Home desensitization video nursing system and method based on sensor

Abstract

The invention discloses a household desensitization video nursing system based on a sensor, and the system comprises a video data collection and processing module which is used for collecting and processing video data; the key node generating and marking module is used for generating and marking key data nodes; the data transmission and pushing module is used for transmitting and pushing the data; and the data processing and application module is used for processing and applying the data. According to the invention, through deep analysis of the desensitized video data, the system can automatically identify abnormal behaviors, potential safety risks and health early warning signals, and provides intelligent assistance for family care.

Description

Technical Field

[0001] The present invention belongs to the field of medical health technology, and specifically is a sensor-based family desensitization video nursing system and method. Background Art

[0002] With the aging of society and the gradual relaxation of birth control policies, young and middle-aged working families are facing unprecedented pressure on the cost of living, with a significant increase in pension and childcare expenses, becoming the new normal for family finances. In this context, a couple is required to take on the responsibility of supporting four to eight elderly people and two to three children at the same time. This phenomenon is common and has placed a huge psychological and economic burden on the main labor force in contemporary society. They not only have to ensure the physical and mental health and quality of life of their elderly relatives, but also have to balance family operations and personal career development. This dual responsibility is particularly prominent in the fast-paced modern work environment.

[0003] In response to this situation, the demand for home video care technology, as a key means of remote care, has grown dramatically. However, the existing video care system has significant technical bottlenecks: on the one hand, the video data is not effectively processed, and its huge volume not only takes up a lot of storage space, but also increases the burden of network transmission, making it difficult to achieve long-term and efficient monitoring; on the other hand, if the sensitive information in the video (such as personal portraits, home layout, etc.) is not desensitized, it is very easy to cause privacy leakage risks. At the same time, continuous monitoring of video content is not realistic for busy labor groups, resulting in delayed response to emergency events.

[0004] Although there are some video care solutions on the market, most of them focus on the realization of a single function, such as basic video recording and remote viewing, and lack comprehensive data processing and analysis capabilities. Although some high-end systems have a certain degree of intelligent analysis capabilities, they are often difficult to popularize due to low data processing efficiency, insufficient privacy protection measures or high costs. In addition, most of these systems fail to fully utilize edge computing capabilities, resulting in limited data processing and response speeds, and cannot meet the high requirements of modern families for immediacy and accuracy. Summary of the invention

[0005] In view of the above problems, the present invention is proposed to provide a sensor-based home desensitization video care system and method that overcomes the above problems or at least partially solves the above problems.

[0006] To achieve the above object, the present invention adopts the following technical solutions:

[0007] A sensor-based family desensitization video nursing system, the system comprising:

[0008] Video data acquisition and processing module, used for acquiring and processing video data;

[0009] Key node generation and marking module, used to generate and mark key data nodes;

[0010] Data transmission and push module, used for transmitting and pushing data;

[0011] Data processing and application module, used to process and apply data.

[0012] Optionally, the video data acquisition and processing module includes:

[0013] A video data acquisition unit, which acquires video data through a camera;

[0014] The video data processing unit is used to analyze the collected video data.

[0015] Optionally, the video data processing unit includes:

[0016] Transmit the collected video data to the smart chip inside the sensor;

[0017] Use the internal computing power of the sensor to decompose the video data frame by frame and perform image segmentation;

[0018] Identify the human body area in the video data and calculate the human body area data image matrix;

[0019] Image processing technology is used to blur or replace sensitive information in video data.

[0020] Optionally, the key node generation and marking module includes:

[0021] Generate key node data based on the processing results of video data,

[0022] Mark the information of each key node.

[0023] Optionally, the data transmission and push module includes:

[0024] Communicate key node data to the outside world through the network for receipt by the cloud or application software database;

[0025] The cloud or application software outside the sensor receives key node data and stores it locally, and pushes it to the video simulation unit and deep learning pattern recognition algorithm unit.

[0026] Optional data processing and application modules include:

[0027] A data receiving unit, used for receiving data pushed by a database;

[0028] A video simulation and display unit, used for video simulation and display of data;

[0029] The deep learning recognition unit is used to analyze the desensitized data.

[0030] Optionally, the video simulation and display unit includes:

[0031] Through the key point video simulation algorithm, the key points are restored frame by frame into simulated compressed video, which is output and displayed externally to form a video stream interface.

[0032] Optionally, the deep learning recognition unit includes:

[0033] Use deep learning technology and machine learning technology to conduct in-depth mining and analysis of desensitized video data;

[0034] The various postures and behavior patterns of the human body are identified, and the statistical information of each label is formed after the simulated video is calculated frame by frame according to the processed data labels, and a data interface is formed.

[0035] The present invention also provides a sensor-based family desensitization video care method, which is based on any of the above-mentioned sensor-based family desensitization video care systems, and comprises the following steps:

[0036] S1, collect video data through the camera;

[0037] S2, processing the collected video data;

[0038] S3, generating key point data of human body posture;

[0039] S4, receiving and storing the generated human body posture key point data;

[0040] S5, converting the key point data into a simulation video and displaying it;

[0041] S6. Conduct in-depth mining and analysis on the video data after desensitization processing;

[0042] S7. Calculate the simulated video frame by frame to form label statistics and a data interface.

[0043] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0044] 1. This invention deeply integrates cutting-edge technologies such as deep learning and machine learning, and has powerful video content analysis capabilities. Through in-depth analysis of desensitized video data, the system can automatically identify abnormal behaviors, potential safety risks, and health warning signals, providing intelligent assistance for family care. On this basis, the system can also build a predictive care model, using historical data and analysis results to predict and identify in advance the various risks that family members may face, providing a scientific basis for early intervention and prevention.

[0045] 2. The camera sensor of the present invention not only has the function of data collection, but also can intelligently analyze the collected data through deep learning and pattern recognition algorithms, and provide users with more personalized and accurate care suggestions. It helps to improve the intelligent level of family care and meet the diverse care needs of users. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 A schematic flow chart of a sensor-based home desensitization video care system provided in an embodiment of the present application. DETAILED DESCRIPTION

[0047] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0048] Embodiment 1:

[0049] See also Figure 1 This embodiment provides a sensor-based family desensitization video care system, the system comprising:

[0050] The video data acquisition and processing module is used to acquire and process video data.

[0051] The video data acquisition and processing module includes:

[0052] The video data acquisition unit acquires video data through a camera.

[0053] The video data processing unit is used to analyze the collected video data.

[0054] The video data processing unit comprises:

[0055] The collected video data is transmitted to the smart chip inside the sensor.

[0056] Using the internal computing power of the sensor, the video data is decomposed frame by frame and image segmentation is performed, dividing it into a digital image data matrix composed of a certain number of pixels.

[0057] The deep learning human posture estimation and recognition algorithm is used to identify the human body area in the video data, and the image matrix of the human body area data is calculated.

[0058] Image processing technology is used to blur or replace sensitive information in video data.

[0059] The key node generation and marking module is used to generate and mark key data nodes.

[0060] The key node generation and marking module includes:

[0061] Generate key node data based on the processing results of the video data.

[0062] The information of each key node is marked according to the defined data table format.

[0063] The data transmission and push module is used to transmit and push data.

[0064] The data transmission and push modules include:

[0065] The key node data is communicated to the outside through the network for reception by the cloud or application software database. The formats of published and transmitted data include: txt, pictures; the content of the transmission must at least include: device mac address, key point coordinate information, and file creation time.

[0066] The cloud or application software outside the sensor receives key node data and stores it locally, and pushes it to the video simulation unit and deep learning pattern recognition algorithm unit.

[0067] Data processing and application module, used to process and apply data.

[0068] Data processing and application modules include:

[0069] The data receiving unit is used to receive data pushed by the database.

[0070] The video simulation and display unit is used to simulate and display the data in the form of video.

[0071] The deep learning recognition unit is used to analyze the desensitized data.

[0072] The video simulation and display unit comprises:

[0073] Through the key point video simulation algorithm, the key points are restored frame by frame into simulated compressed video, which is output and displayed externally to form a video stream interface.

[0074] The deep learning recognition unit comprises:

[0075] Use deep learning technology and machine learning technology to conduct in-depth mining and analysis of desensitized video data.

[0076] By integrating frame-by-frame key point information, various postures and behavior patterns of the human body (such as: "standing", "sitting", "walking", "lying down", "waving for help", "fighting", "drinking water", "eating", "bending over", "sleeping", etc.) are accurately identified. The simulated video is calculated frame by frame according to the processed data labels to form the statistical information of each label and form a data interface.

[0077] This embodiment deeply integrates cutting-edge technologies such as deep learning and machine learning, and has powerful video content analysis capabilities. Through in-depth analysis of desensitized video data, the system can automatically identify abnormal behaviors, potential safety risks, and health warning signals, providing intelligent assistance for family care. On this basis, the system can also build a predictive care model, using historical data and analysis results to predict and identify various risks that family members may face in advance, providing a scientific basis for early intervention and prevention.

[0078] The camera sensor of this embodiment not only has the function of data collection, but also can intelligently analyze the collected data through deep learning and pattern recognition algorithms to provide users with more personalized and accurate care suggestions. This helps to improve the level of intelligence of home care and meet the diverse care needs of users.

[0079] Embodiment 2:

[0080] Embodiment 2 discloses a sensor-based family desensitization video care method, which is based on the above-mentioned sensor-based family desensitization video care system, and includes the following steps:

[0081] S1. Collect video data through a camera.

[0082] S2. Processing the collected video data.

[0083] S3. Generate key point data of human body posture.

[0084] S4. Receive and store the generated human body posture key point data.

[0085] S5. Convert key point data into simulated video and display it.

[0086] S6. Conduct in-depth mining and analysis on the desensitized video data.

[0087] S7. Calculate the simulated video frame by frame to form label statistics and a data interface.

[0088] The above contents are further detailed descriptions of the present invention in combination with specific preferred embodiments, and it cannot be determined that the specific implementation of the present invention is limited to these descriptions. For ordinary technicians in the technical field to which the present invention belongs, several simple deductions or substitutions can be made without departing from the concept of the present invention, which should be regarded as falling within the protection scope of the present invention.

Claims

1. A sensor-based family desensitization video care system, characterized in that: The system comprises: Video data acquisition and processing module, used for acquiring and processing video data; Key node generation and marking module, used to generate and mark key data nodes; Data transmission and push module, used for transmitting and pushing data; Data processing and application module, used to process and apply data.

2. A sensor-based home desensitization video care system as claimed in claim 1, characterized in that: The video data acquisition and processing module includes: A video data acquisition unit, which acquires video data through a camera; The video data processing unit is used to analyze the collected video data.

3. A sensor-based home desensitization video care system as claimed in claim 2, characterized in that: The video data processing unit comprises: Transmit the collected video data to the smart chip inside the sensor; Use the internal computing power of the sensor to decompose the video data frame by frame and perform image segmentation; Identify the human body area in the video data and calculate the human body area data image matrix; Image processing technology is used to blur or replace sensitive information in video data.

4. A sensor-based home desensitization video care system as claimed in claim 1, characterized in that: The key node generation and marking module includes: Generate key node data based on the processing results of video data, Mark the information of each key node.

5. A sensor-based home desensitization video care system as claimed in claim 4, characterized in that: The data transmission and push modules include: Communicate key node data to the outside world through the network for receipt by the cloud or application software database; The cloud or application software outside the sensor receives key node data and stores it locally, and pushes it to the video simulation unit and deep learning pattern recognition algorithm unit.

6. A sensor-based home desensitization video care system as claimed in claim 1, characterized in that: Data processing and application modules include: A data receiving unit, used for receiving data pushed by a database; A video simulation and display unit, used for video simulation and display of data; The deep learning recognition unit is used to analyze the desensitized data.

7. A sensor-based family desensitization video care system as claimed in claim 6, characterized in that: The video simulation and display unit comprises: Through the key point video simulation algorithm, the key points are restored frame by frame into simulated compressed video, which is output and displayed externally to form a video stream interface.

8. A sensor-based home desensitization video care system as claimed in claim 6, characterized in that: The deep learning recognition unit comprises: Use deep learning technology and machine learning technology to conduct in-depth mining and analysis of desensitized video data; The various postures and behavior patterns of the human body are identified, and the statistical information of each label is formed after the simulated video is calculated frame by frame according to the processed data labels, and a data interface is formed.

9. A sensor-based family desensitization video care method, the method is based on a sensor-based family desensitization video care system according to any one of claims 1 to 8, characterized in that: The method comprises the following steps: S1, collect video data through the camera; S2, processing the collected video data; S3, generating key point data of human body posture; S4, receiving and storing the generated human body posture key point data; S5, converting the key point data into a simulation video and displaying it; S6. Conduct in-depth mining and analysis on the video data after desensitization processing; S7. Calculate the simulated video frame by frame to form label statistics and a data interface.

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