Smart home monitoring method and system based on ZigBee

Abstract

The invention relates to a ZigBee-based smart home monitoring method and system, and the method comprises the steps: receiving a monitoring video stream in real time based on a ZigBee network and a camera in a low-definition resolution mode; acquiring a monitoring video stream frame by frame, and preprocessing the monitoring video stream; identifying a motion area of the current frame, and calculating the size of the motion area; when the detected motion area is greater than a first preset threshold value, adjusting the camera to a high-definition resolution mode, and starting a multi-target tracking algorithm to continuously monitor a plurality of motion objects; facial features of the moving object are extracted, the extracted features are compared with a preset family member library, and whether the moving object is a family member or not is judged; matching a user file of a preset family member library; generating a corresponding control command, and sending the control command to the smart home device; and adjusting to different working modes. According to the invention, the intelligent level of the intelligent home system and the user experience are improved.

Description

Technical Field

[0001] The present application relates to the field of intelligent monitoring, and in particular to a ZigBee-based intelligent home monitoring method and system. Background Art

[0002] Existing smart home systems tend to focus on simple automation control for a single user, but the system's processing capabilities are insufficient when faced with the diverse behavior patterns, personalized needs, and simultaneous interaction scenarios of family members. This limitation leads to inefficiency and poor user experience in providing personalized services and responding to complex needs of multiple people. Therefore, improvements are needed. Summary of the invention

[0003] In order to solve the above problems, the present application provides a ZigBee-based smart home monitoring method and system.

[0004] A ZigBee-based smart home monitoring method comprises the following steps: S10: Based on the ZigBee network and the camera in low-definition resolution mode, real-time monitoring video stream is received; S20: Acquire the monitoring video stream frame by frame and pre-process the monitoring video stream; S30: Based on the image processing algorithm, the current frame is compared with the preset background model, the motion area of ​​the current frame is identified, and the size of the motion area is calculated; S40: When the detected motion area is larger than a first preset threshold, adjusting the camera to a high-definition resolution mode, and starting a multi-target tracking algorithm to continuously monitor multiple moving objects; S50: extracting facial features of multiple moving objects based on the facial recognition API, and comparing the extracted features with a preset family member library to determine whether the moving object is a family member; S60: When the exercise object is a family member, matching a user profile of a preset family member library, wherein the user profile includes personalized settings and behavior patterns; S70: When the moving object is a non-family member, record its existence; S80: When the moving object is a family member, a corresponding parameter adjustment command is generated based on the user profile and current time information, and the parameter adjustment command is sent to the smart home device; S90: When the smart home device receives a parameter adjustment command, it adjusts to a different working mode.

[0005] In a preferred embodiment, the step of acquiring the monitoring video stream frame by frame and preprocessing the monitoring video stream comprises the following steps: Read each frame in the surveillance video stream at a fixed time interval; Each frame is preprocessed, including resizing and grayscale conversion.

[0006] In a preferred embodiment, the step of comparing the current frame with a preset background model based on an image processing algorithm, identifying the motion area of ​​the current frame, and calculating the size of the motion area comprises the steps of: When there is no moving object, collect surveillance video frames for a period of time, calculate the average value of these frames, and create a preset background model; Based on the absolute difference method, each frame is compared with the preset background model; By comparing the current frame with the preset background model, the motion area is identified; Based on the contour detection method, the size of the motion area is calculated.

[0007] In a preferred embodiment, the step of extracting facial features of multiple moving objects based on a facial recognition API, and comparing the extracted features with a preset family member library to determine whether the moving object is a family member includes the following steps: Extract facial features of moving objects based on facial recognition API and generate feature vectors; Standardize the extracted feature vectors; Compare the standardized feature vector with the feature vector of the preset family member database; When the comparison score is greater than a preset similarity threshold, the moving object is judged to be a family member; When the comparison score is less than a preset similarity threshold, it is determined that the moving object is not a family member.

[0008] In a preferred embodiment, when the moving object is a family member, a user profile in a preset family member library is matched, and the user profile user includes personalized settings and behavior patterns, including the steps of: The personalized settings include user preference settings, and the user preference settings include light brightness and air conditioning temperature; The behavior pattern includes the user's daily activity habits, and the user's daily activity habits include activity time and commonly used equipment.

[0009] In a preferred embodiment, when the smart home device receives a parameter adjustment command, the step of adjusting to a different working mode is followed by the following steps: The working modes include home mode, away mode, sleep mode and entertainment mode; When the working mode is home mode, adjust the light to 70% brightness and set the air conditioner to 25℃; When the working mode is away from home mode, turn off all lights, lock the door, and start the surveillance camera; When the working mode is sleep mode, adjust the light to 10% brightness and set the air conditioner to 20℃; When the working mode is entertainment mode, turn off all lights and set the air conditioner to 22℃.

[0010] The above-mentioned second objective of the present application is achieved through the following technical solutions: A computer device comprises a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the above-mentioned ZigBee-based smart home monitoring method are implemented.

[0011] The third objective of the present application is achieved through the following technical solutions: A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned ZigBee-based smart home monitoring method are implemented.

[0012] In summary, the present application includes at least one of the following beneficial technical effects: 1. The camera runs in low-definition resolution mode and transmits video streams in real time through the ZigBee network. This is done to minimize network bandwidth usage and data processing requirements while ensuring monitoring functions, because low-resolution video streams have smaller data volumes than high-definition video streams and are more suitable for real-time transmission in a wireless network environment. In short, this is the process of initializing the monitoring environment to ensure that real-time image information of the monitoring scene can be continuously received without consuming too many network resources; 2. Extract images frame by frame from the video captured by the camera and perform preliminary processing on these images in preparation for subsequent analysis. This process ensures that the video data is clear and in a uniform format before further use; 3. The system uses image processing algorithms to analyze the current video frame captured by the camera and compare it with a pre-set background model. This process aims to find the parts of the frame that are different from the static background, that is, the moving areas. Through this comparison, the system can identify moving objects and calculate the size of these moving areas in the picture. This step is an important part of monitoring activities, as it helps the system distinguish between dynamic elements in the background and foreground; 4. When the motion area identified by the monitoring system is larger than a set size threshold, it will be triggered. At this time, the camera will switch from low-definition mode to high-definition mode to provide clearer image details, and the system will start the tracking algorithm to continuously monitor the moving object to ensure that the activity of the object can be tracked and recorded in real time; 5. The system uses the facial recognition API to analyze the facial features of the moving object and compares these features with the facial feature library of family members stored in the system. Through this comparison, the system can determine whether the moving object is a family member, thereby realizing the recognition and distinction of family members; 6. When the system confirms that the moving object is a family member, it will further match the user profile of the member in the preset family member library. This user profile contains the personalized settings and behavior patterns of the family member, so that the system can customize the response of the smart home device based on this information, providing more personalized services and automated control; 7. The system will automatically generate control commands suitable for the family member based on the user profile of the matched family member and the current date and time information. These control commands are designed to adjust the state of smart home devices according to the user's personalized preferences and behavioral habits. The system will then send these commands to the corresponding smart home devices to perform operations such as adjusting temperature, lighting or security settings; 8. After receiving control instructions from users or automation systems, smart home devices will change their current working state according to the requirements of the instructions and enter different pre-set working modes. For example, if it is a smart light bulb, it may switch from off mode to light mode, or adjust from one brightness level to another; if it is a smart thermostat, it may switch from cooling mode to heating mode to adapt to the changing needs of indoor temperature. This process reflects the high flexibility and responsiveness of smart home devices. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 This is a flowchart of an implementation of a ZigBee-based smart home monitoring method embodiment of the present application; Figure 2 This is a flowchart for implementing step S20 in an embodiment of a ZigBee-based smart home monitoring method of the present application; Figure 3 This is a first implementation flow chart of step S30 in an embodiment of a ZigBee-based smart home monitoring method of the present application; Figure 4 This is a flowchart of an implementation of step S50 in an embodiment of a ZigBee-based smart home monitoring method of the present application; Figure 5 This is a flowchart of an implementation of step S60 in an embodiment of a ZigBee-based smart home monitoring method of the present application; Figure 6 This is an implementation flow chart after step S90 in an embodiment of a ZigBee-based smart home monitoring method of the present application; Figure 7This is a principle block diagram of a computer device of the present application. DETAILED DESCRIPTION

[0014] The following is combined with Figure 1-7 This application is described in further detail.

[0015] In one embodiment, if Figure 1 As shown, the present application discloses a ZigBee-based smart home monitoring method, which specifically includes the following: S10: Based on the ZigBee network and the camera in low-definition resolution mode, real-time monitoring video stream is received; In this embodiment, the S10 stage describes a surveillance camera system using ZigBee wireless communication technology. In this stage, the camera operates in low-definition resolution mode and transmits video streams in real time through the ZigBee network. This is done to minimize the network bandwidth usage and data processing requirements while ensuring the monitoring function, because low-resolution video streams have smaller data volumes than high-definition video streams and are more suitable for real-time transmission in a wireless network environment. In short, this is the process of initializing the monitoring environment of the system to ensure that real-time image information of the monitoring scene can be continuously received without consuming too many network resources.

[0016] S20: Acquire the monitoring video stream frame by frame and pre-process the monitoring video stream; In this embodiment, in the operation process of the monitoring system, the S20 stage involves extracting images frame by frame from the video captured by the camera and performing preliminary processing on these images in preparation for subsequent analysis. This process ensures that the video data is clear and in a uniform format before further use.

[0017] S30: Based on the image processing algorithm, the current frame is compared with the preset background model, the motion area of ​​the current frame is identified, and the size of the motion area is calculated; In this embodiment, in step S30, the system uses an image processing algorithm to analyze the current video frame captured by the camera and compares it with a pre-set background model. This process aims to find the parts of the frame that are different from the static background, that is, the moving areas. Through this comparison, the system can identify moving objects and calculate the size of the area occupied by these moving areas in the picture. This step is an important part of monitoring activities, which helps the system distinguish between dynamic elements in the background and foreground.

[0018] S40: When the detected motion area is larger than a first preset threshold, adjusting the camera to a high-definition resolution mode, and starting a multi-target tracking algorithm to continuously monitor multiple moving objects; In this embodiment, when the motion area identified in the monitoring system is larger than a set size threshold, step S40 will be triggered, at which time the camera will switch from low-definition mode to high-definition mode to provide clearer image details, and the system will start a multi-target tracking algorithm to continuously monitor multiple moving objects to ensure that the activity of the object can be tracked and recorded in real time.

[0019] S50: extracting facial features of multiple moving objects based on the facial recognition API, and comparing the extracted features with a preset family member library to determine whether the moving object is a family member; In this embodiment, in step S50, the system uses the facial recognition API to analyze the facial features of multiple moving objects, and compares these features with the facial feature library of family members stored in the system. Through this comparison, the system can determine whether the moving object is a family member, thereby realizing the recognition and distinction of family members.

[0020] S60: When the exercise object is a family member, matching a user profile of a preset family member library, wherein the user profile includes personalized settings and behavior patterns; In this embodiment, in step S60, when the system confirms that the moving object is a family member, it will further match the user profile of the member in the preset family member library. This user profile contains the personalized settings and behavior patterns of the family member, so that the system can customize the response of the smart home device based on this information, providing more personalized services and automated control.

[0021] S70: When the moving object is a non-family member, record its existence; In this embodiment, the system first needs to detect that an object is moving through a camera or other sensor. The system usually uses facial recognition, body feature recognition, or other biometric technology to distinguish whether the moving object is a family member. Family members are usually users known and registered by the system. If the system determines that the moving object is not a family member, it will perform a recording operation. This usually means that the system will save the following information: Timestamp: Record the exact time when the moving object appears. Location: Record the location or area where the moving object is located. Image or video: The system will save pictures or video clips of the moving object for subsequent review or analysis. The purpose of this rule is to enhance home security and provide additional security for the family by monitoring and recording the activities of non-family members. Such records can help family members understand who enters their home, when they enter, and what they are doing, so that they can take appropriate measures when necessary.

[0022] S80: When the moving object is a family member, a corresponding parameter adjustment command is generated based on the user profile and current time information, and the parameter adjustment command is sent to the smart home device; In this embodiment, in step S80, the system automatically generates adjustment parameter commands suitable for the member based on the user profile of the matched family member and the current date and time information. These adjustment parameter commands are intended to adjust the state of the smart home device according to the user's personalized preferences and behavioral habits, and then the system sends these commands to the corresponding smart home device to perform operations such as adjusting temperature, lighting or security settings.

[0023] S90: When the smart home device receives a parameter adjustment command, it adjusts to a different working mode.

[0024] In this embodiment, in this step, after receiving the parameter adjustment instruction from the user or the automation system, the smart home device will change its current working state according to the instruction and enter different pre-set working modes. For example, if it is a smart light bulb, it may switch from off mode to light mode, or adjust from one brightness level to another; if it is a smart thermostat, it may switch from cooling mode to heating mode to adapt to the changing needs of indoor temperature. This process reflects the high flexibility and responsiveness of smart home devices.

[0025] Figure 2 , step S20, comprising the steps of: S201: Read each frame in the surveillance video stream at a fixed time interval; S202: Preprocess each frame, including resizing and grayscale conversion.

[0026] In this embodiment, "S201" and "S202" are two consecutive steps in the monitoring system. In step "S201", the system will continuously extract each frame of the monitoring video stream according to the set time period in order to analyze and process these images. Then, in step "S202", the system will perform a series of pre-processing operations on each frame of the image just extracted. These operations include adjusting the size of the image to meet the needs of subsequent processing and converting the image to grayscale format. This can simplify the image data, reduce the amount of calculation, and help improve the speed and efficiency of image processing. These two steps are the basis of the video analysis process and prepare for subsequent advanced processing tasks such as feature extraction and recognition.

[0027] Figure 3 , step S30, comprising the steps of: S301: When there is no moving object, collect surveillance video frames for a period of time, calculate the average value of these frames, and create a preset background model; S302: Compare each frame with a preset background model based on the absolute difference method; S303: Identify the motion area by comparing the current frame with a preset background model; S304: Calculate the size of the motion area based on the contour detection method.

[0028] In this embodiment, in the monitoring video analysis process, steps "S301" to "S304" involve creating a background model and using it to detect moving objects. First, in step "S301", when there are no moving objects in the monitoring scene, the system collects video frames for a period of time and calculates the average value of these frames to create a background model, which represents the static part of the scene. Next, in step "S302", the system uses the absolute difference method to compare each frame of video with the previously created background model to find the difference. In step "S303", by comparing the current frame with the background model, the system can identify the moving areas in the video, that is, those parts that are significantly different from the background model. Finally, in step "S304", the system uses the contour detection method to calculate the size of these moving areas, so that the characteristics of the moving object can be further analyzed or the corresponding alarm action can be triggered. This series of steps is the key link of motion detection in the intelligent monitoring system.

[0029] Figure 4 , step S50, comprising the steps of: S501: extracting facial features of a moving object based on a facial recognition API and generating a feature vector; S502: Standardize the extracted feature vector; S503: Compare the standardized feature vector with the feature vector of the preset family member library; S504: When the comparison score is greater than a preset similarity threshold, it is determined that the moving object is a family member; S505: When the comparison score is less than the preset similarity threshold, it is determined that the moving object is not a family member.

[0030] In this embodiment, in the facial recognition process of the intelligent monitoring system, steps "S501" to "S505" involve extracting, processing and comparing facial features of moving objects to identify family members. First, in step "S501", the system uses the facial recognition API to extract facial features from the detected moving objects and converts them into feature vectors. Next, in step "S502", these feature vectors are standardized to ensure data consistency and comparability. Then, in step "S503", the system compares the processed feature vectors with the feature vectors in the preset family member library. In step "S504", if the comparison score exceeds the preset similarity threshold, the system will determine that the moving object is a family member. On the contrary, in step "S505", if the comparison score is lower than the threshold, the system determines that the moving object is not a family member. This series of steps realizes the rapid identification and classification of people in the monitoring screen.

[0031] Figure 5 , step S60, comprising the steps of: S601: The personalized settings include user preference settings, and the user preference settings include light brightness and air conditioning temperature; S602: The behavior pattern includes the user's daily activity habits, and the user's daily activity habits include activity time and commonly used devices.

[0032] In this embodiment, in the smart home system, steps "S601" and "S602" describe the personalized settings and behavior patterns contained in the user profile. Specifically, "S601" refers to the user's personalized settings, which cover the user's preferences, such as the brightness level of the lights and the temperature settings of the air conditioner, which are customized according to the user's personal preferences. "S602" involves the user's behavior pattern, which includes the user's daily activity habits, such as the user's activity time period (e.g., waking up, sleeping time) and frequently used devices (e.g., TV, stereo, etc.), which are used to understand and predict the user's behavior so that the system can automatically adjust the home environment to adapt to the user's living habits.

[0033] Figure 6 , step S90, and subsequent steps: S901: The working modes include home mode, away mode, sleep mode, and entertainment mode; S902: When the working mode is home mode, adjust the light to 70% brightness and set the air conditioner to 25°C; S903: When the working mode is away mode, turn off all lights, lock the door, and start the surveillance camera; S904: When the working mode is the sleep mode, adjust the light to 10% brightness and set the air conditioner to 20°C; S905: When the working mode is entertainment mode, turn off all lights and set the air conditioner to 22°C.

[0034] In this embodiment, in the smart home system, steps "S901" to "S905" define different working modes and corresponding home environment adjustments. Specifically, "S901" lists four working modes: home mode, away mode, sleep mode, and entertainment mode. Each mode has a series of environmental settings. "S902" describes that in home mode, the system automatically adjusts the light brightness to 70% and sets the air conditioning temperature to 25°C to create a comfortable home environment. "S903" describes the settings in away mode, including turning off all lights, locking door locks, and starting surveillance cameras to ensure family safety. "S904" specifies the environmental adjustments in sleep mode, adjusting the lights to 10% brightness, setting the air conditioning to 20°C, and turning off unnecessary appliances to create a suitable environment for sleeping. "S905" describes the settings in entertainment mode, turning off all lights to reduce interference, and setting the air conditioning temperature to 22°C, probably to keep comfortable when watching movies or playing games. These steps show how the smart home system automatically adjusts home devices according to different life scenarios to enhance the living experience.

[0035] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0036] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 7 As shown. The computer device includes a processor, a memory, a network interface and a database connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store family members. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a smart home monitoring method based on ZigBee is implemented.

[0037] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, a ZigBee-based smart home monitoring method is implemented.

[0038] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, a ZigBee-based smart home monitoring method is implemented.

[0039] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0040] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

Claims

1. A ZigBee-based smart home monitoring method, characterized in that: Includes steps: S10: Based on the ZigBee network and the camera in low-definition resolution mode, real-time monitoring video stream is received; S20: Acquire the monitoring video stream frame by frame and pre-process the monitoring video stream; S30: Based on the image processing algorithm, the current frame is compared with the preset background model, the motion area of ​​the current frame is identified, and the size of the motion area is calculated; S40: When the detected motion area is larger than a first preset threshold, adjusting the camera to a high-definition resolution mode, and starting a multi-target tracking algorithm to continuously monitor multiple moving objects; S50: extracting facial features of multiple moving objects based on the facial recognition API, and comparing the extracted features with a preset family member library to determine whether the moving object is a family member; S60: When the exercise object is a family member, matching a user profile of a preset family member library, wherein the user profile includes personalized settings and behavior patterns; S70: When the moving object is a non-family member, record its existence; S80: When the moving object is a family member, a corresponding parameter adjustment command is generated based on the user profile and current time information, and the parameter adjustment command is sent to the smart home device; S90: When the smart home device receives a parameter adjustment command, it adjusts to a different working mode.

2. The ZigBee-based smart home monitoring method according to claim 1, characterized in that: The step of acquiring the monitoring video stream frame by frame and preprocessing the monitoring video stream comprises the following steps: Read each frame in the surveillance video stream at a fixed time interval; Each frame is preprocessed, including resizing and grayscale conversion.

3. The ZigBee-based smart home monitoring method according to claim 1, characterized in that: The step of comparing the current frame with a preset background model based on an image processing algorithm, identifying the motion area of ​​the current frame, and calculating the size of the motion area comprises the steps of: When there is no moving object, collect surveillance video frames for a period of time, calculate the average value of these frames, and create a preset background model; Based on the absolute difference method, each frame is compared with the preset background model; By comparing the current frame with the preset background model, the motion area is identified; Based on the contour detection method, the size of the motion area is calculated.

4. The ZigBee-based smart home monitoring method according to claim 1, characterized in that: The step of extracting facial features of multiple moving objects based on the facial recognition API, and comparing the extracted features with a preset family member library to determine whether the moving object is a family member includes the following steps: Extract facial features of moving objects based on facial recognition API and generate feature vectors; Standardize the extracted feature vectors; Compare the standardized feature vector with the feature vector of the preset family member database; When the comparison score is greater than a preset similarity threshold, the moving object is judged to be a family member; When the comparison score is less than a preset similarity threshold, it is determined that the moving object is not a family member.

5. The ZigBee-based smart home monitoring method according to claim 1, characterized in that: When the moving object is a family member, the user profile in the preset family member library is matched, and the user profile user includes personalized settings and behavior patterns, including the steps of: The personalized settings include user preference settings, and the user preference settings include light brightness and air conditioning temperature; The behavior pattern includes the user's daily activity habits, and the user's daily activity habits include activity time and commonly used equipment.

6. The ZigBee-based smart home monitoring method according to claim 1, characterized in that: When the smart home device receives the parameter adjustment command, the smart home device is adjusted to a different working mode, and then the following steps are performed: The working modes include home mode, away mode, sleep mode and entertainment mode; When the working mode is home mode, adjust the light to 70% brightness and set the air conditioner to 25℃; When the working mode is away from home mode, turn off all lights, lock the door, and start the surveillance camera; When the working mode is sleep mode, adjust the light to 10% brightness and set the air conditioner to 20℃; When the working mode is entertainment mode, turn off all lights and set the air conditioner to 22℃.

7. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the steps of a ZigBee-based smart home monitoring method as described in any one of claims 1 to 6 are implemented.

8. A computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the steps of a ZigBee-based smart home monitoring method as described in any one of claims 1 to 6.

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