FTTR-supported home security and protection monitoring integrated system and equipment

Abstract

The invention relates to the technical field of intelligent security and protection, and particularly discloses an FTTR-supported home security and protection monitoring integrated system and equipment, and the system comprises a front-end monitoring module, a data processing module, a user interaction module, an alarm notification module, a network connection module, and a background management module. The front-end monitoring module is used for collecting parameter information and indoor and outdoor image information of a home environment in real time, the data processing module is used for receiving data from monitoring equipment and carrying out data integration and processing, and the user interaction module is used for checking a monitoring picture in real time, receiving an alarm notice and carrying out remote control; according to the method, data from monitoring equipment are integrated, a dense and octree map is built to restore object distribution of the home environment, and important positions are processed independently, so that the change condition of each part in the home environment can be found, the special condition type is determined in combination with sensor data collected in real time, and the real-time monitoring of the home environment is realized. And the specific situation is reported to the user for timely handling.

Description

Technical Field

[0001] The present invention belongs to the field of intelligent security technology, and specifically relates to an integrated home security monitoring system and equipment supported by FTTR. Background Art

[0002] In the home environment, on the one hand, the elderly and young children often fall down accidentally. Once a person falls down, if they are not discovered and rescued in time, the injuries to the elderly and young children may be aggravated. On the other hand, as people's living standards continue to improve, it is also required to improve the level of intelligent security protection in the home to prevent strangers from intruding. In addition, for families with pets, when the pet owner goes out, travels on business or travels, it is necessary to know the status of the pet at home in real time. These demands have put forward requirements for video security monitoring and intelligent video analysis and recognition in the home environment.

[0003] At present, video surveillance equipment can realize intelligent analysis of collected video data within a single device and output the monitoring analysis result data of a single video device; or adopt cloud monitoring solutions to transmit collected video data to the cloud, analyze and output result data in the cloud. However, in the case of multiple video surveillance devices installed in a home environment, on the one hand, it is necessary to aggregate the multi-channel video data collected by multiple video surveillance devices and perform correlation analysis based on the multi-channel video data; on the other hand, it is necessary to protect the privacy data of home users and try to avoid uploading privacy data to the cloud. It is necessary to provide service capabilities that can store and analyze user data in the home edge environment.

[0004] Therefore, it is necessary to propose an integrated home security monitoring system and equipment supported by FTTR to at least partially solve the problems existing in the prior art.

[0005] The above information disclosed in this background technology is only used to increase the understanding of the background technology of the present invention and therefore, it may include information that does not constitute the prior art known to ordinary technicians in this field. Summary of the invention

[0006] The purpose of the present invention is to provide an integrated home security monitoring system and equipment supported by FTTR to solve the problems raised in the above background technology.

[0007] To achieve the above object, the present invention provides the following technical solutions: An integrated home security monitoring system supported by FTTR includes: a front-end monitoring module, a data processing module, a user interaction module, an alarm notification module, a network connection module, and a background management module; the front-end monitoring module is used to collect parameter information and indoor and outdoor image information of the home environment in real time, the data processing module is used to receive data from monitoring equipment and integrate and process the data, the user interaction module is used to view the monitoring screen in real time, receive alarm notifications and perform remote control, the alarm notification module is used to issue alarm sounds and flash prompts, and notify users through application push and SMS reminders, the network connection module is used to apply FTTR technology to directly introduce optical fibers into each room, and use high-performance routers and switches at the same time, and the background management module is used to manage user devices and provide technical support and consulting services to users.

[0008] Preferably, the real-time collection of parameter information of the home environment and indoor and outdoor image information: the MQTT protocol is used to collect parameter information of the home environment in real time, and the data of multiple sensors are aggregated and processed through a data fusion algorithm; at the same time, efficient video coding standards and deep learning algorithms are used for image compression processing, and histogram equalization and median filtering are used to improve image quality.

[0009] Preferably, the data received from the monitoring device: the data is preliminarily processed on the existing monitoring device using the lightweight Apache Flink framework, the data processing module is encapsulated into a container using containerization technology, and the container is deployed on the existing monitoring device to run the processing task, and the service quality policy is implemented to prioritize the processing of important application traffic.

[0010] Preferably, the data integration and processing: by equally dividing each layer of the image pyramid into blocks, using the standard deviation to calculate the size of the Hariss value of the feature point, and extracting the expected number of feature points from large to small according to the standard deviation calculation results, using the screened image key frames to generate point cloud data, constructing dense and octree point cloud maps, introducing blockchain technology to store sensor data in blocks, ensuring the integrity and non-tamperability of sensor data, and using data encryption and identity authentication mechanisms to protect users' personal privacy.

[0011] Point clouds include information such as the coordinates and colors of spatial points. Point clouds can be obtained through sensors or synthesized through image frame information. Dense reconstruction requires calculating and using the distance between pixels in the image, and different cameras have different solutions. When constructing a dense map of the home environment, the pixel color information and distance information of the key frame obtained after processing the image information are used to generate a point cloud, and then the point cloud is fused to form a dense point cloud map of the home environment.

[0012] The main steps of the dense point cloud thread are as follows: (1) Extract pixel points from the feature points of the selected image key frames; (2) The pixel coordinate values ​​extracted from the key frame are projected into the spatial coordinate system in combination with the depth information, and point cloud splicing and fusion are performed; (3) Filter out isolated point clouds and point clouds with inaccurate depth, and then construct a map for the remaining point clouds.

[0013] Dense point cloud maps can effectively restore the distribution of objects in a home environment, but the amount of data in point cloud maps is very large, so the octree map is introduced to process important locations separately to reduce the impact of surrounding objects. The basic logic of octree operation: take a certain node as the root node and divide it into eight child nodes; then continue to divide the eight nodes into eight child nodes respectively, and keep looping until all child nodes of a parent node are occupied or no information is stored. At this point, the loop ends and the octree stops dividing, otherwise it will continue to divide nodes.

[0014] The octree child nodes may be affected by noise and appear to be occupied. In order to eliminate the influence of noise, the octree uses a probability-based data method to determine whether a node is occupied. , the probability of not being occupied is , ,make Representation Node The corresponding result of the probability calculation. The conversion relationship between the two is expressed as follows:

[0015] If the node When occupancy is observed, The value will increase, and vice versa. By inversely transforming the above, we can get the following formula, which represents the node Is it occupied?

[0016] When the node When observed multiple times, let the node exist The observed value at time is , the node from the initial time to The logarithmic value of the state probability corresponding to the moment is , then the next moment can be expressed as:

[0017] For Node By adding the logarithmic integral to the observed state, the probability form of the above formula can be expressed as:

[0018] The octree uses the above probability formula to determine whether a node is occupied. When all child nodes of a parent node are occupied or not occupied, the octree stops splitting. Therefore, the octree generally cannot expand all leaf nodes, which can save storage space.

[0019] Preferably, the real-time viewing of monitoring screens and remote control: use WebSocket to achieve real-time two-way communication, transmit sensor data and image streams to the user interface, combine AR technology to display real-time data and monitoring videos of the home environment to users, and provide a user-friendly management interface and operating instructions.

[0020] Preferably, the alarm sound and flash light prompts: adaptively adjust the alarm sensitivity and response strategy according to different scenarios and behavior patterns of family members, record detailed information of all alarm events, and provide processing and response mechanisms to support users in tracing back and analyzing alarm events.

[0021] Preferably, the FTTR technology is applied to directly introduce optical fiber into each room: use multimode optical fiber to install it according to the optical fiber routing path set by the network layout, apply firewalls and access control policies, regularly check the operating status of optical fiber lines and network equipment, and keep the network equipment and firmware updated.

[0022] Preferably, the management of user devices and provision of technical support and consulting services to users includes: using a unified service management platform, integrating sensors and control devices at the software level, performing centralized processing and monitoring, collecting user feedback and suggestions, answering user questions, generating data analysis reports to help understand user behavior and device performance, and supporting decision making.

[0023] An integrated home security monitoring device supported by FTTR, comprising at least one processor; a computer-readable storage medium communicatively connected to the at least one processor; wherein, when the computer-readable storage medium is called by the at least one processor, the at least one processor can implement an integrated home security monitoring system supported by FTTR as described in any one of the above.

[0024] A computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by at least one processor, the at least one processor implements an integrated home security monitoring system supported by FTTR as described above.

[0025] Compared with the prior art, the present invention has the following beneficial effects: The present invention applies edge computing design to perform primary data processing on existing equipment, reduce the computing power of the system platform, integrate data from monitoring equipment, build dense and octree maps to restore the distribution of objects in the home environment, and focus on separate processing of important locations. In this way, changes in various parts of the home environment can be discovered, and the type of special situation can be determined in combination with real-time collected sensor data, and the specific situation can be notified to the user for timely disposal.

[0026] The present invention introduces blockchain technology to store sensor data in blocks, ensuring the integrity and non-tamperability of sensor data. At the same time, it uses data encryption and identity authentication mechanisms to protect users' personal privacy, combined with primary data processing operations on existing devices. In this way, when uploading to the cloud, the privacy information attached to the data can be reduced, reducing the probability of user privacy data leakage.

[0027] The above summary is for illustrative purposes only and is not intended to be limiting in any way. In addition to the illustrative aspects, embodiments and features described above, further aspects, embodiments and features of the present invention will be readily apparent by reference to the accompanying drawings and the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 This is a framework diagram of the integrated home security monitoring system of the present invention. DETAILED DESCRIPTION

[0029] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0030] It should be further understood that the term “and / or” used in the specification of this application and the appended claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0031] Embodiment 1:

[0032] See also Figure 1 As shown, an integrated home security monitoring system supported by FTTR includes: a front-end monitoring module for real-time collection of parameter information of the home environment and indoor and outdoor image information, using the MQTT protocol to collect parameter information of the home environment in real time, and aggregating data from multiple sensors through a data fusion algorithm; at the same time, using efficient video coding standards and deep learning algorithms for image compression processing, and using histogram equalization and median filtering to improve image quality.

[0033] The data processing module is used to receive data from monitoring devices and perform data integration and processing. It uses the lightweight Apache Flink framework to preliminarily process data on existing monitoring devices, and uses containerization technology to encapsulate the data processing module into a container, which is deployed on existing monitoring devices to run processing tasks and implement service quality policies to prioritize important application traffic.

[0034] Apache Flink is used to perform real-time stream processing on sensor and image data to ensure fast data response, run processing tasks on existing monitoring equipment, and complete the initial fusion of sensor data and image data.

[0035] A pyramid is constructed for the image, and each layer of the pyramid is divided into square areas of equal size. The standard deviation is used to calculate the Hariss value of the feature points of each square, and the feature points above the set threshold are extracted. A certain number of feature points are stored according to the number of pyramid layers, and the screened image key frames are used to generate point cloud data. Dense and octree point cloud maps are constructed, and blockchain technology is introduced to store sensor data in blocks to ensure the integrity and immutability of sensor data. At the same time, data encryption and identity authentication mechanisms are used to protect users' personal privacy.

[0036] The number of construction layers is Image pyramid, let the first Layer , image pyramid Layer can be The image of the layer is obtained by downsampling. and The specific scaling formula is as follows:

[0037] In the formula, Represents the first The scale of the layer, It is expressed as the scale factor size of each layer of the image pyramid, Represented as the number of layers of the image.

[0038] The scale of each layer of the image pyramid is different, which leads to the inability to keep the same number of feature points extracted from each layer. Therefore, each layer of the image pyramid is divided into squares of the same size. Based on the principle of uniform distribution of feature points, the total number of feature points extracted is , the expected number of feature points per layer is calculated as follows:

[0039] In the formula, Represents the first The total number of feature points required for the layer, Represents the inverse of the scale factor.

[0040] As the image is continuously downsampled, the number of feature points will gradually decrease. Therefore, it is necessary to set the expected number of feature points for each square in each layer of the image pyramid and extract feature points from large to small according to the Hariss standard deviation to obtain feature points with balanced information.

[0041] First set the The number of feature points required to extract a single grid layer , and its calculation formula is as follows:

[0042] In the formula, and Indicates The number of rows and columns of the layer division; In order to extract a sufficient number of feature points, a threshold is set for each layer of the image pyramid, and the expected number of feature points in each layer is calculated to improve the credibility of the feature points. The calculation method is as follows:

[0043] In the formula, represents the threshold value of feature extraction quantity in the grid, Indicates The average gray value of the layer image pyramid, Indicates The total number of pixels in the layer image, Represents the image pyramid The grayscale size of individual pixels of the layer.

[0044] Let the expected value of each feature point square be With minimum extraction value If the result is greater than 0, only the previous If the result is less than 0, the lowest extraction value of After extracting the number of feature points of each layer of grids according to the above method, it is necessary to select feature points with relatively uniform information from the extracted feature points. The specific steps are as follows: (1) Pyramidize the input image and calculate the number of feature points required for all pyramids. ; (2) Divide each layer of the image pyramid into squares. , and calculate and sort the Hariss value of the feature points of each square according to the standard deviation formula. The calculation formula is as follows:

[0045]

[0046] In the formula, Representative The Harss value of feature points, Represents the number of feature points in the grid and sets a threshold , if the calculated value , then the values ​​below If the number of Harss feature points extracted from the square is less than the expected value in each square of the image pyramid, then the number of feature points expected in each square is calculated. Extract the largest front in the grid. Feature points, in order to ensure the number of feature points extracted; (3) Scan Detect the squares in the layer whose feature points are not 0, and input the Harss value of the feature points in the scanned squares into the standard deviation formula, calculate the result, and sort the feature points corresponding to the result from large to small, let it be SortPF, and then compare it with the expected number of feature points in the current square. Compare, if less than , then extract all feature points in SortPF and put them into the feature point set. If it is greater than , then only extract the previous Item feature points to feature point set; (4) Expected number of feature points per layer If the difference is greater than 0, the first In addition to the feature points in the feature point set, the feature points in the layer are sorted according to the size of the standard deviation, and the feature points with the same difference value are inserted into the feature point set from large to small. If the difference value is less than 0, the feature point set is directly stored in the final feature point set; (5) If ,set up , jump to step (2) and repeat the steps until Terminate the loop.

[0047] Embodiment 2:

[0048] See also Figure 1As shown, an integrated home security monitoring system supported by FTTR also includes: a user interaction module for viewing monitoring images in real time, receiving alarm notifications and performing remote control, using WebSocket to achieve real-time two-way communication, passing sensor data and image streams to the user interface, combining AR technology to display real-time data and monitoring videos of the home environment to users, and providing a user-friendly management interface and operating instructions.

[0049] The alarm notification module is used to issue alarm sounds and flash prompts, and notify users through application push and SMS reminders. It adaptively adjusts the alarm sensitivity and response strategy according to different scenarios and behavior patterns of family members, records detailed information of all alarm events, and provides processing and response mechanisms to support users in tracing back and analyzing alarm events.

[0050] The network connection module is used to apply FTTR technology to directly introduce optical fiber into each room, and use high-performance routers and switches at the same time, use multi-mode optical fiber to install according to the optical fiber routing path set by the network layout, apply firewalls and access control policies, regularly check the operating status of optical fiber lines and network equipment, and keep the network equipment and firmware updated.

[0051] The backend management module is used to manage user devices and provide technical support and consulting services to users. It uses a unified service management platform, integrates sensors and control devices at the software level, performs centralized processing and monitoring, collects user feedback and suggestions, answers user questions, and generates data analysis reports to help understand user behavior and device performance and support decision making.

[0052] From the above, it can be seen that the present invention applies edge computing design to perform primary data processing on existing devices, reduce the computing power of the system platform, integrate data from monitoring equipment, build dense and octree maps to restore the distribution of objects in the home environment, and focus on separate processing of important locations, so that changes in various parts of the home environment can be discovered, and the type of special situation can be determined in combination with real-time collected sensor data, and the specific situation can be notified to the user for timely disposal; the present invention introduces blockchain technology to store sensor data in blocks, ensures the integrity and non-tamperability of sensor data, and uses data encryption and identity authentication mechanisms to protect the user's personal privacy, combined with primary data processing operations on existing devices, so that when uploading to the cloud, the privacy information attached to the data can be reduced, reducing the probability of user privacy data leakage.

[0053] Embodiment 3:

[0054] An embodiment of the present invention also provides an integrated home security monitoring device supported by FTTR, comprising at least one processor; and a computer-readable storage medium communicatively connected to the at least one processor; wherein, when the computer-readable storage medium is called by the at least one processor, the at least one processor can implement an integrated home security monitoring system supported by FTTR as described in any one of the above.

[0055] The embodiment of the present invention also provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by at least one processor, each process of the above-mentioned embodiment of the integrated home security monitoring system is implemented, and the same technical effect can be achieved. To avoid repetition, it is not repeated here. Among them, the computer-readable storage medium, such as read-only memory (Read-Only Memory, referred to as ROM), random access memory (Random ACGessMemory, referred to as RAM), disk or optical disk, etc.

[0056] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "examples", "specific examples" or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine different embodiments or examples described in this specification and the features of different embodiments or examples, unless they are contradictory.

[0057] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems) and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to generate a program, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A way to specify functions in one or more boxes.

[0058] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0059] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0060] In the drawings of the embodiments disclosed in the present invention, only the structures involved in the embodiments disclosed in the present invention are involved, and other structures can refer to the general design. In the absence of conflict, the same embodiment and different embodiments of the present invention can be combined with each other.

[0061] The flowcharts shown in the accompanying drawings are only examples and do not necessarily include all the contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps may also be decomposed, combined or partially merged, so the actual execution order may change according to actual conditions.

[0062] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An integrated home security monitoring system supported by FTTR, characterized in that: include: Front-end monitoring module, data processing module, user interaction module, alarm notification module, network connection module, and background management module; The front-end monitoring module is used to collect parameter information of the home environment and indoor and outdoor image information in real time; The data processing module is used to receive data from the monitoring equipment and perform data integration and processing; The user interaction module is used to view the monitoring screen in real time, receive alarm notifications and perform remote control; The alarm notification module is used to send out alarm sounds and flash reminders, and notify users through application push and SMS reminders; The network connection module is used to apply FTTR technology to directly introduce optical fiber into each room, while using high-performance routers and switches; The background management module is used to manage user equipment and provide technical support and consulting services to users.

2. According to claim 1, the integrated home security monitoring system supported by FTTR is characterized in that: The real-time collection of home environment parameter information and indoor and outdoor image information: the MQTT protocol is used to collect the home environment parameter information in real time, and the data of multiple sensors are aggregated and processed through a data fusion algorithm; at the same time, efficient video coding standards and deep learning algorithms are used for image compression processing, and histogram equalization and median filtering are used to improve image quality.

3. The integrated home security monitoring system supported by FTTR according to claim 2 is characterized in that: The receiving of data from the monitoring device: using the lightweight Apache Flink framework to preliminarily process the data on the existing monitoring device, using containerization technology to encapsulate the data processing module into a container, deploying it on the existing monitoring device to run the processing task, and implementing a service quality policy to prioritize important application traffic.

4. The integrated home security monitoring system supported by FTTR according to claim 3 is characterized by: The data integration and processing are as follows: by equally dividing each layer of the image pyramid, using the standard deviation to calculate the Hariss value of the feature point, and extracting the expected number of feature points from large to small according to the standard deviation calculation results, using the screened image key frames to generate point cloud data, constructing dense and octree point cloud maps, introducing blockchain technology to store sensor data in blocks, and using data encryption and identity authentication mechanisms to protect users' personal privacy.

5. The integrated home security monitoring system supported by FTTR according to claim 4 is characterized in that: The real-time viewing of monitoring images and remote control: WebSocket is used to achieve real-time two-way communication, sensor data and image streams are transmitted to the user interface, AR technology is combined to display real-time data and monitoring videos of the home environment to users, and a user-friendly management interface and operating instructions are provided.

6. The integrated home security monitoring system supported by FTTR according to claim 5 is characterized in that: The alarm sound and flash light prompts: according to different scenarios and the behavior patterns of family members, the alarm sensitivity and response strategy are adaptively adjusted, the detailed information of all alarm events is recorded, and a processing and response mechanism is provided.

7. The integrated home security monitoring system supported by FTTR according to claim 6 is characterized by: The application of FTTR technology introduces optical fiber directly into each room: multi-mode optical fiber is used to install the optical fiber routing path set by the network layout, firewall and access control policies are applied, the operating status of optical fiber lines and network equipment is regularly checked, and the network equipment and firmware are kept up to date.

8. The integrated home security monitoring system supported by FTTR according to claim 7 is characterized by: The management of user equipment and provision of technical support and consulting services to users: using a unified service management platform, integrating sensors and control devices at the software level, performing centralized processing and monitoring, collecting user feedback and suggestions, answering user questions, and generating data analysis reports to help understand user behavior and device performance.

9. An integrated home security monitoring device supported by FTTR, characterized in that: It includes at least one processor; a computer-readable storage medium communicatively connected to the at least one processor; wherein, when the computer-readable storage medium is called by the at least one processor, the at least one processor can implement an integrated home security monitoring system supported by an FTTR as described in any one of claims 1-8.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by at least one processor, the at least one processor implements an integrated home security monitoring system supported by FTTR as described in any one of claims 1-8.

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