A home remote monitoring and early warning system and method based on personalized threshold

Abstract

This invention relates to the field of home remote monitoring and control technology, specifically a home remote monitoring and early warning system and method based on personalized thresholds. The method involves: acquiring first and second home data of a target; dividing the second home data into third home data based on the target's behavior; acquiring the surface material of a region based on the first home data; obtaining a home sub-region based on the surface material, the first home data, and the third home data; obtaining a first personalized threshold for the home sub-region and combining it with the third home data to obtain first home early warning data; determining an early warning sub-region based on the first home early warning data; acquiring sub-region data of cleaning the early warning sub-region by a second smart device; obtaining second home early warning data based on the third home data and the sub-region data; and adjusting and controlling the second smart device based on the second home early warning data. This invention accurately achieves early warning and cleaning of pet hair through the first and second home early warning data.

Description

Technical Field

[0001] This invention relates to the field of home remote monitoring and control technology, specifically to a home remote monitoring and early warning system and method based on personalized thresholds. Background Technology

[0002] While pets bring joy and companionship to families, they also bring some unavoidable troubles. For example, pets often shed during their daily activities, especially during shedding season or after strenuous exercise. This hair, adhering to furniture, floors, bedding, and other items, not only increases the difficulty of cleaning but can also affect air quality, leading to an increase in allergens in the home and posing a potential threat to the health of both family members and pets.

[0003] While cleaning tools such as vacuum cleaners and roller brushes are available to help with pet hair removal, these traditional methods are often ineffective when dealing with pet hair. On one hand, they require a significant amount of time and effort, making it difficult to achieve comprehensive coverage; on the other hand, existing cleaning tools typically cannot accurately identify the specific locations of hair loss, often relying on the user's subjective judgment for cleaning, resulting in poor cleaning outcomes and potentially overlooking important areas.

[0004] With technological advancements and the rapid development of artificial intelligence and the Internet of Things (IoT), smart home solutions are entering the market. By utilizing advanced sensing technologies, machine learning algorithms, and image recognition, real-time monitoring of pet activity and precise identification of areas where pets shed hair can be achieved. However, most existing solutions focus on automated cleaning or static environmental monitoring, lacking a deep understanding and real-time feedback on dynamic pet activity and the shedding process. How to achieve intelligent monitoring, accurate early warning, and efficient cleaning based on pet behavior in the home environment has become a pressing challenge for the industry.

[0005] To address this, a home remote monitoring and early warning system and method based on personalized thresholds are proposed. Summary of the Invention

[0006] The purpose of this invention is to provide a home remote monitoring and early warning system and method based on personalized thresholds. This involves acquiring first and second home data of a target; dividing the second home data into third home data based on the target's behavior; obtaining the surface material of a region based on the first home data; obtaining a home sub-region based on the surface material, the first home data, and the third home data; obtaining a first personalized threshold for the home sub-region and combining it with the third home data to obtain first home early warning data; determining an early warning sub-region based on the first home early warning data; acquiring sub-region data of cleaning performed by a second smart device on the early warning sub-region; obtaining second home early warning data based on the third home data and the sub-region data; and adjusting and controlling the second smart device based on the second home early warning data. This invention accurately achieves early warning and cleaning of pet hair through the first and second home early warning data.

[0007] To achieve the above objectives, the present invention provides the following technical solution:

[0008] A home remote monitoring and early warning system based on personalized thresholds includes:

[0009] The first intelligent device control module controls the first intelligent device to monitor the target and acquire the target's first home data and second home data; the first home data is pet trajectory data, and the second home data is pet behavior data; the second home data is divided and identified according to the target's behavior to obtain third home data, which includes the target's pet hair shedding data and the target's area of ​​influence data;

[0010] The first intelligent device early warning module obtains the surface material of the area based on the first home data; it performs clustering based on the surface material of the area, the first home data, and the third home data to obtain home sub-areas; it obtains the first personalized threshold based on the surface material of the home sub-areas; and it obtains the first home warning data based on the first personalized threshold and the third home data.

[0011] The second intelligent device control module determines the warning sub-area based on the first home warning data of the home sub-area, controls the second intelligent device to clean the warning sub-area, and adjusts the parameters of the second intelligent device based on the first home warning data;

[0012] The second smart device early warning module acquires sub-area data of the second smart device cleaning the early warning sub-area, obtains second home early warning data based on the third home data and sub-area data, and adjusts and controls the second smart device based on the second home early warning data.

[0013] The process of acquiring third-party home data in the first smart device control module is as follows:

[0014] The system acquires target behavior and target parameters, including target type, target age, target gender, and target health status. The target health status includes target physiological indicators, hair health data, and skin health data. The physiological indicators include heart rate data, body temperature data, and blood parameters. The hair health data includes data on pet body hair and pet shed hair. The skin health data includes abnormal skin areas and a skin severity coefficient, determined based on the impact of abnormal skin areas on hair loss.

[0015] The second home furnishing data is split based on the target behavior to obtain the second home furnishing split data;

[0016] A target behavior recognition model is constructed to identify the second home furnishing breakdown data and target parameters, and to obtain target hair loss data and target influence range data.

[0017] The target behavior recognition model includes a hair loss data recognition layer and an impact range recognition layer, and its construction process is as follows:

[0018] Identify the target's historical behavior data to obtain the target's historical parameters, historical behavior, historical hair loss data, and historical influence range.

[0019] The target's historical parameters, historical behavior, historical hair loss data, and historical influence range are combined to obtain first historical training data and second historical training data; the first historical training data includes target historical parameters, target historical behavior, and target historical hair loss data; the second historical training data includes target historical parameters, target historical behavior, and target historical influence range.

[0020] A hair loss data recognition layer is trained based on the first historical training data; an influence range recognition layer is trained based on the second historical training data.

[0021] The process of dividing the home sub-regions is as follows:

[0022] Based on the first home furnishing data and the third home furnishing data, integrated home furnishing data is obtained; the integrated home furnishing data includes pet trajectory data, as well as the target shedding data and target influence range corresponding to the pet trajectory data;

[0023] The integrated home furnishing data is divided according to the surface material of the area to obtain single-material home furnishing data; the single-material home furnishing data includes integrated home furnishing data under the same surface material of the area;

[0024] The data of furniture made of a single material is clustered and divided according to the clustering algorithm to obtain furniture sub-regions.

[0025] The first personalized threshold reflects the difficulty of hair removal from the surface material of the area; the setting process is as follows: an experimental material sample is established based on the surface material of the area, pet hair is evenly placed on the experimental material sample, and a second smart device is selected to clean the experimental material sample. The cleaning process is the same.

[0026] A first personalized threshold is obtained based on the hair removal efficiency of the experimental material sample and the damage coefficient of the experimental material; the hair removal efficiency is determined based on the weight of pet hair removed from the placed sample, and the damage coefficient of the experimental material is obtained based on the difference between the cleaned area image and the standard image.

[0027] Obtain the initial weight of the warning sub-area before the second intelligent device cleans it, and the final weight of the warning sub-area after the second intelligent device cleans it.

[0028] The sub-region cleanup weight is obtained based on the initial weight and the final weight.

[0029] The system acquires an image of impurities obtained from cleaning the warning sub-region using a second intelligent device, identifies the impurity image, and obtains the distribution ratio of pet hair; it then obtains hair cleaning data and non-hair cleaning data based on the sub-region cleaning weight and the pet hair distribution ratio; and finally obtains sub-region data based on the hair cleaning data and non-hair cleaning data.

[0030] Secondary home warning data is obtained based on hair removal data, non-hair removal data, and target hair loss data from the third home data; the operating parameters of the secondary smart device are adjusted based on the secondary home warning data.

[0031] A method for remote home monitoring and early warning based on personalized thresholds, comprising:

[0032] The system controls a first intelligent device to monitor a target and acquires first and second home data of the target. The first home data is pet trajectory data, and the second home data is pet behavior data. The second home data is divided and identified based on the target's behavior to obtain third home data, which includes target shedding data and target influence range data.

[0033] The surface material of the area is obtained based on the first home furnishing data; the area surface material, the first home furnishing data, and the third home furnishing data are clustered to obtain home furnishing sub-regions; a first personalized threshold is set based on the surface material of the home furnishing sub-regions; and the first home furnishing warning data is obtained based on the first personalized threshold and the third home furnishing data.

[0034] Based on the first home warning data, a warning sub-area is determined, and the second smart device is controlled to clean the warning sub-area. The parameters of the second smart device are adjusted according to the first home warning data.

[0035] The system acquires sub-area data from the second smart device's cleanup of the warning sub-area, obtains second home warning data based on the third home data and the sub-area data, and adjusts and controls the second smart device based on the second home warning data.

[0036] The process of acquiring the third-party home data is as follows:

[0037] The target behavior and target parameters are obtained, including target type, target age, target gender, and target health status; the target health status includes target physiological indicators, hair health data, and skin health data.

[0038] The second home furnishing data is split based on the target behavior to obtain the second home furnishing split data;

[0039] A target behavior recognition model is constructed to identify the second home furnishing breakdown data and target parameters, and to obtain target hair loss data and target influence range data.

[0040] The target behavior recognition model includes a hair loss data recognition layer and an impact range recognition layer, and its construction process is as follows:

[0041] Identify the target's historical behavior data to obtain the target's historical parameters, historical behavior, historical hair loss data, and historical influence range.

[0042] The target's historical parameters, historical behavior, historical hair loss data, and historical influence range are combined to obtain first historical training data and second historical training data; the first historical training data includes target historical parameters, target historical behavior, and target historical hair loss data; the second historical training data includes target historical parameters, target historical behavior, and target historical influence range.

[0043] A hair loss data recognition layer is trained based on the first historical training data; an influence range recognition layer is trained based on the second historical training data.

[0044] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0045] 1. This invention splits second home data based on target behavior to obtain second home split data; constructs a target behavior recognition model to identify the second home split data and target parameters; identifies the second home split data and target parameters based on a pet shedding data recognition layer to obtain pet shedding data; identifies the second home split data and target parameters based on an influence range recognition layer to obtain pet influence range data; and accurately identifies the shedding situation based on the pet's behavior data.

[0046] 2. This invention obtains integrated home furnishing data based on first and third home furnishing data; the integrated home furnishing data includes pet trajectory data, as well as target shedding data and target influence range corresponding to the pet trajectory data; the integrated home furnishing data is divided according to the surface material of the area to obtain single-material home furnishing data; the integrated home furnishing data under the same surface material of the area is accurately obtained; and then the single-material home furnishing data is clustered and divided by a clustering algorithm to accurately divide the areas affected by pet hair.

[0047] 3. This invention establishes experimental material samples based on the surface material of the area, evenly places pet hair on the experimental material samples, selects a second intelligent device to clean the experimental material samples, and obtains a first personalized threshold based on the hair cleaning efficiency and the damage coefficient of the experimental material samples; based on the first personalized threshold and third home data, the urgency of cleaning the surface material of the area is accurately measured. Attached Figure Description

[0048] Figure 1 This is a schematic diagram of the structure of a home remote monitoring and early warning system based on personalized thresholds according to the present invention;

[0049] Figure 2 This is a flowchart illustrating a home remote monitoring and early warning method based on personalized thresholds according to the present invention.

[0050] Figure 3 This is a schematic diagram of the carpet according to the present invention. Detailed Implementation

[0051] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0052] Example 1

[0053] This invention proposes a home remote monitoring and early warning system based on personalized thresholds, the structure of which is as follows: Figure 1 As shown, it includes: a first intelligent device control module, a first intelligent device early warning module, a second intelligent device control module, and a second intelligent device early warning module.

[0054] The first intelligent device control module controls the first intelligent device to monitor the target and acquire the target's first home data and second home data; the first home data is pet trajectory data, and the second home data is pet behavior data; the second home data is divided and identified according to the target's behavior to obtain third home data, which includes the target's pet hair shedding data and the target's influence range data.

[0055] The process of acquiring third-party home data in the first smart device control module is as follows:

[0056] The system acquires target behavior and target parameters, including target type, target age, target gender, and target health status. The target health status includes target physiological indicators, hair health data, and skin health data. The physiological indicators include heart rate data, body temperature data, and blood indicators. The hair health data includes data on pet hair and pet shed hair. The skin health data includes abnormal skin areas and skin severity coefficients. The skin severity coefficient is determined based on the impact of abnormal skin areas on hair loss.

[0057] The second home furnishing data is split based on the target behavior to obtain the second home furnishing split data;

[0058] A target behavior recognition model is constructed to identify the second home furnishing breakdown data and target parameters, and to obtain target hair loss data and target influence range data.

[0059] The target behavior recognition model includes a hair loss data recognition layer and an impact range recognition layer, and its construction process is as follows:

[0060] Identify the target's historical behavior data to obtain the target's historical parameters, historical behavior, historical hair loss data, and historical influence range.

[0061] The target's historical parameters, historical behavior, historical hair loss data, and historical influence range are combined to obtain first historical training data and second historical training data; the first historical training data includes target historical parameters, target historical behavior, and target historical hair loss data; the second historical training data includes target historical parameters, target historical behavior, and target historical influence range.

[0062] A hair loss data recognition layer is trained based on the first historical training data; an influence range recognition layer is trained based on the second historical training data.

[0063] To verify the effectiveness of the target behavior recognition model in recognizing target hair loss data and target influence range data, the data dimensions selected for the target health status were validated, including the first validation model, the second validation model, and the third validation model.

[0064] The first verification model is the target behavior recognition model described in this invention, which identifies target hair loss data and target influence range data through target physiological indicators, hair health data and skin health data;

[0065] The second verification model, based on the target behavior recognition model, does not consider hair health data, but only identifies target hair loss data and target influence range data through target physiological indicators and skin health data.

[0066] The third verification model, based on the target behavior recognition model, does not consider skin health data, but only identifies target hair loss data and target influence range data through target physiological indicators and hair health data.

[0067] Based on the target hair loss data and the actual hair loss data, the hair loss recognition accuracy is obtained; based on the target influence range data and the actual target influence range, the range recognition accuracy is obtained; based on the weighted calculation of the hair loss recognition accuracy and the range recognition accuracy, the model recognition accuracy is obtained, where the weight is 0.5 by default; the data obtained after multiple verifications are shown in Table 1.

[0068] Table 1 Validation Data of Target Behavior Recognition Model

[0069] Model Hair loss detection accuracy Range recognition accuracy Model recognition accuracy First verification model 0.9246 0.9048 0.9147 Second verification model 0.8624 0.8734 0.8679 Third verification model 0.8892 0.8624 0.8758

[0070] According to the data in Table 1, the first verification model has the best recognition performance.

[0071] This invention segments second home data based on target behavior to obtain segmented second home data; constructs a target behavior recognition model to identify the segmented second home data and target parameters; uses a pet shedding data recognition layer to identify the segmented second home data and target parameters to obtain pet shedding data; uses an influence range recognition layer to identify the segmented second home data and target parameters to obtain pet influence range data; and accurately identifies the shedding situation based on the pet's behavior data.

[0072] The first intelligent device early warning module obtains the surface material of the area based on the first home data; it performs clustering based on the surface material of the area, the first home data, and the third home data to obtain home sub-areas; it sets a first personalized threshold based on the surface material of the home sub-areas, and obtains the first home warning data based on the first personalized threshold and the third home data.

[0073] The first home warning data is the product of a first personalized threshold and the average hair distribution. The average hair distribution of the home sub-region is obtained based on the target hair loss data in the third home data and the area of ​​the home sub-region.

[0074] The process of dividing the home sub-regions is as follows:

[0075] Based on the first home furnishing data and the third home furnishing data, integrated home furnishing data is obtained; the integrated home furnishing data includes pet trajectory data, as well as the target shedding data and target influence range corresponding to the pet trajectory data;

[0076] The integrated home furnishing data is divided according to the surface material of the area to obtain single-material home furnishing data; the single-material home furnishing data includes integrated home furnishing data under the same surface material of the area;

[0077] The data of furniture made of a single material is clustered and divided according to the clustering algorithm to obtain furniture sub-regions.

[0078] This invention obtains integrated home furnishing data based on first and third home furnishing data. The integrated home furnishing data includes pet trajectory data, as well as target hair loss data and target influence range corresponding to the pet trajectory data. The integrated home furnishing data is divided according to the surface material of the area to obtain single-material home furnishing data. The integrated home furnishing data under the same surface material is accurately obtained. Then, the single-material home furnishing data is clustered and divided using a clustering algorithm to accurately divide the areas affected by pet hair.

[0079] The first personalized threshold reflects the difficulty of hair removal from the surface material of the area; the setting process is as follows: an experimental material sample is established based on the surface material of the area, pet hair is evenly placed on the experimental material sample, and a second smart device is selected to clean the experimental material sample. The cleaning process is the same.

[0080] A first personalized threshold is obtained based on the hair removal efficiency of the experimental material sample and the damage coefficient of the experimental material; the hair removal efficiency is determined based on the weight of pet hair removed from the placed sample, and the damage coefficient of the experimental material is obtained based on the difference between the cleaned area image and the standard image.

[0081] This invention establishes experimental material samples based on the surface material of a region, evenly places pet hair on the experimental material samples, selects a second intelligent device to clean the experimental material samples, and obtains a first personalized threshold based on the hair cleaning efficiency and the damage coefficient of the experimental material samples; based on the first personalized threshold and third home data, the cleaning difficulty of the surface material of the region is accurately measured.

[0082] The second smart device control module determines the warning sub-area based on the first home warning data of the home sub-area, controls the second smart device to clean the warning sub-area, and adjusts the parameters of the second smart device based on the first home warning data.

[0083] The second smart device early warning module acquires sub-area data of the second smart device cleaning the early warning sub-area, obtains second home early warning data based on the third home data and sub-area data, and adjusts and controls the second smart device based on the second home early warning data.

[0084] Obtain the initial weight of the warning sub-area before the second intelligent device cleans it, and the final weight of the warning sub-area after the second intelligent device cleans it.

[0085] The sub-region cleanup weight is obtained based on the initial weight and the final weight.

[0086] The system acquires an image of impurities obtained from cleaning the warning sub-region using a second intelligent device, identifies the impurity image, and obtains the distribution ratio of pet hair; it then obtains hair cleaning data and non-hair cleaning data based on the sub-region cleaning weight and the pet hair distribution ratio; and finally obtains sub-region data based on the hair cleaning data and non-hair cleaning data.

[0087] Second home warning data is obtained based on target hair loss data from hair removal data, non-hair removal data, and third home data.

[0088] The operating parameters of the second smart device are adjusted based on the second home's early warning data.

[0089] This invention obtains the initial weight of the second intelligent device before cleaning the warning sub-area, and the final weight after cleaning the warning sub-area; it then obtains the sub-area cleaning weight based on the initial and final weights; it acquires an image of the impurities obtained from the cleaning of the warning sub-area by the second intelligent device, and obtains the distribution ratio of pet hair; based on the sub-area cleaning weight and the distribution ratio of pet hair, it obtains hair cleaning data and non-hair cleaning data; and it obtains sub-area data based on the hair cleaning data and non-hair cleaning data. Accurately obtaining the pet hair cleaning status of a home sub-area allows for accurate adjustment of the second intelligent device.

[0090] This invention also proposes a method for remote home monitoring and early warning based on personalized thresholds, the process of which is as follows: Figure 2 As shown, it includes: controlling a first smart device to monitor a target, and acquiring first home data and second home data of the target; the first home data is pet trajectory data, and the second home data is pet behavior data; the second home data is divided and identified according to the target behavior to obtain third home data, wherein the third home data includes target shedding data and target influence range data;

[0091] The surface material of the area is obtained based on the first home furnishing data; the area surface material, the first home furnishing data, and the third home furnishing data are clustered to obtain home furnishing sub-regions; a first personalized threshold is set based on the surface material of the home furnishing sub-regions; and the first home furnishing warning data is obtained based on the first personalized threshold and the third home furnishing data.

[0092] Based on the first home warning data, a warning sub-area is determined, and the second smart device is controlled to clean the warning sub-area. The parameters of the second smart device are adjusted according to the first home warning data.

[0093] The system acquires sub-area data from the second smart device's cleanup of the warning sub-area, obtains second home warning data based on the third home data and the sub-area data, and adjusts and controls the second smart device based on the second home warning data.

[0094] This invention acquires first and second home data of a target; divides the second home data into third home data based on the target's behavior; obtains the surface material of the area based on the first home data; obtains home sub-regions based on the surface material, first home data, and third home data; obtains a first personalized threshold for the home sub-regions, and combines it with the third home data to obtain first home warning data; determines warning sub-regions based on the first home warning data; acquires sub-region data of cleaning the warning sub-regions by a second smart device, obtains second home warning data based on the third home data and sub-region data, and adjusts and controls the second smart device based on the second home warning data. This invention accurately achieves pet hair cleaning through the first and second home warning data.

[0095] Example 2.

[0096] The invention's home remote monitoring and early warning system based on personalized thresholds was validated using a real-world pet dog scenario. The structure of the personalized threshold-based home remote monitoring and early warning system is as follows: Figure 1 As shown, it includes:

[0097] The first intelligent device control module controls the first intelligent device to monitor the target and acquire the target's first home data and second home data; the first home data is pet trajectory data, and the second home data is pet behavior data; the second home data is divided and identified according to the target's behavior to obtain third home data, which includes the target's pet hair shedding data and the target's influence range data.

[0098] The process of acquiring the third-party home data is as follows:

[0099] Obtain target behavior and target parameters, including target type, target age, target gender, and target health status;

[0100] The second home furnishing data is split based on the target behavior to obtain the second home furnishing split data;

[0101] A target behavior recognition model is constructed to identify the second home furnishing breakdown data and target parameters, and to obtain target hair loss data and target influence range data.

[0102] The target parameters for the pet dogs were collected, as shown in Table 2.

[0103] Table 2 Target Parameter Data Table for Pet Dogs

[0104] Pet ID Target types Target age (months) Target gender 01 Labrador Retriever 25 0 02 Golden Retriever 12 1 03 Poodle 18 1 04 Chihuahua 34 0 05 Shiba Inu 23 1

[0105] In Table 2, "1" represents male and "0" represents female in the target gender column. Furthermore, the age of the pet is indicated by the month, which also reflects the impact of the month and season on pet shedding.

[0106] The target behavior recognition model includes a hair loss data recognition layer and an impact range recognition layer, and its construction process is as follows:

[0107] Identify the target's historical behavior data to obtain the target's historical parameters, historical behavior, historical hair loss data, and historical influence range.

[0108] The target's historical parameters, historical behavior, historical hair loss data, and historical influence range are combined to obtain first historical training data and second historical training data; the first historical training data includes target historical parameters, target historical behavior, and target historical hair loss data; the second historical training data includes target historical parameters, target historical behavior, and target historical influence range.

[0109] A hair loss data recognition layer is trained based on the first historical training data; an influence range recognition layer is trained based on the second historical training data.

[0110] This invention segments second home data based on target behavior to obtain segmented second home data; constructs a target behavior recognition model to identify the segmented second home data and target parameters; uses a pet shedding data recognition layer to identify the segmented second home data and target parameters to obtain pet shedding data; uses an influence range recognition layer to identify the segmented second home data and target parameters to obtain pet influence range data; and accurately identifies the shedding situation based on the pet's behavior data.

[0111] The first intelligent device early warning module obtains the surface material of the area based on the first home data; it performs clustering based on the surface material of the area, the first home data, and the third home data to obtain home sub-areas; it sets a first personalized threshold based on the surface material of the home sub-areas, and obtains the first home warning data based on the first personalized threshold and the third home data.

[0112] Based on the first home furnishing data and the third home furnishing data, integrated home furnishing data is obtained; the integrated home furnishing data includes pet trajectory data, as well as the target shedding data and target influence range corresponding to the pet trajectory data;

[0113] The integrated home furnishing data is divided according to the surface material of the area to obtain single-material home furnishing data; the single-material home furnishing data includes integrated home furnishing data under the same surface material of the area;

[0114] The data of furniture made of a single material is clustered and divided according to the clustering algorithm to obtain furniture sub-regions.

[0115] The first personalized threshold reflects the difficulty of hair removal from the surface material of the area; the setting process is as follows: an experimental material sample is established based on the surface material of the area, pet hair is evenly placed on the experimental material sample, and a second smart device is selected to clean the experimental material sample. The cleaning process is the same.

[0116] A first personalized threshold is obtained based on the hair removal efficiency of the experimental material sample and the damage coefficient of the experimental material; the hair removal efficiency is determined based on the weight of pet hair removed from the placed sample, and the damage coefficient of the experimental material is obtained based on the difference between the cleaned area image and the standard image.

[0117] The process of obtaining the first personalized threshold is shown in Table 3, where the experimental material sample selected is a square with a side length of 1 meter.

[0118] Table 3. Data table for obtaining the first personalized threshold.

[0119] Area surface material Pet hair weight Pet hair cleaning weight Damage coefficient of experimental material floor tiles 30.0g 28.1g 0.98 carpet 30.0g 22.6 0.83 sofa 30.0g 24.2 0.87 wooden dining table 30.0g 27.3 0.97 curtain 30.0g 18.4 0.92

[0120] Among them, the carpet scene is as follows Figure 3 As shown.

[0121] This invention obtains integrated home furnishing data based on first and third home furnishing data. The integrated home furnishing data includes pet trajectory data, as well as target hair loss data and target influence range corresponding to the pet trajectory data. The integrated home furnishing data is divided according to the surface material of the area to obtain single-material home furnishing data. The integrated home furnishing data under the same surface material is accurately obtained. Then, the single-material home furnishing data is clustered and divided using a clustering algorithm to accurately divide the areas affected by pet hair.

[0122] The first personalized threshold reflects the difficulty of hair removal from the surface material of the area; the setting process is as follows: an experimental material sample is established based on the surface material of the area, pet hair is evenly placed on the experimental material sample, and a second smart device is selected to clean the experimental material sample. The cleaning process is the same.

[0123] A first personalized threshold is obtained based on the hair removal efficiency of the experimental material sample and the damage coefficient of the experimental material; the hair removal efficiency is determined based on the weight of pet hair removed from the placed sample, and the damage coefficient of the experimental material is obtained based on the difference between the cleaned area image and the standard image.

[0124] This invention establishes experimental material samples based on the surface material of a region, evenly places pet hair on the experimental material samples, selects a second intelligent device to clean the experimental material samples, and obtains a first personalized threshold based on the hair cleaning efficiency and the damage coefficient of the experimental material samples; thus accurately measuring the cleaning difficulty of the surface material of the region.

[0125] The second smart device control module determines the warning sub-area based on the first home warning data of the home sub-area, controls the second smart device to clean the warning sub-area, and adjusts the parameters of the second smart device based on the first home warning data.

[0126] The second smart device early warning module acquires sub-area data of the second smart device cleaning the early warning sub-area, obtains second home early warning data based on the third home data and sub-area data, and adjusts and controls the second smart device based on the second home early warning data.

[0127] Obtain the initial weight of the warning sub-area before the second intelligent device cleans it, and the final weight of the warning sub-area after the second intelligent device cleans it.

[0128] The sub-region cleanup weight is obtained based on the initial weight and the final weight.

[0129] The system acquires an image of impurities obtained from cleaning the warning sub-region using a second intelligent device, identifies the impurity image, and obtains the distribution ratio of pet hair. Based on the sub-region cleaning weight and the distribution ratio of pet hair, it obtains hair cleaning data and non-hair cleaning data. Based on the hair cleaning data and non-hair cleaning data, it obtains sub-region data.

[0130] Secondary home warning data is obtained based on hair removal data, non-hair removal data, and target hair loss data from the third home data. The working parameters of the secondary smart device are adjusted based on the secondary home warning data. If it is determined that the area has been cleaned, the next home sub-area is cleaned. If it is determined that the area has not been cleaned, it is cleaned again. During the re-cleaning process, its parameters can be adjusted to improve the cleaning effect.

[0131] This invention obtains the initial weight of the second intelligent device before cleaning the warning sub-area, and the final weight after cleaning the warning sub-area; it then obtains the sub-area cleaning weight based on the initial and final weights; it acquires an image of the impurities obtained from the cleaning of the warning sub-area by the second intelligent device, and obtains the distribution ratio of pet hair; based on the sub-area cleaning weight and the distribution ratio of pet hair, it obtains hair cleaning data and non-hair cleaning data; and it obtains sub-area data based on the hair cleaning data and non-hair cleaning data. Accurately obtaining the pet hair cleaning status of a home sub-area allows for accurate adjustment of the second intelligent device.

[0132] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A home remote monitoring and early warning system based on personalized thresholds, characterized in that, include: The first intelligent device control module controls the first intelligent device to monitor the target and acquire the target's first home data and second home data; The first home data is pet trajectory data, and the second home data is pet behavior data; the second home data is divided and identified according to the target behavior to obtain the third home data, which includes target shedding data and target influence range data; The first intelligent device early warning module obtains the surface material of the area based on the first home data; it performs clustering based on the surface material of the area, the first home data, and the third home data to obtain home sub-areas; it obtains the first personalized threshold based on the surface material of the home sub-areas; and it obtains the first home warning data based on the first personalized threshold and the third home data. The second intelligent device control module determines the warning sub-area based on the first home warning data of the home sub-area, controls the second intelligent device to clean the warning sub-area, and adjusts the parameters of the second intelligent device based on the first home warning data; The second smart device early warning module acquires sub-area data of the second smart device cleaning the early warning sub-area, obtains second home early warning data based on the third home data and sub-area data, and adjusts and controls the second smart device based on the second home early warning data.

2. The home remote monitoring and early warning system based on personalized thresholds according to claim 1, characterized in that: The process of acquiring third-party home data in the first smart device control module is as follows: The system acquires target behavior and target parameters, including target type, target age, target gender, and target health status. The target health status includes target physiological indicators, hair health data, and skin health data. The physiological indicators include heart rate data, body temperature data, and blood parameters. The hair health data includes data on pet body hair and pet shed hair. The skin health data includes abnormal skin areas and a skin severity coefficient, determined based on the impact of abnormal skin areas on hair loss. The second home furnishing data is split based on the target behavior to obtain the second home furnishing split data; A target behavior recognition model is constructed to identify the second home furnishing breakdown data and target parameters, and to obtain target hair loss data and target influence range data.

3. A home remote monitoring and early warning system based on personalized thresholds according to claim 2, characterized in that: The target behavior recognition model includes a hair loss data recognition layer and an impact range recognition layer, and its construction process is as follows: Identify the target's historical behavior data to obtain the target's historical parameters, historical behavior, historical hair loss data, and historical influence range. The target's historical parameters, historical behavior, historical hair loss data, and historical influence range are combined to obtain first historical training data and second historical training data; the first historical training data includes target historical parameters, target historical behavior, and target historical hair loss data. The second set of historical training data includes target historical parameters, target historical behavior, and target historical influence range; A hair loss data recognition layer is trained based on the first historical training data; an influence range recognition layer is trained based on the second historical training data.

4. A home remote monitoring and early warning system based on personalized thresholds according to claim 1, characterized in that: The process of dividing the home sub-regions is as follows: Based on the first home furnishing data and the third home furnishing data, integrated home furnishing data is obtained; the integrated home furnishing data includes pet trajectory data, as well as the target shedding data and target influence range corresponding to the pet trajectory data; The integrated home furnishing data is divided according to the surface material of the area to obtain single-material home furnishing data; the single-material home furnishing data includes integrated home furnishing data under the same surface material of the area; The data of furniture made of a single material is clustered and divided according to the clustering algorithm to obtain furniture sub-regions.

5. A home remote monitoring and early warning system based on personalized thresholds according to claim 1, characterized in that: The first personalized threshold reflects the difficulty of hair removal from the surface material of the area; the setting process is as follows: an experimental material sample is established based on the surface material of the area, pet hair is evenly placed on the experimental material sample, and a second smart device is selected to clean the experimental material sample. The cleaning process is the same. A first personalized threshold is obtained based on the hair removal efficiency of the experimental material sample and the damage coefficient of the experimental material; the hair removal efficiency is determined based on the weight of pet hair removed from the placed sample; the damage coefficient of the experimental material is obtained based on the difference between the cleaned area image and the standard image.

6. A home remote monitoring and early warning system based on personalized thresholds according to claim 1, characterized in that: Obtain the initial weight of the warning sub-area before the second intelligent device cleans it, and the final weight of the warning sub-area after the second intelligent device cleans it. The sub-region cleanup weight is obtained based on the initial weight and the final weight. The system acquires an image of impurities obtained from cleaning the warning sub-region using a second intelligent device, identifies the impurity image, and obtains the distribution ratio of pet hair. Based on the sub-region cleaning weight and the distribution ratio of pet hair, it obtains hair cleaning data and non-hair cleaning data. Based on the hair cleaning data and non-hair cleaning data, it obtains sub-region data. Second home warning data is obtained based on target hair loss data from hair removal data, non-hair removal data, and third home data. The operating parameters of the second smart device are adjusted based on the second home's early warning data.

7. A method for remote home monitoring and early warning based on personalized thresholds, characterized in that, include: Control the first smart device to monitor the target and obtain the target's first home data and second home data; The first home data is pet trajectory data, and the second home data is pet behavior data; the second home data is divided and identified according to the target behavior to obtain the third home data, which includes target shedding data and target influence range data; The surface material of the area is obtained based on the first home furnishing data; the area surface material, the first home furnishing data, and the third home furnishing data are clustered to obtain home furnishing sub-regions; a first personalized threshold is set based on the surface material of the home furnishing sub-regions; and the first home furnishing warning data is obtained based on the first personalized threshold and the third home furnishing data. Based on the first home warning data, a warning sub-area is determined, and the second smart device is controlled to clean the warning sub-area. The parameters of the second smart device are adjusted according to the first home warning data. The system acquires sub-area data from the second smart device's cleanup of the warning sub-area, obtains second home warning data based on the third home data and the sub-area data, and adjusts and controls the second smart device based on the second home warning data.

8. A method for remote home monitoring and early warning based on personalized thresholds according to claim 7, characterized in that: The process of acquiring the third-party home data is as follows: The target behavior and target parameters are obtained, including target type, target age, target gender, and target health status; the target health status includes target physiological indicators, hair health data, and skin health data. The second home furnishing data is split based on the target behavior to obtain the second home furnishing split data; A target behavior recognition model is constructed to identify the second home furnishing breakdown data and target parameters, and to obtain target hair loss data and target influence range data.

9. A method for remote home monitoring and early warning based on personalized thresholds according to claim 8, characterized in that: The target behavior recognition model includes a hair loss data recognition layer and an impact range recognition layer, and its construction process is as follows: Identify the target's historical behavior data to obtain the target's historical parameters, historical behavior, historical hair loss data, and historical influence range. The target's historical parameters, historical behavior, historical hair loss data, and historical influence range are combined to obtain first historical training data and second historical training data; the first historical training data includes target historical parameters, target historical behavior, and target historical hair loss data. The second set of historical training data includes target historical parameters, target historical behavior, and target historical influence range; A hair loss data recognition layer is trained based on the first historical training data; an influence range recognition layer is trained based on the second historical training data.

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