SMS or email to ensure that it could be prepared in time and provide the most appropriate care.

[0046] The above description is only a preferred embodiment of the present application and an explanation of the applied technical principles. Those skilled in the art should understand that the scope of the invention involved in the present application is not limited to the technical solutions formed by the specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the concept of the present application. For example, the technical solutions formed by mutually replacing the above features with the (but not limited to) technical features with similar functions disclosed in the present application.

Claims

1. A multi-mode intelligent control system for pet care and bathing equipment, characterized in that: The system comprises: The washing and care parameter setting module is used to determine the washing and care parameters for each pet when bathing according to the pet's basic information, hair characteristics and skin condition; A cleaning parameter adjustment module is used to obtain pet behavior monitoring images during the pet bathing process, identify the pet's facial expressions, posture changes and body movements, determine the pet's emotional state, and adjust the cleaning parameters during the bathing process based on the pet's emotional state; A pet activity assessment module is used to identify the pet's movement frequency and the amplitude of each movement during the bathing process based on the pet's behavior monitoring images, assess the pet's activity level, and adjust the water flow pattern based on the pet's activity level during the bathing process; The hair drying parameter setting module is used to construct a drying parameter setting model based on the humidity value of the pet's hair after bathing, combined with the pet's hair type and length, and determine the drying parameter combination of the hair dryer; The pet washing and care record and demand prediction module is used to generate a pet washing and care log based on the washing and care parameters, drying parameters, special needs and emotional feedback of the pet washing and care process, and predict the pet's bathing cycle and care needs.

2. The system according to claim 1, wherein: The washing and care parameter setting module is used to determine the washing and care parameters for each pet when bathing according to the pet's basic information, hair characteristics and skin condition, including: Obtain basic pet information through the pet information registration form, including the pet's breed, age, weight and health status; obtain real-time pet pictures through video sensors, use convolutional neural networks for model training, build a pet hair feature and skin condition recognition model, and identify the pet's hair features and skin conditions, including hair length, hair type and the type of dirt on the hair. Hair types include but are not limited to short hair, long hair, and curly hair; store the pet's breed, age, weight, hair features, skin condition, and pet-adapted washing and care parameters in the pet washing and care database. The washing and care parameters include water flow rate, water temperature, foam generation, cleaning product ratio, washing and care time and hair care procedures. The hair washing procedures include foam generation, cleaning rhythm and intensity; according to the pet's breed, age, weight, hair features, skin condition, and pet-adapted washing and care parameters, use recurrent neural networks for model training to determine the initial settings of the pet care bathing equipment for water flow rate, cleaning product ratio and hair washing procedures when bathing each pet.

3. The system according to claim 1, wherein: The cleaning parameter adjustment module is used to obtain pet behavior monitoring images during the pet bathing process, identify the pet's facial expressions, body changes and body movements, judge the pet's emotional state, and adjust the cleaning parameters during the bathing process based on the pet's emotional state, including: Based on the initial settings of the pet care and bathing equipment, the pet care and bathing equipment is used to wash and care for the pet, and the pet behavior monitoring images during the pet bathing process are obtained in real time through the video sensor in the pet care and bathing equipment; the pet behavior monitoring images with the pet's facial expressions and postures labeled are used, and a convolutional neural network is used for model training to build a pet behavior recognition model to recognize the pet's facial expressions, posture changes and body movements, which include but are not limited to limb movements, head movements, and tail swings; according to the pet's facial expressions, posture changes and body movements, a decision tree algorithm is used for model training to build a pet emotion recognition model to recognize the pet's emotional state, which includes but is not limited to anxiety, fear and relaxation; according to the emotional state, the cleaning parameters during the bathing process are adjusted, including the cleaning rhythm, strength and water flow speed.

4. The system according to claim 1, wherein: The pet activity assessment module is used to identify the pet's movement frequency and the amplitude of each movement during the bathing process according to the pet behavior monitoring image, assess the pet's activity, and adjust the water flow mode based on the pet's activity during the bathing process, including: Based on the pet behavior monitoring images obtained during multiple consecutive bathing processes, the pet's movement frequency and the amplitude of each movement during the bathing process are identified, and the timestamp of each movement is obtained; the movements are divided into activities of different intensities by presetting the movement threshold, and the activity evaluation formula is used according to the intensity of each movement Evaluate the pet's activity level A, where different intensities include low intensity, medium intensity, and high intensity, n is the number of movements detected during bathing, and S i represents the intensity score of the ith action, low intensity = 1, medium intensity = 2, high intensity = 3, T i Represents the duration of the ith action; through the pet activity threshold adjustment unit, according to the basic information of the pet, a pet activity threshold adjustment model is constructed to determine the first activity threshold and the second activity threshold of different pets; if the pet's activity is greater than the preset first activity threshold, the water flow pattern is adjusted according to the pet's activity during bathing, and the water flow pattern includes the water flow speed and the water temperature; if the pet's overall activity is greater than the preset second activity threshold, the pet is moved to the separated dry and wet areas of the pet care and bathing equipment, and the pet washing and care is completed using a dry and wet alternating washing and care method.

5. The system according to claim 4, wherein: The pet activity threshold adjustment unit is used to construct a pet activity threshold adjustment model according to the basic information of the pet, and determine the first activity threshold and the second activity threshold of different pets, including: The basic information of the pet is obtained through the historical care records of the pet care database, and the benchmark first activity threshold and the benchmark second activity threshold corresponding to the basic information of the pet are marked. The random forest regression algorithm is used for model training to build a pet activity threshold adjustment model; according to the basic information of the pet obtained in real time, the activity threshold adjustment model is used to determine the first activity threshold and the second activity threshold for different pets.

6. The system according to claim 1, wherein: The hair drying parameter setting module is used to construct a drying parameter setting model according to the humidity value of the pet's hair after bathing, combined with the pet's hair type and length, and determine the drying parameter combination of the hair dryer, including: The humidity value of the pet's hair after bathing is obtained through a humidity sensor, combined with the pet's hair type and length, and the corresponding drying parameters of each pet are obtained. A recurrent neural network is used for model training to build a drying parameter setting model. The drying parameters include temperature, wind speed, and blowing time. According to the real-time obtained humidity value, hair type, and length of the pet's hair, the drying parameter setting model is used to determine the drying parameter combination of the hair dryer. The humidity sensor is used to monitor the drying process in real time, and the humidity value of the pet's hair is continuously fed back. If the humidity does not meet expectations during the drying process, the drying parameters are adjusted in real time. If your pet's coat is drying unevenly, increase wind speed or temperature in areas where humidity is not reaching the desired level.

7. The system according to claim 1, wherein: The pet washing and care record and demand prediction module is used to generate a pet washing and care log according to the washing and care parameters, drying parameters, special needs and emotional feedback of the pet washing and care process, and predict the pet's bathing cycle and care needs, including: The washing and drying parameters of the pet washing and care process are obtained through sensors, and the bathing preferences and special needs of each pet are recorded and uploaded to the pet washing and care database; the pet's basic information, changes in emotional state, frequency of movements during bathing and the amplitude of each movement are obtained, and based on the pet's emotional state and the frequency of movements during bathing and the amplitude of each movement, the pet's emotional feedback on different bathing rhythms and intensities during bathing is obtained, and combined with the washing and care parameters, drying parameters, bathing preferences and special needs, it is saved as a pet washing and care log; the pet washing and care frequency is obtained through historical pet washing and care records, and combined with health status, emotional state, skin condition and hair characteristics, a recurrent neural network is used for model training to build a pet washing and care demand prediction model, predict the pet's bathing cycle and care needs, obtain a personalized care plan for each pet, generate the pet's future bathing plan and product needs, and remind the pet owner via mobile phone text message or email.

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