Camera control method and system based on Z-Wave

Abstract

The invention discloses a Z-Wave-based camera control method and a Z-Wave-based camera control system, and belongs to the field of camera communication heart machines. The method comprises the following steps: a control terminal initializes a Z-Wave module and establishes secure connection with a camera; receiving a user control instruction, wherein the instruction comprises a communication quality parameter associated with the network state of the double-Internet-of-Things card; monitoring the cellular network in real time, triggering a Z-Wve channel optimization algorithm when the network quality of the main card is poor, and calculating an optimal communication path and power; sending an encryption control signal to a camera through a Z-Wve network, and starting multi-stage timeout monitoring; the camera executes the instruction and feeds back environment sensing data; if the feedback is not received in the case of timeout, starting an adaptive retransmission mechanism; the control terminal dynamically generates or updates an adaptive control strategy according to the feedback data and the network quality by fusing historical logs; and finally encrypting and storing the whole-process data and updating the system topology and the equipment capability set. According to the invention, the control reliability, self-adaptability and safety of the camera in a complex network environment are improved.

Description

Technical Field

[0001] This application relates to the field of camera communication technology, and in particular to a camera control method and system based on Z-Wave. Background Technology

[0002] With the rapid development of information technology, the function of cameras has evolved from traditional local image acquisition and recording to a comprehensive system integrating video acquisition, network transmission, and intelligent control. The deep integration of networks, cameras, and control constitutes the core technological foundation for modern remote monitoring, machine vision, intelligent sensing, and Internet of Things applications.

[0003] Early camera systems relied heavily on analog signal transmission and localized control. Cameras were directly connected to dedicated monitors or recording equipment via coaxial cables or similar media, and control commands (such as pan / tilt rotation and focus adjustment) were implemented through separate control cables. This approach inherently suffered from complex wiring, limited transmission distance, inability to remotely access and control, and low system scalability.

[0004] The widespread adoption of internet technology, especially broadband networks and wireless communication technologies, has spurred the rise of IP cameras. IP cameras, with their built-in processors and network interfaces, can directly digitize and compress captured video signals (e.g., H.264 / 265, MJPEG) and transmit them over local area networks (LANs) or wide area networks (WANs) via TCP / IP protocols (e.g., RTP / RTSP, HTTP). Users can view the video stream in real-time from any network-accessible location using a standard web browser or dedicated client software. Simultaneously, control commands for the camera (PTZ control, parameter configuration, event triggering, etc.) are encapsulated into network protocols (e.g., ONVIF, PSIA, or vendor-specific protocols), enabling remote and centralized network-based control. This has revolutionized the deployment flexibility, management efficiency, and coverage of surveillance systems.

[0005] Patent application No. WO2020135306A1, published on July 2, 2020, discloses a method, control device, network device, and camera for controlling a camera. In the solution provided in this application, the control device determines the resolution of multiple cameras under its control based on the total uplink rate achievable by the multiple cameras and the priority of the multiple cameras, and then sends this resolution to the cameras. The control device receives guarantee information from the cameras and sends the guarantee information to the network device. In this way, the network device can reserve appropriate resources for cameras requesting uplink rate guarantees, ensuring the correct transmission of video packets from the cameras.

[0006] The shortcomings of the above technical solutions are as follows: 1. In multi-camera scenarios, wireless network resources are limited, and there is a lack of a priority-based dynamic resource allocation mechanism when transmitting camera video data back. This results in the video transmission of critical cameras (such as the forward-facing camera in remote driving) being unreliable and prone to delays or interruptions. 2. The camera resolution is set fixed and cannot be adaptively adjusted in real time according to network conditions. When network resources fluctuate, video clarity decreases or transmission fails, and there is a lack of dynamic optimization mechanisms to maintain business continuity. Summary of the Invention

[0007] To address the shortcomings of existing technologies, this application aims to provide a camera control method and system based on Z-Wave. This method can solve the problems of reliability, real-time performance, and security of camera control command transmission in heterogeneous network environments through adaptive optimization and secure communication mechanisms, ensuring that control actions can still be executed efficiently even when the network fluctuates.

[0008] To achieve the above objectives, this application adopts the following technical solution: This application provides a camera control method based on Z-Wave, including the following steps: S101, The control terminal initializes the Z-Wave wireless communication module, scans and authenticates cameras in the Z-Wave network, and establishes a secure connection between the control terminal and the camera based on the Z-Wave protocol. S102, the control terminal receives control instructions or scene policies input by the user, wherein the control instructions include at least the target device identifier, control action, and communication quality parameters associated with the dual IoT card network status; S103, the control terminal monitors the current cellular network status in real time. When the network quality of the primary IoT card is lower than the threshold, it automatically triggers the Z-Wave control channel optimization algorithm to calculate the optimal communication path and power, and obtain the optimized communication parameters. S104, according to the control command and the optimized communication parameters, the control terminal sends an encrypted control signal to the target camera through the Z-Wave network and starts a multi-level response timeout monitoring mechanism; S105, the target camera receives and decrypts the encrypted control signal, executes control actions, synchronously collects multi-dimensional environmental perception data, and packages the execution results and environmental perception data through the Z-Wave network to feed back to the control terminal. S106 If the control terminal does not receive feedback during the initial timeout period, it will start the adaptive retransmission mechanism, dynamically adjust the retransmission interval and signal strength according to the network quality, and record the retransmission trajectory. S107, the control terminal dynamically generates or updates adaptive control policies by integrating historical control logs with the feedback environmental perception data and current network quality data, and through the policy engine. S108, the control terminal encrypts and stores the signal data, environmental perception data, network quality data, and generated adaptive control strategy of this control process, and updates the collaborative control topology and device capability set.

[0009] As a preferred technical solution, in step S101, establishing a secure connection based on the Z-Wave protocol includes: The control terminal initiates an authentication process based on the security framework of the Z-Wave protocol, and performs two-way authentication by exchanging asymmetric keys and network keys. After successful authentication, the control terminal officially adds the camera device to its device list and assigns a unique node ID to the communication link, thus establishing an end-to-end secure connection between the control terminal and the camera device based on advanced encryption standards.

[0010] As a preferred technical solution, in step S102, receiving user input control commands or scene strategies includes: The instruction parsing module in the control terminal parses the input content, extracts the camera device identifier corresponding to the target control object, and the specific control action to be performed; at the same time, the network monitoring module in the system obtains the current cellular network status of the dual IoT cards in real time; the parsing module associates and encapsulates the extracted device identifier, control action and real-time communication quality parameters to form a structured control instruction data frame.

[0011] As a preferred technical solution, in step S103, the automatic triggering of the Z-Wave control channel optimization algorithm to calculate the optimal communication path and power includes: When the network quality score of the primary IoT SIM card is determined to be lower than the handover threshold, the optimization algorithm module dynamically calculates an optimal end-to-end communication path that bypasses potentially interfered relay nodes based on the current Z-Wave network device topology, the historical signal quality of each link, and the expected impact range of cellular network quality deterioration. At the same time, based on the path loss model and the target signal-to-noise ratio, appropriate radio frequency transmit power is reallocated to the control terminal and Z-Wave relay devices in the path, generating a set of optimization parameters that includes the optimal path node sequence and the recommended transmit power of each node.

[0012] As a preferred technical solution, in step S104, sending the encrypted control signal to the target camera includes: The protocol processing module within the control terminal encapsulates the structured control command data frame with the optimized parameter set, assembling it into a data frame conforming to the Z-Wave protocol application layer format. Subsequently, the security processing module uses the established S2 secure session key to encrypt the payload portion of the data frame using Advanced Encryption Standard (AES), forming the final encrypted application layer data. This encrypted data is then transmitted to the target camera via the physical radio frequency module according to the optimal communication path node sequence and transmission power specified in the optimized parameter set.

[0013] As a preferred technical solution, in step S105, the target camera receives and decrypts the encrypted control signal, executes control actions, and synchronously collects multi-dimensional environmental perception data, including: The target camera's security coprocessor uses a pre-negotiated session key to decrypt the encrypted payload and restore the original control command. The camera's main controller parses the command and drives the corresponding actuator to complete the specified control action. While executing the control action, the camera simultaneously collects multi-dimensional environmental perception data from its built-in sensors. The data includes at least ambient light intensity, infrared sensor status, and moving object information identified by the image sensor.

[0014] As a preferred technical solution, in step S106, the activation of the adaptive retransmission mechanism includes: The retransmission control module obtains the current real-time cellular and Z-Wave dual network quality indicators from the network status monitoring module, and combines them with the historical channel evaluation data of this communication to calculate an optimized retransmission interval and an increased Z-Wave transmit power through a built-in dynamic parameter algorithm. The protocol processing module uses the adjusted parameters to retransmit the encrypted original control commands through the Z-Wave network.

[0015] As a preferred technical solution, in step S107, the dynamic generation or updating of the adaptive control strategy through the strategy engine includes: The strategy engine module receives and parses the environmental perception data fed back by the target camera, and simultaneously accesses real-time dual network quality data provided by the network status monitoring module; it calls the historical log database to retrieve relevant historical control log records; the engine's built-in decision algorithm performs multi-source information fusion analysis on environmental data, network data, and historical logs to evaluate the effectiveness of the current control strategy and predict network status change trends; based on the analysis results, it dynamically generates or updates an adaptive control strategy for the camera, which is output in the form of executable configuration instructions, specifically covering the camera's working mode, video encoding parameters, and the master / slave switching rules and switching thresholds for dual IoT cards under specific network quality conditions.

[0016] As a preferred technical solution, in step S108, the updating of the cooperative control topology and device capability set includes: Based on the changes in network link stability and the response status of each camera reflected in this interaction, the topology management unit within the system dynamically fine-tunes the stored collaborative control topology and updates the connection weights or hierarchical relationships between nodes. At the same time, if a difference is found between the actual working capability of a camera and the preset capability set during the interaction, the device management module will update its device capability set accordingly.

[0017] This application also provides a Z-Wave-based camera control system, including: The terminal control module is used to initialize the Z-Wave wireless communication module, scan and authenticate cameras in the Z-Wave network, establish a secure connection with the cameras based on the Z-Wave protocol, and receive control commands or scene policies input by the user. The network monitoring and optimization module is used to monitor the current cellular network status in real time. When the network quality of the primary IoT card is lower than the threshold, it automatically triggers the Z-Wave control channel optimization algorithm to calculate the optimal communication path and power and generate optimized communication parameters. The secure communication and feedback module is used to send encrypted control signals to the target camera through the Z-Wave network according to the control commands and optimized communication parameters, start a multi-level response timeout monitoring mechanism, and receive execution results and environmental perception data from the camera. The adaptive retransmission and response module is used to initiate an adaptive retransmission mechanism when no feedback is received during the initial timeout period. It dynamically adjusts the retransmission interval and signal strength based on network quality and records the retransmission trajectory. The policy generation and management module is used to dynamically generate or update adaptive control policies based on feedback environmental awareness data and current network quality data, combined with historical control logs, and through the policy engine. The data storage and update module is used to encrypt and store signal data, environmental perception data, network quality data and generated adaptive control strategies during the control process, and update the collaborative control topology and device capability set.

[0018] Compared with the prior art, the beneficial effects of this application are as follows: This application establishes a secure connection through a Z-Wave wireless communication module (S101) and combines it with an adaptive retransmission mechanism (S106) to dynamically adjust the retransmission interval and signal strength when the network quality deteriorates, thereby significantly improving the delivery rate of control commands and reducing the risk of packet loss or timeout.

[0019] This application monitors the cellular network status in real time and triggers the Z-Wave control channel optimization algorithm (S103) to calculate the optimal communication path and power, enabling the system to adapt to network fluctuations, maintain a low-latency control link, and avoid control interruption due to failure of the primary IoT card.

[0020] This application dynamically generates or updates adaptive control strategies through a strategy engine (S107), integrates environmental perception data and historical logs, realizes the self-evolution of control strategies, and improves the system's decision-making ability in complex environments.

[0021] This application employs encrypted control signals (S104) and encrypted data storage (S108) to ensure that the control process is free from malicious interference, while building a trustworthy control ecosystem through device authentication and collaborative topology updates.

[0022] After the target camera performs a control action, it feeds back multi-dimensional environmental perception data (S105), forming a control-feedback closed loop, enabling the control terminal to evaluate the execution effect in real time and optimize subsequent instructions, thereby improving the overall control accuracy.

[0023] In summary, this application effectively solves the reliability challenges of camera control in heterogeneous networks by combining the Z-Wave protocol with an adaptive mechanism, while also taking into account low power consumption and security requirements, making it suitable for scenarios such as smart homes and industrial monitoring. Attached Figure Description

[0024] Figure 1 This is a flowchart illustrating the steps of the camera control method based on Z-Wave in this application. Detailed Implementation

[0025] To enable those skilled in the art to better understand the present application, the technical solutions in specific embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings.

[0026] like Figure 1 As shown, this application provides a camera control method based on Z-Wave, including the following steps: S101, the control terminal initializes the Z-Wave wireless communication module, scans and authenticates cameras in the Z-Wave network, and establishes a secure connection between the control terminal and the camera based on the Z-Wave protocol.

[0027] Specifically, after powering on, the control terminal loads and runs its Z-Wave protocol stack, initializing the radio frequency parameters and network configuration of the wireless communication module. Subsequently, the control terminal enters "inclusion" mode, actively broadcasting probe signals in the Z-Wave band to scan and discover camera device nodes in the "add" state within the network. For each discovered camera, the control terminal initiates an authentication process according to the Z-Wave S2 security framework, exchanging public keys with the camera and negotiating a unique network key based on a security algorithm. After successful two-way authentication, the control terminal assigns the camera a unique node ID within the network and officially registers it in its device routing table. Finally, a secure data connection based on the Z-Wave protocol, using advanced encryption standards for end-to-end encryption, is established between the control terminal and the camera.

[0028] S102, the control terminal receives control commands or scene policies input by the user. The control commands include at least the target device identifier, control actions, and communication quality parameters associated with the network status of the dual IoT cards.

[0029] Specifically, the control terminal receives user input through its human-machine interface, which includes direct control commands or predefined scene policy configuration files. The command parsing module within the control terminal parses the input, extracting the camera device identifier corresponding to the target control object and the specific control action to be performed. Simultaneously, the network monitoring module within the system reads network status parameters in real time from the modem interface of the dual IoT cards. These parameters include at least the signal strength of the primary and backup cards, network latency, packet loss rate, and the currently active data channel identifier. The command parsing module associates and encapsulates the extracted target device identifier, control action, and real-time acquired communication quality parameters to form a structured control command data frame containing network status awareness information.

[0030] S103: The control terminal monitors the current cellular network status in real time. When the network quality of the primary IoT card is lower than the threshold, it automatically triggers the Z-Wave control channel optimization algorithm to calculate the optimal communication path and power, and obtain the optimized communication parameters.

[0031] Specifically, the network status monitoring module embedded in the control terminal continuously polls the modem interfaces of the two IoT cards (primary and secondary cards) for real-time network quality parameters, including signal strength, signal-to-noise ratio, packet round-trip time, and packet loss rate. This module comprehensively evaluates these parameters of the primary IoT card based on a preset algorithm, generates a comprehensive network quality score, and compares it with a preset quality threshold. When the comprehensive network quality score of the primary card is determined to be lower than the threshold, the monitoring module immediately sends a trigger signal and a snapshot of the current network status to the Z-Wave control channel optimization algorithm module. After being triggered, the optimization algorithm module first acquires the current Z-Wave network topology and historical communication quality data for each link, and then uses the cellular network quality information carried in the trigger signal to predict potential wide-area wireless interference areas. Subsequently, based on the path discovery protocol and link cost calculation model, the algorithm dynamically calculates an optimal end-to-end communication path in the topology that avoids predicted interference nodes and has stable link quality. Simultaneously, based on the calculated path loss and target received signal-to-noise ratio, the algorithm calculates and allocates appropriate radio frequency transmit power for the control terminal itself and the Z-Wave relay nodes required in the path. Finally, the optimization algorithm module outputs a set of optimized communication parameters, which includes the node ID sequence of the optimal communication path and the corresponding RF transmit power value of each node.

[0032] S104, based on the control command and optimized communication parameters, the control terminal sends an encrypted control signal to the target camera through the Z-Wave network and initiates a multi-level response timeout monitoring mechanism.

[0033] Specifically, the protocol processing module within the control terminal receives the control command data frame and the optimized communication parameter set output by the optimization algorithm module, and encapsulates and assembles them into a data frame conforming to the Z-Wave protocol application layer format. Subsequently, the security processing module uses the S2 security session key negotiated in step S101 to encrypt the payload portion of the data frame using Advanced Encryption Standard (AES), forming the final encrypted application layer data frame. This encrypted data frame is encapsulated at the network and application layers via the Z-Wave protocol stack, and transmitted to the Z-Wave network through the physical layer's radio frequency module according to the optimal communication path node sequence and the corresponding radio frequency transmission power of the nodes specified in the optimized communication parameter set. Simultaneously with the issuance of the transmission command, the timeout management unit within the control terminal immediately initiates a multi-level response timeout monitoring mechanism for this interaction. This mechanism presets a waiting sequence containing multiple incremental time thresholds for this command and begins counting down to the first-level timeout threshold.

[0034] S105: The target camera receives and decrypts the encrypted control signal, executes control actions, synchronously collects multi-dimensional environmental perception data, and packages the execution results and environmental perception data through the Z-Wave network to feed back to the control terminal.

[0035] Specifically, the target camera receives wireless signals through its Z-Wave RF front-end. After decapsulating the data into application layer data frames via the Z-Wave protocol stack, the embedded security coprocessor uses the session key negotiated in step S101 to decrypt the encrypted payload using Advanced Encryption Standard (AES) to reconstruct the structured control commands. The camera's main control unit parses these commands and drives the corresponding pan / tilt, lens, or encoder hardware to execute the specified control actions. Simultaneously, the camera activates its various built-in sensors, collecting multi-dimensional environmental perception data, including ambient light sensor readings, passive infrared sensor status, and object position and speed information extracted by the image sensor using motion detection algorithms. Subsequently, the main control unit encapsulates the execution result code of the control actions and the collected environmental perception data into a new Z-Wave application layer data frame and encrypts it using the same security key. Finally, this encrypted feedback data frame is sent back to the control terminal via the camera's own Z-Wave protocol stack and RF module, either unicast or through a relay.

[0036] S106 If the control terminal does not receive feedback during the initial timeout period, it will activate the adaptive retransmission mechanism, dynamically adjust the retransmission interval and signal strength according to the network quality, and record the retransmission trajectory.

[0037] Specifically, the timeout management unit within the control terminal continuously monitors feedback signals from the target camera. If no valid feedback is received within the first-level timeout threshold of the multi-level response timeout monitoring mechanism, the communication is deemed to have timed out, and a trigger command is immediately sent to the retransmission control module. The retransmission control module then initiates an adaptive retransmission mechanism. It first obtains the current real-time cellular network quality parameters and Z-Wave link quality indication from the network status monitoring module, and combines this with historical channel evaluation data from this communication attempt. Using a built-in dynamic parameter algorithm based on fuzzy control or reinforcement learning, it calculates an optimized retransmission interval and an increased Z-Wave RF transmit power value. Next, the protocol processing module uses the calculated new retransmission interval and increased transmit power to retransmit the encrypted control signal to the target camera via the Z-Wave network. Simultaneously, the system's log recording module generates a retransmission trajectory record containing the retransmission timestamp, the calculated retransmission interval, the adjusted transmit power, a network quality snapshot at the time of triggering the retransmission, and the associated control command identifier, and stores this record in non-volatile memory.

[0038] S107, the control terminal dynamically generates or updates adaptive control policies by integrating feedback environmental perception data and current network quality data with historical control logs through the policy engine.

[0039] Specifically, the control terminal's policy engine module receives and parses encrypted data packets from the target camera, extracting environmental perception data and control action execution results. Simultaneously, it accesses real-time dual-IoT SIM card network quality data provided by the network monitoring module. This module calls the historical log database to retrieve historical control log records that match the current target camera's identifier, similar time periods, and environmental and network conditions. The engine's built-in decision algorithm (based on rule-based reasoning or machine learning models) performs multi-source information fusion and joint analysis on the aforementioned real-time environmental perception data, real-time network quality data, and historical log records. This evaluates the effectiveness of the current control strategy under these combined conditions and predicts short-term trends in network status and environment. Based on the analysis and prediction results, the policy engine dynamically generates or updates an adaptive control strategy for the camera. This strategy is output as an executable configuration instruction set, specifically covering the camera's operating mode (e.g., regular monitoring, motion tracking, low-power monitoring), video encoding parameters (e.g., resolution, frame rate, bitrate, keyframe interval), and the dual-IoT SIM card master / slave switching rules, switching thresholds, and post-switching parameter adjustment schemes under specific network quality conditions.

[0040] S108, the control terminal encrypts and stores the signal data, environmental perception data, network quality data, and generated adaptive control strategy of this control process, and updates the collaborative control topology and device capability set.

[0041] Specifically, after generating the adaptive control strategy in step S107, the data management module of the control terminal summarizes and encapsulates all data generated during this control interaction. This data includes at least: communication signal data from sending encrypted control signals in step S104, multi-dimensional environmental perception data from the target camera in step S105, real-time network quality data from steps S103 and S106, and the newly generated adaptive control strategy in step S107. This module first standardizes and structures this heterogeneous data to form a complete transaction log record, and encrypts it using a key of the same security level as Z-Wave (e.g., AES-128). The encrypted log record is written in parallel to the control terminal's local non-volatile memory and immediately uploaded to the cloud server for persistent storage and backup via the currently active IoT card data channel. Simultaneously, the topology management unit within the system analyzes the target camera's response latency, link stability indicators, and retransmission events during this interaction, and dynamically fine-tunes the stored collaborative control topology based on this data, updating the connection weights or hierarchical relationships between relevant nodes. In addition, the device management module compares the execution results and actual working status fed back by the camera with its preset capability set. If it finds differences such as support for new encoding formats or sensor functions, it will automatically update the internal capability set description of the device to ensure the accuracy of subsequent control strategy formulation.

[0042] As a preferred technical solution, step S101, establishing a secure connection based on the Z-Wave protocol, includes: The control terminal initiates an authentication process based on the security framework of the Z-Wave protocol, performing two-way authentication by exchanging asymmetric keys and network keys. After successful authentication, the control terminal officially adds the camera device to its device list and assigns a unique node ID to the communication link, ultimately establishing an end-to-end secure connection between the control terminal and the camera device based on Advanced Encryption Standard (AES).

[0043] In this application, the control terminal initiates an authentication request to the camera device in "containment" mode based on the S2 (Security 2) security framework defined by the Z-Wave protocol. This process begins with the control terminal generating and sending an initialization data packet containing its temporary public key to the camera.

[0044] Upon receiving a request, the camera uses its own private key and the received temporary public key to compute a shared secret using a secure algorithm (such as Elliptic Curve Diffie-Hellman, ECDH). Simultaneously, the camera generates a unique network key and encrypts it using its own public key. The camera then sends the encrypted network key, its own public key, and an authentication tag generated using the shared secret back to the control terminal. The control terminal performs reverse computation to verify the authentication tag. Upon successful verification, it decrypts the tag to obtain the network key, thus completing the two-way authentication and secure distribution of the network key based on asymmetric cryptography.

[0045] After successful two-way authentication, the control terminal officially adds the camera device to its internal Z-Wave network device list. During this process, the control terminal, acting as the network controller, assigns a unique Node ID to the camera from the available address pool within the entire Z-Wave network. This ID will serve as the unique logical address for all communication within the network.

[0046] Based on the successfully negotiated network key, a secure channel is established between the control terminal and the camera for all subsequent application-layer communication. All commands and data transmitted through this channel are encrypted and decrypted using the same network key via Advanced Encryption Standard (AES-128), thus establishing a strongly encrypted, end-to-end secure data connection between the control terminal and the camera device.

[0047] This technical solution brings the following beneficial effects: 1. Enhance overall system security: By enforcing the Z-Wave S2 security framework for authentication and key exchange, and finally using AES-128 for end-to-end encryption, it fundamentally prevents commands from being eavesdropped, tampered with, or replayed, ensuring the confidentiality and integrity of control commands and sensitive data (such as video stream metadata) during wireless transmission, and meeting the high security requirements of IoT devices.

[0048] 2. Enhanced network reliability and scalability: Each successfully authenticated camera is assigned a unique node ID across the entire network, avoiding address conflicts and ensuring that control commands can be accurately and reliably routed to the target device. This standardized network entry and addressing mechanism lays a solid foundation for smooth system expansion and the addition of more cameras or other Z-Wave devices, supporting the construction of large-scale collaborative control topologies.

[0049] 3. Achieve automation and standardization of device management: The process of establishing a secure connection (probing, authentication, key exchange, network access, and ID allocation) is standardized and automated, greatly simplifying user deployment and configuration operations and lowering the technical threshold. At the same time, the unified authentication and key management mechanism provides a consistent security baseline for all access devices, facilitating centralized security policy management and maintenance.

[0050] 4. Providing a reliable foundation for upper-level adaptive control: A robust, standardized, and verifiable secure connection is a prerequisite for the reliable execution of all subsequent intelligent control functions (such as dynamic path optimization, policy learning, and collaborative topology updates). It ensures the authenticity and non-repudiation of feedback data (such as environmental perception data and execution results), making the analysis and policy generation based on this data more reliable, thereby guaranteeing the effectiveness and robustness of the entire adaptive control system's decision-making.

[0051] As a preferred technical solution, in step S102, receiving control commands or scene strategies input by the user includes: The command parsing module within the control terminal parses the input content, extracting the camera device identifier corresponding to the target controlled object and the specific control action to be performed. Simultaneously, the network monitoring module within the system acquires the real-time cellular network status of the dual IoT SIM cards. The parsing module associates and encapsulates the extracted device identifier, control action, and real-time acquired communication quality parameters to form a structured control command data frame.

[0052] In this application, the human-machine interface of the control terminal receives operation instructions from the user. These instructions can be real-time control commands for a single camera (such as "rotate the pan-tilt") or predefined, complex scene strategy files that can trigger a series of actions (such as "away arming mode"). The instruction parsing module parses the input content, identifies and extracts core semantic elements, including the device identifier of the target control object (such as Node ID) and the specific control actions to be performed (such as preset point viewing, enabling motion tracking, adjusting encoding parameters, etc.).

[0053] While parsing user commands, an independent network monitoring module within the system continuously and in parallel acquires the real-time cellular network status of the two IoT SIM cards (primary and secondary). This module periodically reads and calculates key communication quality parameters through the operating system's network interface, primarily including signal strength, network round-trip time, and packet loss rate, and determines the currently active data channel (primary or secondary). These parameters collectively constitute a quantitative description of the current cellular network communication environment.

[0054] The instruction parsing module associates and binds the extracted "target device identifier" and "control action" with the "communication quality parameters" provided in real time by the network monitoring module. Subsequently, this heterogeneous information is encapsulated into a structured, machine-readable control instruction data frame. This data frame not only contains basic control semantics but also embeds contextual information about the current network environment, providing a data foundation for intelligent decision-making to achieve network adaptation in subsequent steps.

[0055] This technical solution brings the following beneficial effects: 1. Achieving cross-layer information fusion, laying the foundation for adaptive control: Deeply integrating and encapsulating user control intentions from the application layer with real-time physical channel states from the network layer during the instruction generation stage. This design breaks down the barriers between control and network in traditional control systems, enabling every subsequent control decision (such as channel selection, power adjustment, and retransmission strategy) to naturally and with low latency be aware of the network environment, providing the core input conditions for achieving true cross-protocol (Z-Wave and cellular) adaptive optimization.

[0056] 2. Enhance the system's decision-making intelligence and robustness in complex cellular network environments: By sensing the network quality of the dual IoT SIM cards in real time, the control commands themselves carry network status "tags." This enables the system to dynamically select or adjust the Z-Wave layer control strategy (such as optimizing communication paths and changing retransmission mechanisms) in subsequent steps based on different scenarios such as "primary SIM card is good," "primary SIM card is degraded," or "switch to secondary SIM card." This network awareness capability significantly improves the reliability and success rate of the entire monitoring system's control link in unstable or congested cellular network environments.

[0057] 3. Supports refined scene strategies and proactive control: Because the control command data frames integrate rich contextual information (network status), predefined "scene strategies" can achieve more refined and intelligent execution logic. For example, a complex strategy can be defined such as "automatically reducing the camera bitrate and prioritizing gimbal control commands when cellular network latency is high." This goes beyond simple timing action combinations, achieving conditional triggering and parameter adaptation based on environmental status, improving the efficiency of automated control and user experience.

[0058] 4. Enhance the modularity and scalability of the system architecture: By dividing functions such as instruction parsing, network monitoring, and data encapsulation into clear and independent modules, the system coupling is reduced. This modular design makes it easier to upgrade or replace any component in the future (such as adopting a new network monitoring algorithm or adding a new instruction type), which is beneficial for the long-term maintenance and functional expansion of the system.

[0059] As a preferred technical solution, in step S103, automatically triggering the Z-Wave control channel optimization algorithm to calculate the optimal communication path and power includes: When the overall network quality score of the primary IoT SIM card is determined to be lower than the handover threshold, the optimization algorithm module dynamically calculates an optimal end-to-end communication path that bypasses potentially interfered relay nodes, based on the current Z-Wave network device topology, the historical signal quality of each link, and the expected impact range of cellular network quality degradation. Simultaneously, based on the path loss model and the target signal-to-noise ratio, appropriate RF transmit power is reallocated to the control terminal and Z-Wave relay devices in the path, generating an optimized parameter set containing the optimal path node sequence and the recommended transmit power for each node.

[0060] In this application, the network status monitoring module performs weighted fusion calculations on the signal strength, signal-to-noise ratio, latency, and packet loss rate of the primary IoT SIM card to generate a real-time comprehensive network quality score. This module continuously compares the score with a preset handover threshold. When the score falls below the threshold, it is determined that the network quality of the primary SIM card does not meet the requirements, and a trigger signal is generated, which, along with a current network quality snapshot (including the type and degree of degradation indicators), is sent to the Z-Wave control channel optimization algorithm module.

[0061] Upon receiving the trigger signal, the optimization algorithm module activates the interference prediction submodule. This submodule, based on a snapshot of cellular network quality (e.g., a sudden drop in signal strength in a specific frequency band) and combined with a pre-defined mapping relationship between cellular base stations and Z-Wave device locations (or a signal propagation model), predicts the expected geographical impact range of potential cellular network quality degradation. This range is used to identify Z-Wave network relay nodes located within this area that may experience performance degradation due to co-channel or adjacent-channel interference, marking these nodes as "potential interference nodes."

[0062] The optimization algorithm module calls upon the current Z-Wave network topology (containing all nodes and their connections) and historical signal quality data for each link (such as LQI - Link Quality Indicator, RSSI - Received Signal Strength). Based on graph theory algorithms (such as the improved Dijkstra's algorithm), with the dual objectives of avoiding "potential interference nodes" and maximizing the overall link quality of the path, it dynamically searches and calculates a new optimal end-to-end communication path in the topology. This path is a sequence of relay node IDs from the control terminal to the target camera.

[0063] After determining the optimal path, the power allocation submodule recalculates the minimum RF transmit power required to just meet communication quality requirements for each node on the path (including the control terminal itself and relay nodes on the path) based on the path loss model (considering factors such as distance and obstacles) and the target signal-to-noise ratio required to ensure reliable communication. The calculation aims to reduce unnecessary radiation, minimize overall network interference, and reduce energy consumption. Finally, the algorithm generates a structured set of optimization parameters, the core of which includes: the optimal path node sequence and the recommended transmit power for each node.

[0064] This technical solution brings the following beneficial effects: 1. Achieving cross-protocol intelligent anti-interference and ensuring the reliability of the core control link: The core value of this solution lies in creating an intelligent linkage mechanism between the cellular network and the Z-Wave network. In traditional solutions, the Z-Wave network operates in isolation and cannot perceive interference from the upper-layer wide-area cellular network. This invention actively and intelligently reconstructs the Z-Wave control path to avoid interference areas by monitoring the cellular network status in real time and predicting its interference range to Z-Wave. This fundamentally solves the key problem of control command interruption or failure due to large-scale external network interference in complex wireless environments, greatly enhancing the robustness and reliability of the core control link of the monitoring system, and directly responding to the core challenge of the project: "adaptive cellular network environment".

[0065] 2. Improve overall network efficiency and reduce energy consumption: By introducing a precise power allocation algorithm based on path loss model and target signal-to-noise ratio, this scheme abandons the traditional fixed power or simple incremental power mode. It allocates appropriate power to each node on the path, minimizing transmission power while ensuring end-to-end communication quality. This not only reduces the energy consumption of individual Z-Wave devices and extends battery life, but more importantly, it significantly reduces wireless signal interference and channel collisions within the entire Z-Wave network, thereby improving the overall spectrum utilization efficiency and capacity of the network.

[0066] 3. Enhance system adaptability and proactive decision-making capabilities: This step elevates the system's response mode from passive "post-fault recovery" to proactive "pre-degradation optimization." When a decline in cellular network quality is detected but before Z-Wave communication completely fails, channel optimization is triggered in advance, and paths are replanned. This proactive adaptive adjustment mechanism allows the system to smoothly transition through periods of network fluctuation, avoiding sudden loss of control commands and the resulting frequent retransmissions, thus improving user experience and the smoothness of system control.

[0067] 4. Optimized resource utilization and support for large-scale deployment: By dynamically calculating the optimal path instead of relying on fixed routes, this solution enables the Z-Wave network to utilize all relay nodes more evenly, avoiding overload of a few nodes. Combined with power control, this provides network-level feasibility for large-scale, high-density deployment of surveillance equipment such as cameras in complex environments (such as large communities and smart parks), ensuring the scalability and stability of the control network.

[0068] As a preferred technical solution, in step S104, sending an encrypted control signal to the target camera includes: The protocol processing module within the control terminal encapsulates the structured control command data frames and optimized parameter sets, assembling them into data frames conforming to the Z-Wave protocol application layer format. Subsequently, the security processing module uses the established S2 secure session key to encrypt the payload portion of this data frame using Advanced Encryption Standard (AES), forming the final encrypted application layer data. This encrypted data is then transmitted to the target camera via the physical radio frequency module according to the optimal communication path node sequence and transmission power specified in the optimized parameter set.

[0069] In this application, the protocol processing module receives a structured control command data frame (containing the target, action, and network status) generated in step S102 and an optimization parameter set (containing the optimal path node sequence and recommended transmit power for each node) generated in step S103. This module first treats the control command data frame as the application layer data payload, and then, according to the Z-Wave protocol specification, adds an application layer frame header containing information such as command class and command to the payload. Next, it maps the "optimal path node sequence" information in the optimization parameter set to the routing table or source routing options of the Z-Wave protocol network layer, and converts the "recommended transmit power for each node" information into power control parameters for the physical layer. Finally, it assembles this information, along with the application layer data, into a complete data frame to be transmitted, conforming to the Z-Wave protocol application layer format.

[0070] The security processing module intervenes, using the S2 secure session key negotiated and stored during the secure connection establishment phase in step S101. This module performs Advanced Encryption Standard (AES) encryption on the payload portion of the assembled Z-Wave application layer data frame (i.e., the core content containing control commands) to generate ciphertext. The encrypted payload is then recombined with the unencrypted frame header, routing information, etc., to form the final encrypted application layer data frame that can be processed by the Z-Wave protocol stack, ensuring the confidentiality and integrity of commands during transmission.

[0071] The Z-Wave protocol stack (network layer and physical layer) receives this encrypted application layer data frame. The network layer, based on the "optimal path node sequence" information carried in the data frame, uses source routing to forward the data packet hop-by-hop to the designated relay node in the path, ultimately delivering it to the target camera, rather than relying on a potentially jammed default route. At each hop, the physical layer dynamically adjusts the radio frequency transmission power of its node based on the "recommended transmission power for each node" parameter. The control terminal's own radio frequency module first uses this optimized power to transmit the first hop, thus completing the entire transmission process.

[0072] This technical solution brings the following beneficial effects: 1. Achieving Deep Synergy Between Control and Transmission for a Leap in Reliability: The core innovation of this step lies in the deep binding and precise execution of upper-layer optimization decisions (optimal path and power) with the lower-layer protocol transmission mechanism. Traditional Z-Wave applications only use default routes and fixed-power transmission commands. This invention, by directly injecting the optimized parameter set into the protocol stack, forces data packets to be transmitted along a pre-calculated, interference-avoiding optimal path, and transmits them at each hop with precisely calculated minimum necessary power. This fundamentally guarantees ultra-high reliability of control command delivery in complex interference environments from the transmission mechanism perspective, representing a perfect implementation of the "adaptive control" concept at the physical transmission layer.

[0073] 2. Optimizing network energy efficiency while ensuring reliability: Traditional power control often employs a "flood" approach of high-power transmission for reliability. This invention, through precise calculation in step S103, allocates appropriate power to each hop. In this step, the physical layer strictly executes power control according to this allocation, ensuring that the network as a whole meets the signal-to-noise ratio required for reliable communication while minimizing unnecessary RF radiation and energy consumption. This significantly reduces self-interference and overall power consumption in the Z-Wave network, particularly benefiting battery-powered relay equipment and extending the continuous operating time of the entire monitoring network.

[0074] 3. Enhancing the balance between end-to-end security and transmission efficiency: By encrypting core control commands at the application layer using AES, end-to-end security of business data is ensured, preventing eavesdropping and tampering. Simultaneously, encryption is performed only in the application layer payload; routing information and frame headers remain in plaintext. This allows relay nodes in the Z-Wave network to perform correct and efficient routing forwarding without consuming resources for encryption and decryption operations. This design guarantees network efficiency for multi-hop transmission without sacrificing security.

[0075] 4. Forming a closed loop from decision-making to execution, enhancing the system's intelligence level: This step is a crucial link connecting "intelligent decision-making" (S103) and "physical actions." It transforms the abstract optimization strategy (path sequence, power value) calculated by the algorithm into specific parameters recognizable by the network protocol stack and enforces their execution, enabling the intelligent analysis in the preceding steps to produce tangible physical layer effects. This marks the formation of a complete "perception-decision-execution" adaptive closed loop, reflecting the qualitative leap of the system from traditional "open-loop control" to "intelligent closed-loop feedback control."

[0076] As a preferred technical solution, in step S105, the target camera receives and decrypts the encrypted control signal, executes control actions, and simultaneously collects multi-dimensional environmental perception data, including: The target camera's security coprocessor uses a pre-negotiated session key to decrypt the encrypted payload, restoring the original control commands. The camera's main controller parses these commands and drives the corresponding actuators to complete the specified control actions. Simultaneously, the camera acquires multi-dimensional environmental perception data from its built-in sensors, including at least ambient light intensity, infrared sensor status, and information on moving objects identified by the image sensor.

[0077] In this application, the Z-Wave radio frequency module of the target camera receives a wireless signal, which is processed by the protocol stack to the application layer, resulting in an encrypted application layer data frame. This frame is then submitted to the security coprocessor built into the camera. The security coprocessor uses the S2 secure session key, negotiated with the controller and securely stored during initial pairing (step S101), to perform Advanced Encryption Standard (AES) decryption on the encrypted payload portion of the data frame. After successful decryption and verification of message integrity, the structured, plaintext original control commands are restored.

[0078] The decrypted raw control commands are transmitted to the camera's main controller (such as a system-on-a-chip or microprocessor). The main controller parses the opcodes and parameters in the commands and then drives the corresponding actuators through the appropriate hardware interface bus (such as I2C, UART, or GPIO). For example, if the command is "pan-tilt rotation preset position 1", the main controller sends a precise rotation command to the pan-tilt drive motor through the serial port; if the command is "switch to low bitrate mode", the main controller configures the image sensor and encoding chip through the MIPICSI interface or internal registers, and adjusts parameters such as resolution and frame rate.

[0079] Simultaneously (not afterward) while driving the actuators, the camera's main controller triggers and reads data from its integrated multi-sensor array. This includes at least: reading the current ambient light intensity (Lux value) via the ambient light sensor's I2C interface; reading the digital output pin status of the passive infrared sensor to determine if a heat source is moving; and invoking the image sensor's visual analysis functions (such as through a built-in ISP or lightweight AI coprocessor) to run motion detection algorithms in real time and obtain information such as the coordinates, size, and velocity vector of moving objects in the image. This data is collected and temporarily stored in parallel by the main controller.

[0080] This technical solution brings the following beneficial effects: 1. Achieving real-time and reliable feedback in the control loop, ensuring control accuracy and security: By using end-to-end S2 security keys for decryption, the source and integrity of control commands are ensured, preventing malicious forgery of commands and unauthorized manipulation of the camera. More importantly, the high synchronization between action execution and sensor acquisition ensures that the "execution result" and "environmental state" fed back to the controller strictly correspond to the actual situation at the moment the command takes effect. This provides the controller with accurate and reliable closed-loop feedback, a fundamental prerequisite for subsequent accurate strategy evaluation and adaptive optimization, fundamentally improving the determinism and reliability of the entire control system.

[0081] 2. Provide rich contextual information for decision-making, enabling control to move from "open-loop" to "perceptual intelligence": Traditional control feedback is often limited to "success / failure" status codes. This solution requires the camera to actively collect multi-dimensional environmental perception data, such as lighting, infrared, and moving targets, while executing control actions. This is equivalent to adding rich "environmental context labels" to each control action. After this data is uploaded, the control terminal can not only know "whether the command was executed," but also understand "in what environment the command was executed," thereby supporting the system to make more intelligent decisions. For example, automatically switching between day and night modes based on lighting conditions, or automatically adjusting gimbal tracking based on moving target information.

[0082] 3. Significantly improves adaptive optimization efficiency and system response agility: Hardware-level synchronization of action execution and environmental perception at the device end avoids the serial delay of "execute first, then collect," maximizing the compression of the perception-control closed-loop latency. This allows the control terminal to obtain near real-time feedback on the environmental effects after action execution, enabling faster evaluation of the suitability of the current control strategy (such as gimbal angle and image parameters) and more agile triggering of adaptive adjustments (such as strategy generation in step S107). This significantly improves the overall efficiency and intelligence level of the system in responding to dynamically changing environments.

[0083] 4. Enabling Predictive Maintenance and Advanced Application Scenarios: Continuously and synchronously collected, refined sensing data, combined with control commands, forms a high-quality time-series dataset. This can be used not only for real-time control but also for long-term log analysis to achieve predictive maintenance (such as predicting gimbal failures through motor response data) or support advanced application scenarios (such as automatically optimizing camera cruise paths based on heatmaps of historical moving object information).

[0084] As a preferred technical solution, step S106, activating the adaptive retransmission mechanism includes: The retransmission control module obtains the current real-time cellular and Z-Wave dual network quality indicators from the network status monitoring module, and combines this with historical channel evaluation data from this communication. Using a built-in dynamic parameter algorithm, it calculates an optimized retransmission interval and an increased Z-Wave transmit power. The protocol processing module then uses the adjusted parameters to retransmit the encrypted original control commands through the Z-Wave network.

[0085] In this application, during the multi-level response timeout monitoring initiated in step S104, if the timeout management unit within the control terminal does not receive any valid feedback from the target camera within the preset primary timeout period, it immediately determines that the communication attempt has timed out and failed. The unit then sends a trigger command containing a unique identifier for this transaction to the retransmission control module.

[0086] Once the retransmission control module is triggered, it first queries the network status monitoring module to obtain a real-time snapshot of the dual network quality. This includes: the primary / secondary SIM card signal strength, latency, packet loss rate, and currently active SIM card identifier in the cellular network; and in the Z-Wave network, the real-time link quality indicators and received signal strength of each link along the path from the control terminal to the target camera. Simultaneously, the module extracts historical channel evaluation data from the logs for this communication attempt, such as the previous transmission power and channel noise level.

[0087] The retransmission control module inputs the aforementioned real-time and historical network data into its built-in dynamic parameter algorithm. The core decision logic of this algorithm (e.g., based on a reinforcement learning Q-learning model or a fuzzy logic controller) is: when cellular network quality is poor, a more aggressive retransmission strategy is preferred to compensate for instability; when Z-Wave link quality is poor, power needs to be increased to overcome path loss, and the retransmission timing should be carefully selected to avoid congestion. After comprehensive evaluation, the algorithm outputs an optimized retransmission interval (e.g., extending the interval when the network is congested and shortening the interval when the channel is idle) and an increased Z-Wave transmit power value.

[0088] The protocol processing module receives the optimized retransmission parameters from the retransmission control module. It first waits for the calculated "optimized retransmission interval" duration, then uses the "enhanced Z-Wave transmit power" parameter to re-invoke the physical layer driver and retransmit the encrypted original control commands. This process utilizes the encrypted data frame constructed in step S104 to ensure command consistency.

[0089] Simultaneously with initiating a retransmission action, the logging module generates a detailed retransmission trajectory record. This record includes at least: the retransmission timestamp, the triggered timeout level, the optimized retransmission interval used, the increased transmit power value, and the dual network quality snapshots obtained when the retransmission is triggered. This record is stored in a temporary cache to provide crucial data for subsequent network quality analysis and strategy optimization (step S107).

[0090] This technical solution brings the following beneficial effects: 1. Achieving intelligent retransmission across protocols, significantly improving control reliability in complex environments: This is the core innovative value of this step. Traditional retransmission mechanisms are based on simple timing or fixed number of retransmissions, or only consider a single-layer network (such as LQI in Z-Wave). This invention creatively integrates cellular network quality (reflecting the impact of a wide-area environment) and Z-Wave link quality (reflecting local channel conditions) for perception and joint decision-making. Through dynamic parameter algorithms, the system can intelligently distinguish the main cause of failure: whether it is due to upper-layer heartbeat interruption caused by cellular network fluctuations or Z-Wave link deterioration itself. Thus, the most targeted strategies are adopted (such as adjusting the interval to cope with network congestion, or increasing power to overcome link attenuation), achieving cross-network layer, context-aware adaptive retransmission. In complex heterogeneous network environments, the final delivery power of control commands is increased to near the theoretical limit.

[0091] 2. Effectively avoids wireless channel contention and congestion, improving overall network efficiency: By dynamically calculating the "optimized retransmission interval" based on real-time network quality (especially Z-Wave channel assessment), this mechanism proactively avoids blindly and frequently retransmitting when the channel is busy or the collision rate is high. This "backoff" and "opportunity-based" strategy significantly reduces the impact of unnecessary duplicate data packets on the shared wireless channel, lowering the overall network collision probability and channel congestion. This not only improves the success rate of the current retransmission but also protects the smooth communication of other devices within the same Z-Wave network, demonstrating good network citizenship behavior.

[0092] 3. Achieving a precise balance between ensuring reliability and optimizing energy consumption: Traditional power boosting strategies often involve fixed steps or directly using maximum power. This solution uses an algorithm to calculate the "boosted Z-Wave transmit power" based on real-time, quantified link loss. This ensures that the power boost is precise, necessary, and sufficient to overcome current link obstacles, rather than simply using maximum power. This precise control ensures that retransmitted commands reliably penetrate obstacles while maximizing energy savings and minimizing interference with other devices on the same frequency, achieving a triple optimization of reliability, energy consumption, and electromagnetic environment friendliness.

[0093] 4. Providing a high-quality data loop for network optimization and policy learning, supporting continuous system evolution: The detailed "retransmission trajectory" is extremely valuable diagnostic and learning data. It correlates a snapshot of the network environment at each communication failure (or successful retransmission) with the retransmission strategy (interval, power) adopted. This data is incorporated into the historical log, allowing the policy engine in step S107 to perform in-depth analysis to optimize the parameters of the retransmission algorithm itself, and even predict the optimal communication parameters under specific conditions. This enables the system's retransmission mechanism to learn from experience and continuously optimize itself, moving from automation based on fixed strategies to data-driven intelligence.

[0094] As a preferred technical solution, step S107, dynamically generating or updating the adaptive control strategy through the strategy engine, includes: The strategy engine module receives and parses environmental perception data from the target camera, while simultaneously accessing real-time dual network quality data provided by the network status monitoring module. It then retrieves relevant historical control log records from the historical log database. The engine's built-in decision algorithm performs multi-source information fusion analysis on environmental data, network data, and historical logs to evaluate the effectiveness of the current control strategy and predict network status change trends. Based on the analysis results, it dynamically generates or updates an adaptive control strategy for the camera. This strategy is output as executable configuration instructions, specifically covering the camera's operating mode, video encoding parameters, and the master / slave switching rules and thresholds for dual IoT cards under specific network quality conditions.

[0095] In this application, the strategy engine module receives and parses environmental perception data (such as illumination and moving object information) transmitted encrypted in step S105 from the target camera. Simultaneously, it accesses dual network quality data (including RSSI, latency, and packet loss rate of the primary / secondary IoT cards, and LQI of the Z-Wave link) provided by the network monitoring module in real time via an internal interface.

[0096] The engine accesses the historical log database and constructs query conditions based on the current camera identifier, time, geographical location, and real-time environmental network characteristics. It retrieves historical control log records under similar contexts (such as similar time periods, similar lighting conditions, and similar network load patterns). These records contain complete information such as past control commands, the network path used, and the final results (success / failure, video quality feedback).

[0097] The engine's built-in decision-making algorithm (such as a random forest-based classification and regression model or a lightweight neural network) is activated. This algorithm performs multi-source information fusion analysis on real-time environmental awareness data, real-time network quality data, and retrieved historical logs. Its core tasks are twofold: first, to evaluate the effectiveness of the current (or previous) control strategy in the current complex environment (e.g., whether the current bitrate causes stuttering under existing network jitter); and second, to predict short-term trends in network status based on network quality time-series data (e.g., predicting further attenuation of cellular signals).

[0098] Based on the above analysis, evaluation, and trend prediction results, the strategy engine dynamically calculates and generates (or updates) an adaptive control strategy specifically for this camera. This strategy is output as a set of configuration instructions that can be directly executed by both the camera and the control terminal. Its core content covers at least three levels: Operating mode commands: such as switching to "Motion Tracking Mode", "Low Light Enhancement Mode" or "Low Power Keeper Mode".

[0099] Video encoding parameter commands: such as dynamically adjusting resolution, frame rate, target bit rate, and keyframe interval.

[0100] Network behavior rule instructions: Clarify the master / slave switching rules and precise switching thresholds for dual IoT cards under specific network quality indicators (such as primary card RSSI < -90dBm and latency > 200ms), as well as the linkage adjustment scheme for video parameters after switching.

[0101] The newly generated adaptive control strategy is immediately loaded into the strategy execution unit of the control terminal, becoming the immediate basis for guiding the next round of control decisions. Simultaneously, a copy of this strategy is sent to the log system and stored along with the data used in this analysis, forming a new historical record that can be retrieved in the future, thus completing the strategy learning loop.

[0102] This technical solution brings the following beneficial effects: 1. Achieving a fundamental leap from "environmentally reactive" to "situationally predictive" control: This step is the core manifestation of the system's "intelligence." Traditional monitoring systems often adjust parameters passively based on simple thresholds (such as turning on infrared when the light level is below a certain value). This invention, through a strategy engine, integrates real-time environmental, real-time network, and historical experience data. It can not only make optimized decisions based on the current state but also make forward-looking adjustments based on trend predictions. For example, before predicting that the cellular network is about to enter a blind spot, it proactively reduces the bitrate and switches IoT cards, avoiding video stream interruptions and achieving a smooth transition, fundamentally improving system fluency and user experience.

[0103] 2. Achieving a globally dynamic optimal balance between service quality, network efficiency, and energy consumption: The policy engine's decision output is a comprehensive optimized solution. It doesn't optimize image quality (high bitrate) or maintain connectivity (blindly switching networks) in isolation, but rather dynamically balances "available network bandwidth," "the value of the current monitoring scenario" (e.g., the presence or absence of moving targets), and "device power consumption." For example, at night when there are no moving objects and the network is poor, it automatically adopts a "low bitrate + low frame rate + secondary card" strategy to maximize bandwidth and power savings while ensuring basic monitoring functions. This global adaptive capability based on multi-objective optimization cannot be achieved with single threshold control.

[0104] 3. A continuously evolving control strategy knowledge base is formed, making the system increasingly intelligent with use: Every control interaction, environmental awareness, network status, and the final generated strategy is encrypted and stored as a historical log. This allows the decision-making foundation of the strategy engine (historical log database) to continuously enrich and evolve over time. The system can learn the optimal control parameters for specific locations, times, weather conditions, and network environments, thus making the adaptive strategy increasingly accurate and increasingly tailored to the unique patterns of the actual deployment environment, achieving a qualitative leap from "factory-preset intelligence" to "on-site learning intelligence."

[0105] 4. Significantly reduce operational complexity and costs, and improve system reliability: By automating complex parameter tuning and network switching decisions that previously required manual experience, the reliance on professional operations and maintenance personnel is greatly reduced. The system can automatically respond to daily network fluctuations and environmental changes, maintaining stable monitoring services. This significantly reduces the long-term operational costs and frequency of manual intervention for large-scale monitoring deployments. Simultaneously, through automated and intelligent rapid response, it reduces monitoring gaps caused by untimely human intervention, improving the overall system's business continuity and reliability.

[0106] As a preferred technical solution, step S108, updating the cooperative control topology and device capability set includes: Based on the changes in network link stability and the response status of each camera reflected in this interaction, the topology management unit within the system dynamically fine-tunes the stored collaborative control topology, updating the connection weights or hierarchical relationships between nodes. Simultaneously, if a discrepancy is found between the actual working capabilities of a camera and the preset capability set during the interaction, the device management module will update its device capability set accordingly.

[0107] In this application, after each control interaction, the topology management unit extracts key performance indicators from the logs, including: the end-to-end response time from sending the command to receiving a complete response, the number of retransmissions recorded in step S106, the Z-Wave link quality change curve during the interaction, and the target camera's response success rate. Based on this raw data, the unit calculates a comprehensive link stability score and a node response reliability score.

[0108] This unit compares the calculated score with historical averages and preset thresholds. Based on a set of predefined rules (e.g., if a path's stability score continuously declines and falls below a threshold, its quality is considered degraded), it dynamically adjusts the Z-Wave network topology stored in memory. Adjustment operations include: updating connection weights between nodes (increasing the weight of stable paths and decreasing the weight of unstable paths to influence subsequent optimal path calculations), or reconstructing hierarchical relationships in extreme cases (e.g., demoting a frequently timed-out relay node from a core routing node to a leaf node, or introducing a backup relay node to establish a new logical connection).

[0109] The fine-tuned new topology map will take effect immediately, replacing the old version, and will be used for the next control command routing decision (steps S103, S104). At the same time, a topology change summary is recorded in the encrypted log.

[0110] The device management module continuously compares the camera's preset capabilities with the actual capabilities reflected in the current (and recent) interactions in the background. This is achieved through various mechanisms, such as: analyzing whether the camera's feedback data contains video encoding formats or sensor data types not declared in the presets; checking whether the camera successfully executes certain advanced control commands (such as specific gimbal cruise modes) or returns an "unsupported" error code; and analyzing whether its actual night vision mode in low-light conditions is consistent with the preset.

[0111] Once a discrepancy is confirmed (e.g., a camera actually supports H.265 encoding while the default only supports H.264), the device management module will not directly overwrite the original default. Instead, it will update the local device capability set description in a secure and auditable manner. Typically, it creates a timestamped "discovered capabilities" extended record for the device in the database. This update ensures that the control list in subsequent step S102 and the control policy generated by the policy engine in step S107 are calculated based on the most accurate device capability information, avoiding the issuance of commands that the device cannot execute or the failure to fully utilize its new features.

[0112] This technical solution brings the following beneficial effects: 1. Achieving dynamic self-optimization of network and device models, enabling the system to possess "immunity" and "evolutionary" capabilities: This is the core value of this step. Traditional IoT systems typically have statically configured topology and device information. This invention endows the system with the ability to perceive real-time changes in network performance and the actual state of devices, and to automatically and safely adjust its internal model. This allows the system to proactively avoid network paths with degraded performance, adapt to new functions brought about by device firmware upgrades or environmental changes, and thus maintain optimal overall control performance when facing network fluctuations and device iterations, exhibiting adaptive and evolutionary characteristics similar to those of a living organism.

[0113] 2. Preventing failures at their source and improving the reliability and maintainability of large-scale networks: Through continuous fine-tuning of the topology, the system can marginalize or replace potential failures (such as link instability caused by aging relay nodes) before they develop into complete communication outages, achieving predictive maintenance. This is especially important in large-scale monitoring networks with hundreds or thousands of nodes, significantly reducing the risk of monitoring failure across the entire area due to single-point or localized problems, greatly improving network robustness and business continuity, while reducing the troubleshooting burden on maintenance personnel.

[0114] 3. Optimize network resource utilization and decision-making accuracy: Dynamically updated connection weights ensure that the path selection algorithm (step S103) is always calculated based on the latest and most realistic network conditions, thereby guiding control traffic along the smoothest "lane" and optimizing the allocation of network bandwidth and latency resources. Simultaneously, accurately updated device capability sets are the foundation for precise control and policy generation. This ensures that the control terminal does not "waste instructions" by trying non-existent functions, nor does it "miss" available advanced functions (such as updated encoding formats that can save bandwidth), thus achieving a dual improvement in both control accuracy and resource efficiency.

[0115] 4. Forming a complete autonomous intelligent closed loop of "perception-decision-execution-learning": Step S108 is the final piece of the puzzle for achieving true "adaptability" in this method, and also the culmination of the intelligent closed loop. It enables the system not only to react based on the current state (S103-S107), but also to use the results of each interaction to reverse-optimize its own "worldview" (topology and capability set). This continuous learning and optimization cycle allows the system to continuously accumulate experience, adapt to long-term changes in the deployment environment, and its control efficiency and reliability can continuously improve over time, rather than remaining at a fixed level at the time of manufacture, thus realizing the leap from an automated system to an autonomous intelligent system.

[0116] This application also provides a Z-Wave-based camera control system, including: The terminal control module is used to initialize the Z-Wave wireless communication module, scan and authenticate cameras in the Z-Wave network, establish a secure connection with the cameras based on the Z-Wave protocol, and receive control commands or scene policies input by the user.

[0117] The network monitoring and optimization module is used to monitor the current cellular network status in real time. When the network quality of the primary IoT card is lower than the threshold, the Z-Wave control channel optimization algorithm is automatically triggered to calculate the optimal communication path and power, and generate optimized communication parameters.

[0118] The secure communication and feedback module is used to send encrypted control signals to the target camera through the Z-Wave network according to control commands and optimized communication parameters, initiate a multi-level response timeout monitoring mechanism, and receive execution results and environmental perception data from the camera.

[0119] The adaptive retransmission and response module is used to initiate an adaptive retransmission mechanism when no feedback is received during the initial timeout period. It dynamically adjusts the retransmission interval and signal strength based on network quality and records the retransmission trajectory.

[0120] The policy generation and management module is used to dynamically generate or update adaptive control policies based on feedback environmental awareness data and current network quality data, combined with historical control logs, through the policy engine.

[0121] The data storage and update module is used to encrypt and store signal data, environmental perception data, network quality data and generated adaptive control strategies during the control process, and update the collaborative control topology and device capability set.

[0122] It should be noted that the terms "first," "second," and similar terms used in this application specification and claims do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Similarly, "a" or "one," and similar terms do not indicate a quantity limitation, but rather indicate the presence of at least one. "A plurality" or "several" indicates at least two. Unless otherwise stated, terms such as "front," "back," "left," "right," "lower," and / or "upper" are for illustrative purposes only and are not limited to a location or spatial orientation. Terms such as "comprising" or "including" indicate that the elements or objects preceding "comprising" encompass the elements or objects listed following "comprising" or "including" and their equivalents, and do not exclude other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect.

[0123] The singular forms “a,” “the,” and “the” used in this application specification and appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any or all possible combinations of one or more of the associated listed items.

[0124] It should be understood that those skilled in the art can make improvements or modifications based on the above description, and all such improvements and modifications should fall within the protection scope of the appended claims.

Claims

1. A camera control method based on Z-Wave, characterized in that, Includes the following steps: S101, The control terminal initializes the Z-Wave wireless communication module, scans and authenticates cameras in the Z-Wave network, and establishes a secure connection between the control terminal and the camera based on the Z-Wave protocol. S102, the control terminal receives control instructions or scene policies input by the user, wherein the control instructions include at least the target device identifier, control action, and communication quality parameters associated with the dual IoT card network status; S103, the control terminal monitors the current cellular network status in real time. When the network quality of the primary IoT card is lower than the threshold, it automatically triggers the Z-Wave control channel optimization algorithm to calculate the optimal communication path and power, and obtain the optimized communication parameters. S104, according to the control command and optimized communication parameters, the control terminal sends an encrypted control signal to the target camera through the Z-Wave network and starts a multi-level response timeout monitoring mechanism; S105, the target camera receives and decrypts the encrypted control signal, executes control actions, synchronously collects multi-dimensional environmental perception data, and packages the execution results and environmental perception data through the Z-Wave network to feed back to the control terminal. S106 If the control terminal does not receive feedback during the initial timeout period, it will start the adaptive retransmission mechanism, dynamically adjust the retransmission interval and signal strength according to the network quality, and record the retransmission trajectory. S107, the control terminal dynamically generates or updates adaptive control policies by integrating historical control logs with the feedback environmental perception data and current network quality data, and through the policy engine. S108, the control terminal encrypts and stores the signal data, environmental perception data, network quality data, and generated adaptive control strategy of this control process, and updates the collaborative control topology and device capability set.

2. The camera control method based on Z-Wave according to claim 1, characterized in that, In step S101, establishing a secure connection based on the Z-Wave protocol includes: The control terminal initiates an authentication process based on the security framework of the Z-Wave protocol, and performs two-way authentication by exchanging asymmetric keys and network keys. After successful authentication, the control terminal officially adds the camera device to its device list and assigns a unique node ID to the communication link, thus establishing an end-to-end secure connection between the control terminal and the camera device based on advanced encryption standards.

3. The camera control method based on Z-Wave according to claim 1 or 2, characterized in that, In step S102, receiving control commands or scene strategies input by the user includes: The instruction parsing module in the control terminal parses the input content, extracts the camera device identifier corresponding to the target control object, and the specific control action to be performed; at the same time, the network monitoring module in the system obtains the current cellular network status of the dual IoT cards in real time; the parsing module associates and encapsulates the extracted device identifier, control action and real-time communication quality parameters to form a structured control instruction data frame.

4. The camera control method based on Z-Wave according to claim 3, characterized in that, In step S103, the automatic triggering of the Z-Wave control channel optimization algorithm to calculate the optimal communication path and power includes: When the network quality score of the primary IoT SIM card is determined to be lower than the handover threshold, the optimization algorithm module dynamically calculates an optimal end-to-end communication path that bypasses potentially interfered relay nodes based on the current Z-Wave network device topology, the historical signal quality of each link, and the expected impact range of cellular network quality deterioration. At the same time, based on the path loss model and the target signal-to-noise ratio, appropriate radio frequency transmit power is reallocated to the control terminal and Z-Wave relay devices in the path, generating a set of optimization parameters that includes the optimal path node sequence and the recommended transmit power of each node.

5. The camera control method based on Z-Wave according to claim 4, characterized in that, In step S104, sending the encrypted control signal to the target camera includes: The protocol processing module within the control terminal encapsulates the structured control command data frame with the optimized parameter set, assembling it into a data frame conforming to the Z-Wave protocol application layer format. Subsequently, the security processing module uses the established S2 secure session key to encrypt the payload portion of the data frame using Advanced Encryption Standard (AES), forming the final encrypted application layer data. This encrypted data is then transmitted to the target camera via the physical radio frequency module according to the optimal communication path node sequence and transmission power specified in the optimized parameter set.

6. The camera control method based on Z-Wave according to claim 5, characterized in that, In step S105, the target camera receives and decrypts the encrypted control signal, executes control actions, and simultaneously collects multi-dimensional environmental perception data, including: The target camera's security coprocessor uses a pre-negotiated session key to decrypt the encrypted payload and restore the original control command. The camera's main controller parses the command and drives the corresponding actuator to complete the specified control action. While executing the control action, the camera simultaneously collects multi-dimensional environmental perception data from its built-in sensors. The data includes at least ambient light intensity, infrared sensor status, and moving object information identified by the image sensor.

7. The camera control method based on Z-Wave according to claim 5, characterized in that, In step S106, the activation of the adaptive retransmission mechanism includes: The retransmission control module obtains the current real-time cellular and Z-Wave dual network quality indicators from the network status monitoring module, and combines them with the historical channel evaluation data of this communication to calculate an optimized retransmission interval and an increased Z-Wave transmit power through a built-in dynamic parameter algorithm. The protocol processing module uses the adjusted parameters to retransmit the encrypted original control commands through the Z-Wave network.

8. The camera control method based on Z-Wave according to claim 6, characterized in that, In step S107, the dynamic generation or updating of the adaptive control strategy through the strategy engine includes: The strategy engine module receives and parses the environmental perception data fed back by the target camera, and simultaneously accesses real-time dual network quality data provided by the network status monitoring module; it calls the historical log database to retrieve relevant historical control log records; the engine's built-in decision algorithm performs multi-source information fusion analysis on environmental data, network data, and historical logs to evaluate the effectiveness of the current control strategy and predict network status change trends; based on the analysis results, it dynamically generates or updates an adaptive control strategy for the camera, which is output in the form of executable configuration instructions, specifically covering the camera's working mode, video encoding parameters, and the master / slave switching rules and switching thresholds for dual IoT cards under specific network quality conditions.

9. The camera control method based on Z-Wave according to claim 1, characterized in that, In step S108, the updated cooperative control topology and device capability set includes: Based on the changes in network link stability and the response status of each camera reflected in this interaction, the topology management unit within the system dynamically fine-tunes the stored collaborative control topology and updates the connection weights or hierarchical relationships between nodes. At the same time, if a difference is found between the actual working capability of a camera and the preset capability set during the interaction, the device management module will update its device capability set accordingly.

10. A camera control system based on Z-Wave, characterized in that, include: The terminal control module is used to initialize the Z-Wave wireless communication module, scan and authenticate cameras in the Z-Wave network, establish a secure connection with the cameras based on the Z-Wave protocol, and receive control commands or scene policies input by the user. The network monitoring and optimization module is used to monitor the current cellular network status in real time. When the network quality of the primary IoT card is lower than the threshold, it automatically triggers the Z-Wave control channel optimization algorithm to calculate the optimal communication path and power and generate optimized communication parameters. The secure communication and feedback module is used to send encrypted control signals to the target camera through the Z-Wave network according to the control commands and optimized communication parameters, start a multi-level response timeout monitoring mechanism, and receive execution results and environmental perception data from the camera. The adaptive retransmission and response module is used to initiate an adaptive retransmission mechanism when no feedback is received during the initial timeout period. It dynamically adjusts the retransmission interval and signal strength based on network quality and records the retransmission trajectory. The policy generation and management module is used to dynamically generate or update adaptive control policies based on feedback environmental awareness data and current network quality data, combined with historical control logs, and through the policy engine. The data storage and update module is used to encrypt and store signal data, environmental perception data, network quality data and generated adaptive control strategies during the control process, and update the collaborative control topology and device capability set.

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