An end-cloud cooperative cloud internet of things device intelligent linkage method and system

Abstract

The application relates to the technical field of Internet of Things device linkage, and discloses an end-cloud cooperative cloud Internet of Things device intelligent linkage method and system, which comprises the following steps: uploading device information data packets of Internet of Things devices to a cloud cooperative management platform through an edge node; generating linkage information of the device information data packets, distributing linkage messages to neighborhood Internet of Things devices based on device topological relations; generating linkage actions corresponding to the linkage messages by the neighborhood Internet of Things devices, performing conflict detection and priority quantification on the linkage actions; intelligently regulating and controlling the linkage actions of different neighborhood Internet of Things devices by taking an action conflict matrix and a priority vector as constraints, and issuing the intelligently regulated and controlled linkage actions to corresponding neighborhood Internet of Things devices. The application realizes end-cloud cooperative intelligent linkage of cross-Internet of Things devices and multi-device events, and completes event collection, linkage action generation, action conflict resolution and action regulation in a whole process, so that the intelligent cooperative control effect is achieved.

Description

Technical Field

[0001] This invention relates to the field of IoT device linkage, and more particularly to a cloud IoT device intelligent linkage method and system with end-to-cloud collaboration. Background Technology

[0002] With the rapid popularization of IoT technology and the large-scale access of low-power devices, a large number of heterogeneous IoT devices with diverse types and significantly different capabilities have emerged in scenarios such as smart homes, smart buildings, smart parks, and industrial IoT. During operation, these devices continuously generate multi-source heterogeneous data, including environmental parameters, status changes, alarm events, and action feedback. This data not only exhibits high-frequency updates, strong temporal sequence, and contextual dependencies, but also involves complex coupling relationships and linkage logic requirements between devices. In scenarios such as temperature and humidity control, air pollution control, security monitoring, energy management, and industrial production control, multiple devices need to coordinate actions across devices, regions, and even systems based on real-time environmental changes, event triggering conditions, spatial relationships, and device functional responsibilities to ensure environmental stability, smooth processes, safety controllability, and optimal energy consumption.

[0003] Meanwhile, in existing research, patent CN107864174B proposes a rule-based IoT device linkage method. This method obtains rule records from the IoT platform through a rule parsing module and uses the Drools rule engine for rule parsing, compilation, and matching. The system acquires the device operating status in real time to generate linkage facts, and the rule engine outputs device control actions based on the matching results. It then resolves and optimizes conflicts among multiple device control actions before finally sending them to the IoT devices for execution. This method can resolve conflicts when multiple operations are triggered simultaneously on the same device, effectively reducing redundant control actions and improving linkage execution efficiency. However, it still has the following technical challenges: this method mainly relies on cloud-based rule reasoning and execution, lacking the ability to quickly respond to real-time environmental changes on the device side, resulting in high linkage latency; it only resolves single-device action conflicts, lacking systematic handling of timing conflicts, functional conflicts, and topological conflicts in linkage actions between multiple devices.

[0004] To address this issue, this invention proposes an intelligent linkage method and system for cloud IoT devices with end-to-cloud collaboration. This ensures the consistency of timing and logic during the triggering of linkage actions, enhances the self-organization and autonomy capabilities among IoT devices, and effectively strengthens the robustness and security of the cloud IoT system in complex network environments. Summary of the Invention

[0005] This invention provides a cloud-edge collaborative intelligent linkage method and system for IoT devices. Addressing key technical challenges in traditional IoT device linkage, such as fragmented event collection, coarse linkage logic, and difficulty in eliminating device conflicts, it constructs an edge-cloud collaborative intelligent linkage mechanism. Step S1, by uniformly collecting device event data from IoT devices and encapsulating it into structured device information data packets, solves the problems of inconsistent data formats and difficulty in aligning event semantics among different types of IoT devices, enabling the cloud-based collaborative management platform to standardize and uniformly parse reported information. Step S2, relying on device topology generation and neighbor device identification mechanisms, realizes the positioning of linkage messages. The system distributes highly relevant actions to avoid resource waste and chain-like false triggers caused by excessively large linkage scope. Step S3 uses rule matching to quickly generate linkage actions, uses a conflict detection function to quantify conflicts between different linkage actions, and constructs an action priority vector based on topological relationships. This enables the cloud-based collaborative management platform to accurately identify potential conflicts and quantify the priority of each linkage action. Step S4 uses a conflict matrix and priority as constraints to intelligently control linkage actions. Through linkage action cluster division and execution time allocation mechanisms, it achieves the optimal timing arrangement of linkage actions for multiple devices, solving the problems of IoT device interference and low linkage action execution efficiency caused by concurrent linkage actions.

[0006] To achieve the above objectives, the present invention provides a cloud IoT device intelligent linkage method with end-to-cloud collaboration, comprising the following steps:

[0007] S1: Collect device event data from IoT devices, generate device information data packets, and upload the device information data packets to the cloud collaborative management platform through edge nodes;

[0008] S2: The cloud-based collaborative management platform generates linkage information for device information data packets and encapsulates messages, then distributes the linkage messages to neighboring IoT devices based on device topology relationships;

[0009] S3: Based on the received linkage message, the neighboring IoT devices generate the linkage action corresponding to the linkage message and upload it to the cloud collaborative management platform. The linkage actions of all neighboring IoT devices that receive the same linkage message are subjected to conflict detection and priority quantification to obtain the action conflict matrix and priority vector corresponding to the linkage message.

[0010] S4: The cloud-based collaborative management platform uses action conflict matrix and priority vector as constraints to intelligently control the linkage actions of IoT devices in different neighborhoods, and sends the intelligently controlled linkage actions to the corresponding neighborhood IoT devices. The neighborhood IoT devices then execute the received intelligently controlled linkage actions.

[0011] As a further improvement of the present invention:

[0012] Furthermore, device event data from IoT devices is collected to generate device information data packets, including:

[0013] IoT devices periodically collect their own device event data, which includes data collection timestamps, device status parameters, event information, and environmental data. Device status parameters are data reflecting the IoT device's own operating status, event information represents specific action events triggered by the IoT device, and environmental data describes the environmental information data of the IoT device's deployment location.

[0014] The device ID and device event information of the IoT device are encapsulated into a data packet as a device information data packet. The IoT device sends the device information data packet to the edge node through the local communication protocol.

[0015] Furthermore, device information data packets are uploaded to the cloud-based collaborative management platform via edge nodes, including:

[0016] Edge nodes periodically aggregate and package the received device information data packets to obtain batch data packets containing device information data packets transmitted by multiple IoT devices. The aggregation and packaging process includes data integrity verification, caching, and encryption encapsulation.

[0017] Specifically, the data integrity check includes checking whether the data acquisition timestamp is within the current acquisition period of the edge node, checking whether the fields in the device number and device event information are complete, aggregating and compressing the device information data packets that pass the check, and encrypting the compression result using a lightweight AES encryption algorithm as a batch data packet;

[0018] Edge nodes use a transmission protocol to transmit batch data packets to the cloud-based collaborative management platform.

[0019] Furthermore, step S2 includes:

[0020] The cloud-based collaborative management platform monitors the packet loss rate of the communication link between the edge nodes and the cloud-based collaborative management platform in real time within a unit of time, which serves as the communication reliability of the device information data packets transmitted by the edge nodes.

[0021] The cloud-based collaborative management platform generates linkage information for device information data packets. These linkage messages include the device number, data acquisition timestamp, linkage triggering conditions, trust level, environmental data from the device information data packet, and event information. The generation process for these linkage messages is as follows:

[0022] S201: Extract the device number, data acquisition timestamp, event information, and environmental data from the device information data packet;

[0023] S202: Based on the communication reliability of device information data packets and the historical device information data packets sent by IoT devices with the same device number within a preset time range, the device stability score, data integrity score, communication reliability score, and environmental noise interference coefficient of the device information data packets are calculated in sequence as trust score indicators. The trust score indicators are then weighted and calculated as the trust level of the device information data packets.

[0024] Specifically, the formula for calculating the trust level of the device information data packet is as follows:

[0025] ;

[0026] ;

[0027] ;

[0028] ;

[0029] ;

[0030] in, This indicates the trust level of the device information data packet. This indicates the equipment stability score. Indicates the data integrity score. Indicates the communication reliability score. Indicates the environmental noise interference coefficient. Indicates the number of historical device information data packets. This indicates the number of data packets in the historical device information data packets that contain device malfunction events. This indicates the number of historical device information data packets expected to be uploaded by IoT devices within a preset time range. This indicates the reliability of communication for device information data packets. Indicates the communication reliability control coefficient. This represents an exponential function with the natural constant as its base. This represents the standard deviation of signal strength in the device status parameters within the historical device information data packet. This represents the average signal strength among the device status parameters in the historical device information data packet. Indicates the noise control coefficient;

[0031] All represent weighting coefficients, where ;

[0032] S203: Extract device status parameters and event information from the device information data packet, and generate linkage triggering condition information based on the linkage triggering logic;

[0033] If the linkage trigger condition information is True, it means that the event information of the IoT device in the device information data packet will cause the operating status of the neighboring IoT devices to change, and the linkage message of the device information data packet will be encapsulated.

[0034] If the linkage trigger condition message is False, then skip the linkage message in the current device information data packet;

[0035] The cloud-based collaborative management platform distributes linkage messages to neighboring IoT devices based on device topology relationships.

[0036] Furthermore, the process for constructing the device topology includes:

[0037] Obtain the deployment location, device function set, number of times the device information data packets sent within the sliding time window are in the same batch of data packets, and number of times the device information data packets sent within the sliding time window generate linkage messages;

[0038] Calculate the device topology relationship between any two IoT devices:

[0039] ;

[0040] in, Indicates the first Device topology relationships between IoT devices. Indicates the first The distance between the deployment locations of each IoT device Indicates the distance control coefficient. This represents an exponential function with the natural constant as its base. Indicates the first A collection of device functions for an Internet of Things (IoT) device. Indicates the first A collection of device functions for an Internet of Things (IoT) device. This represents the intersection operator. This represents the union operator. This indicates the number of device functions in the set. Indicates the first The number of times a device information data packet sent by an IoT device falls within the same batch of data packets within a sliding time window. Indicates the first The number of times each IoT device generates a linkage message within a sliding time window for sending device information data packets. All represent the device topology control coefficient;

[0041] , Indicates the total number of IoT devices;

[0042] If the device topology If the value exceeds the preset topological relationship threshold, then the first... Each IoT device is a neighboring IoT device.

[0043] Furthermore, based on the received linkage message, the neighborhood IoT devices generate the corresponding linkage action, including:

[0044] The neighborhood IoT devices receive the linkage message and extract the linkage trigger condition information, trust level, environmental data and event information from the device information data packet. They verify whether the linkage trigger condition information is True. If the linkage trigger condition information is True, they concatenate the trust level, environmental data and event information from the device information data packet into an event state vector. They then generate the corresponding linkage action using rule matching and package the generated linkage action, the current timestamp and the device number of the neighborhood IoT device into a linkage data packet. The linkage data packet is then uploaded to the cloud collaborative management platform in real time through the edge node.

[0045] Furthermore, conflict detection and priority quantification are performed on the linkage actions of all neighboring IoT devices receiving the same linkage message to obtain the action conflict matrix and priority vector corresponding to the linkage message, including:

[0046] The cloud-based collaborative management platform extracts the linkage actions of all neighboring IoT devices that receive the same linkage message, uses a conflict detection function to perform conflict detection on the linkage actions of any two different neighboring IoT devices, and generates the action priority of the linkage actions of the neighboring IoT devices based on the conflict detection results and the device topology relationship between the neighboring IoT devices and the IoT devices associated with the linkage message.

[0047] The conflict detection results of the linkage actions of any two different neighboring IoT devices are formed into a matrix as the conflict matrix corresponding to the linkage message. The linkage actions of the neighboring IoT devices are sorted in descending order of action priority as the priority vector corresponding to the linkage message.

[0048] Furthermore, the cloud-based collaborative management platform uses action conflict matrices and priority vectors as constraints to intelligently regulate the coordinated actions of IoT devices in different neighborhoods, including:

[0049] The cloud-based collaborative management platform extracts linkage actions sequentially based on the priority vector corresponding to the linkage action sorting order. During the extraction process, it detects in real time whether the conflict detection result of any two groups of linkage actions extracted so far is higher than the allowable conflict threshold. If it is not higher than the allowable conflict threshold, extraction continues. If it is higher than the allowable conflict threshold, extraction of the current linkage action is stopped, and the currently extracted linkage actions are merged into linkage action clusters. The execution time of the linkage action cluster is allocated and used as the execution time of all linkage actions within the linkage action cluster.

[0050] After allocating execution time, the linked actions that are not in the linked action cluster are extracted based on the sorting order of the linked actions, until all linked actions have been merged into the linked action cluster.

[0051] The linkage action at the execution time will be added as the linkage action after intelligent control.

[0052] The present invention also proposes an edge-cloud collaborative intelligent linkage system for cloud IoT devices. The edge-cloud collaborative intelligent linkage system for cloud IoT devices includes a data transmission device, a computing unit, and a cloud collaborative management platform to realize the edge-cloud collaborative intelligent linkage method for cloud IoT devices as described above.

[0053] Compared with existing technologies, this invention proposes a cloud IoT device intelligent linkage method and system with edge-cloud collaboration, which has the following beneficial effects:

[0054] First, this invention introduces a distance attenuation factor based on an exponential function and a distance control coefficient. This allows the device topology relationships to decrease exponentially with increasing device distance, accurately reflecting the constraints of physical deployment locations on the topology relationships between IoT devices. It ensures automatic downweighting of devices that are far apart and have no direct interaction, improving the physical rationality of the topology; and it introduces functional overlap. Quantitatively representing the similarity of two IoT devices in terms of business capabilities ensures that the device topology relationship between IoT devices with similar functions or collaborative control capabilities is enhanced, making the device topology relationship more consistent with actual business scenarios. For example, temperature and humidity sensors and air conditioners have a high functional correlation and will form a more stable topology connection; while the frequency of co-occurrence of linkage information This reflects the consistency and collaborative potential of IoT devices in time-series behavior from a data mining perspective. It can automatically uncover implicitly related devices that are difficult to identify with traditional solutions, thereby improving the dynamic adaptability of intelligent linkage among IoT devices.

[0055] Meanwhile, this invention introduces a priority vector-based sequential extraction mechanism and monitors the conflict detection between any two sets of linked actions in real time during the extraction process. This enables the cloud-based collaborative management platform to achieve dynamic conflict suppression and adaptive grouping scheduling of multi-device linked actions. Compared to traditional solutions that only execute static linkage strategies at the edge or device side, this invention integrates global information of the conflict matrix and device topology in the cloud-based collaborative management platform. This gives the action extraction and grouping process global optimality across devices and regions. By immediately stopping extraction and forming a cluster of linked actions when the conflict exceeds a threshold, it ensures that there are no high-risk conflicts in the actions within the cluster at either the physical or logical layer, thereby avoiding mutual interference problems caused by simultaneous execution by multiple devices. Attached Figure Description

[0056] Figure 1 This is a flowchart illustrating an intelligent linkage method for cloud IoT devices with edge-cloud collaboration, provided in an embodiment of the present invention.

[0057] Figure 2 A flowchart of a linkage action conflict detection method is provided in one embodiment of the present invention;

[0058] Figure 3 This is a schematic diagram of a cloud IoT device intelligent linkage system structure provided in an embodiment of the present invention.

[0059] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0060] The realization of the objectives, functional characteristics, and advantages of this invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0061] This invention provides a method and system for intelligent linkage of cloud IoT devices with edge-cloud collaboration. The executing entity of this method includes, but is not limited to, at least one electronic device that can be configured to execute the method provided in this invention, such as a server or a terminal. In other words, this method can be executed by software or hardware installed on a terminal device or a server device, and the software may be a blockchain platform. The server includes, but is not limited to, a single server, a server cluster, a cloud server, or a cloud server cluster.

[0062] Reference Figure 1 as well as Figure 2 As shown, Embodiment 1 of the present invention is as follows:

[0063] A method for intelligent linkage of cloud IoT devices with edge-cloud collaboration, comprising:

[0064] S1: Collect device event data from IoT devices, generate device information data packets, and upload the device information data packets to the cloud collaborative management platform through edge nodes.

[0065] Collect device event data from IoT devices and generate device information data packets, including:

[0066] IoT devices periodically collect their own device event data, which includes data collection timestamps, device status parameters, event information, and environmental data. Device status parameters are data reflecting the IoT device's own operating status, event information represents specific action events triggered by the IoT device, and environmental data describes the environmental information data of the IoT device's deployment location.

[0067] Specifically, the IoT devices, edge nodes, and cloud collaborative management platform together constitute the IoT device cloud collaborative architecture, which adopts a three-level collaborative structure of device layer (end) - edge layer (edge) - cloud layer (cloud).

[0068] The device layer consists of various types of IoT devices, including environmental monitoring devices (temperature sensors, gas sensors), actuators (smart air conditioners, lighting controllers, valve control units), and video acquisition devices (cameras, image recognition modules), etc. It sends device information data packets to edge nodes through local communication protocols (such as MQTT, CoAP, Modbus), and can also receive linkage commands issued by the cloud collaborative management platform.

[0069] Edge nodes are deployed at the boundaries of local area networks or at gateways, and are responsible for data preprocessing and local collaboration. The cloud-based collaborative management platform is the central control layer of the entire architecture, and is responsible for global analysis, linkage strategy generation and security control.

[0070] It should be noted that the IoT device uses built-in lightweight detection logic to generate event information when the device status parameters exceed the limit or environmental data changes.

[0071] The device ID and device event information of the IoT device are encapsulated into a data packet as a device information data packet. The IoT device sends the device information data packet to the edge node through the local communication protocol.

[0072] As an embodiment of the present invention, taking a temperature sensing device (device number: SENSOR_TEMP_01) as an example, the device information data packet is in the following form: Device number: SENSOR_TEMP_01; Data acquisition timestamp: 1731375629 (corresponding to 9:40:29 AM on November 12, 2024); Device status parameters: Voltage 3.28 volts, battery charge 91%, signal strength -62 dBm (decibels per milliwatt, representing the strength of signal power relative to 1 milliwatt), temperature detection result 32.5℃; Event information: Temperature exceeds the preset upper limit threshold, current temperature is 32.5℃; Environmental data: Humidity 74.1%, light intensity 520 lux, installation location is "3rd floor, 305 conference room";

[0073] As another embodiment of the present invention, taking a smart door lock (device number: DOORLOCK_05) as an example, the device information data packet is in the following form: Device number: DOORLOCK_05; Data acquisition timestamp: 1724579783 (corresponding to 17:56:23 on August 25, 2024); Device status parameters: Current status is "unlocked", power supply voltage is 4.1 volts, signal strength is -55dBm; Event information: Manual key unlocking event detected; Environmental data: Current ambient temperature is 32.3℃, user ID is U0458, and installation location is "3rd floor, 305 conference room".

[0074] Upload device information data packets to the cloud-based collaborative management platform via edge nodes, including:

[0075] Edge nodes periodically aggregate and package the received device information data packets to obtain batch data packets containing device information data packets transmitted by multiple IoT devices. The aggregation and packaging process includes data integrity verification, caching, and encryption encapsulation.

[0076] Specifically, the data integrity check includes checking whether the data acquisition timestamp is within the current acquisition period of the edge node, checking whether the fields in the device number and device event information are complete, aggregating and compressing the device information data packets that pass the check, and encrypting the compression result using a lightweight AES encryption algorithm as a batch data packet;

[0077] It should be noted that the periodic collection of device event data by IoT devices indicates that IoT devices set their own collection cycle according to their type and application scenario. The IoT device collection cycle is the time interval between two consecutive data collections. The periodic aggregation and packaging of received device information data packets by edge nodes indicates that edge nodes aggregate and package historical device information data packets received at specific times to alleviate cloud communication pressure. Optionally, the time interval between adjacent specific times can be set to 5 minutes. Taking 17:05 as an example, the edge node aggregates and packages the device information data packets sent to the edge node from 17:05 to 17:05 and transmits the batch data packets to the cloud collaborative management platform at 17:05. It is necessary to check whether the data collection timestamp is within the current collection cycle of the edge node (17:00 to 17:05), or when the edge node detects a specific event (such as a fire sensor issuing a fire warning), it immediately aggregates and packages the currently received device information data packets and transmits them to the cloud collaborative management platform.

[0078] Edge nodes use transport protocols (such as MQTT or HTTPS channels) to transmit batch data packets to the cloud-based collaborative management platform.

[0079] Specifically, the cloud-based collaborative management platform decrypts and parses the batch data packets, splits them into multiple device information data packets, and establishes an index for the device information data packets according to the device number and data collection timestamp. Based on the index, the device information data packets are stored in the database of the cloud-based collaborative management platform.

[0080] S2: The cloud-based collaborative management platform generates linkage information for device information data packets and encapsulates messages. Based on the device topology, it distributes linkage messages to neighboring IoT devices.

[0081] Specifically, the cloud-based collaborative management platform monitors the packet loss rate of the communication link between the edge node and the cloud-based collaborative management platform in real time within a unit of time, which serves as the communication reliability of the device information data packets transmitted by the edge node.

[0082] The cloud-based collaborative management platform generates linkage information for device information data packets. These linkage messages include the device number, data acquisition timestamp, linkage triggering conditions, trust level, environmental data from the device information data packet, and event information. The generation process for these linkage messages is as follows:

[0083] S201: Extract the device number, data acquisition timestamp, event information, and environmental data from the device information data packet;

[0084] S202: Based on the communication reliability of device information data packets and the historical device information data packets sent by IoT devices with the same device number within a preset time range, the device stability score, data integrity score, communication reliability score, and environmental noise interference coefficient of the device information data packets are calculated in sequence as trust score indicators. The trust score indicators are weighted and calculated as the trust level of the device information data packets.

[0085] Optionally, the preset time range can be set to a range of 12 hours from the current time;

[0086] Specifically, the formula for calculating the trust level of the device information data packet is as follows:

[0087] ;

[0088] ;

[0089] ;

[0090] ;

[0091] ;

[0092] in, This indicates the trust level of the device information data packet. This indicates the equipment stability score. Indicates the data integrity score. Indicates the communication reliability score. Indicates the environmental noise interference coefficient. Indicates the number of historical device information data packets. This indicates the number of data packets in the historical device information data packets where device abnormal events (such as power outages, sensor malfunctions, or disconnections) occurred. This indicates the number of historical device information data packets expected to be uploaded by IoT devices within a preset time range. This indicates the reliability of communication for device information data packets. Indicates the communication reliability control coefficient (e.g., setting) (1.5) This represents an exponential function with the natural constant as its base. This represents the standard deviation of signal strength in the device status parameters within the historical device information data packet. This represents the average signal strength among the device status parameters in the historical device information data packet. Indicates the noise control factor, set It is 0.1;

[0093] Both represent weighting coefficients, where ; Optionally, the weighting coefficients can be set according to the device type of the Internet of Things device associated with the device information data packet. For example, for an Internet of Things device with a relatively high frequency of uploading device information data packets, larger weighting coefficients for device stability scores and device integrity scores can be set, or fixed weighting coefficients can be set based on experience. Optional setting are 0.3, 0.3, 0.2, and 0.2 respectively in sequence;

[0094] It should be noted that through the establishment of a multi-dimensional trust quantification model, the present invention realizes the real-time credibility assessment of the operating state of Internet of Things devices. Compared with the traditional static assessment method based on a single index (such as communication success rate), the present invention comprehensively models multiple factors such as device stability, data integrity, communication reliability, and environmental noise disturbance to form a dynamically adjustable trust level calculation formula. This trust level calculation formula can quickly identify potential risks in scenarios of sudden device state changes, data loss, or communication delays, quantify the credibility of current event information, and when the trust level is relatively low, adopt manual processing methods to pre-process Internet of Things devices in advance to check whether there are abnormalities in the operating conditions of Internet of Things devices, thereby improving the overall robustness of the cloud collaboration architecture of Internet of Things devices. Specifically, the trust level calculation formula is designed through exponential functions and ratio normalization, and the quantification indicators have strong scale independence, which is convenient for unified evaluation and comparison of different types of devices, so as to achieve cross-device and cross-network credibility classification management and enhance the stability and security credibility of the intelligent linkage of Internet of Things devices.

[0095] S203: Extract the device state parameters and event information in the device information data packet, and generate linkage trigger condition information based on the linkage trigger logic;

[0096] As an embodiment of the present invention, the generation method of the linkage trigger condition information is as follows:

[0097] ;

[0098] Among them, represents the linkage trigger condition information, , represents the logical AND operator, represents the logical OR operator, represents a condition judgment function. If the expression in the condition judgment function conforms to the performance of the device state parameters and event information, the output result of the condition judgment function is Otherwise, the output result of the condition judgment function is , represents the event information, represents the set of event information that can trigger linkages. Specifically, if the event information Belongs to the event information collection ,but The output result is , This indicates that it is necessary to avoid exceeding the preset maximum control threshold. The parameter values ​​in the device status parameters. This indicates that it is necessary to avoid falling below the preset minimum control threshold. The parameter value in the device status parameters, if or If the output result is True, then Set to True, otherwise for ;

[0099] As a supplementary embodiment to the above-described method for generating linkage triggering condition information, for an air quality monitoring device, if the device status parameters are: temperature 33 degrees Celsius, humidity 28%, carbon dioxide concentration 1800 ppm (parts per million, representing gas concentration), PM2.5 concentration 51 micrograms per cubic meter, and the event information is: carbon dioxide concentration too high, the set of event information that can trigger linkage includes: CO2 concentration too high, PM2.5 exceeding the standard; and the maximum control threshold for temperature is 30 degrees Celsius, and the minimum control threshold for humidity is 27%, then... The corresponding output results are as follows ,but The corresponding output results are as follows Then the linkage triggering condition information of the equipment information data packet of the air quality detection equipment is True;

[0100] If the linkage trigger condition information is True, it means that the event information of the IoT device in the device information data packet will cause the operating status of the neighboring IoT devices to change, and the linkage message of the device information data packet will be encapsulated.

[0101] If the linkage trigger condition message is False, then skip the linkage message in the current device information data packet;

[0102] The cloud-based collaborative management platform distributes linkage messages to neighboring IoT devices based on device topology relationships.

[0103] The process of constructing the device topology relationship includes:

[0104] Obtain the deployment location, device function set, number of times the device information data packets sent within the sliding time window are in the same batch of data packets, and number of times the device information data packets sent within the sliding time window generate linkage messages;

[0105] Optionally, the sliding time window can be set to the most recent 7 days;

[0106] Calculate the device topology relationship between any two IoT devices:

[0107] ;

[0108] in, Indicates the first Device topology relationships between IoT devices. Indicates the first The distance between the deployment locations of each IoT device Indicates the distance control coefficient, set It is 10 meters. This represents an exponential function with the natural constant as its base. Indicates the first A collection of device functions for an Internet of Things (IoT) device. Indicates the first A collection of device functions for an Internet of Things (IoT) device. This represents the intersection operator. This represents the union operator. This indicates the number of device functions in the set. Indicates the first The number of times a device information data packet sent by an IoT device falls within the same batch of data packets within a sliding time window. Indicates the first The number of times each IoT device generates a linkage message within a sliding time window for sending device information data packets. All represent the device topology control coefficient, set They are 0.4 and 0.6 respectively;

[0109]

[0110] , Indicates the total number of IoT devices;

[0111] If the device topology If the value exceeds a preset topological relationship threshold (e.g., 0.5), then the first... Each IoT device is a neighboring IoT device.

[0112] S3: Based on the received linkage message, the neighboring IoT devices generate the linkage action corresponding to the linkage message and upload it to the cloud collaborative management platform. The platform performs conflict detection and priority quantification on the linkage actions of all neighboring IoT devices that receive the same linkage message, and obtains the action conflict matrix and priority vector corresponding to the linkage message.

[0113] Based on the received linkage messages, the neighborhood IoT devices generate corresponding linkage actions, including:

[0114] The neighborhood IoT devices receive the linkage message and extract the linkage trigger condition information, trust level, environmental data and event information from the device information data packet. They verify whether the linkage trigger condition information is True. If the linkage trigger condition information is True, they concatenate the trust level, environmental data and event information from the device information data packet into an event state vector. They then generate the corresponding linkage action using rule matching and package the generated linkage action, the current timestamp and the device number of the neighborhood IoT device into a linkage data packet. The linkage data packet is then uploaded to the cloud collaborative management platform in real time through the edge node.

[0115] It should be noted that this invention achieves rapid perception and local decision generation of environmental changes and event information on the IoT device side by completing the verification of linkage trigger conditions and the construction of event state vectors at the neighboring IoT device side. This gives the linkage actions advantages such as low latency and scene adaptability. Furthermore, this invention uses a rule-matching method to generate linkage actions, enabling highly reliable and interpretable action decisions without the need for complex inference models. This ensures that neighboring IoT devices can stably execute linkage logic and generate linkage actions even under resource-constrained conditions.

[0116] Specifically, the rule matching process is as follows:

[0117] The trust level in the event state vector, the environmental data in the device information data packet, and the event information are sequentially matched using rules to generate a coordinated action. This coordinated action includes the action duration, action intensity, and action form. The trust level is used to generate the action duration; a higher trust level results in a shorter action duration, prompting neighboring IoT devices to complete the coordinated action more quickly. The action intensity is generated based on the environmental data, representing the improvement in the operational status of neighboring IoT devices after the coordinated action is taken. The action form is generated based on the event information matching.

[0118] Specifically, the formula for generating the duration of the action is:

[0119] ;

[0120] in, Indicates the duration of the action. Set the minimum preset time to complete the action (e.g., 2 seconds). This indicates the preset maximum duration for completing the action (e.g., 5 seconds). Indicates the trust level. This represents the sensitivity coefficient, which can be set to 1 or 1.3;

[0121] Optionally, a rule matching method based on a membership function is adopted to match environmental data with multiple action intensities, where the action intensities may include low intensity, medium intensity, and high intensity;

[0122] As an embodiment of the present invention, for an air quality detection device (including a sensor, a fan, and a purifier), if the event information in the received linkage message is "high CO2 concentration" or "exceeded PM2.5 standard", the action form is: controlling the ventilation / purification equipment gear and whether to open the window. For the action intensities: low intensity, medium intensity, and high intensity, where low intensity corresponds to 30% gear of the fan and low gear of the purifier, medium intensity corresponds to 60% gear of the fan and medium gear of the purifier, and high intensity corresponds to 100% of the fan, high gear of the purifier, and forced window opening.

[0123] Perform conflict detection and priority quantification on the linkage actions of all neighborhood Internet of Things devices receiving the same linkage message, and obtain an action conflict matrix and a priority vector corresponding to the linkage message, including:

[0124] The cloud collaborative management platform extracts the linkage actions of all neighborhood Internet of Things devices receiving the same linkage message, uses a conflict detection function to perform conflict detection on the linkage actions of any two different neighborhood Internet of Things devices, and generates an action priority for the linkage actions of the neighborhood Internet of Things devices based on the conflict detection results and the device topology relationship between the neighborhood Internet of Things devices and the Internet of Things devices associated with the linkage message;

[0125] Specifically, referring to the Figure 2 shown linkage action conflict detection flowchart, the process of performing conflict detection on the linkage actions of any two different neighborhood Internet of Things devices is as follows:

[0126] S301: Convert the action form in the linkage action into a category label, such as opening the window, closing the window, increasing the wind, decreasing the wind, turning on the purification, turning off the purification, etc., and define an opposition function , where the opposition function takes the category labels of two action forms as inputs. If the semantics of the category labels are opposite (such as opening the window and closing the window), the opposition function outputs 1, otherwise the opposition function outputs 0;

[0127] S302: Quantitatively represent the action intensity in the linkage action. Taking low intensity, medium intensity, and high intensity as examples, the corresponding quantitative representation results are 1, 2, and 3 in sequence;

[0128] S303: Use the conflict detection function to perform conflict detection on the linkage actions of any two different neighborhood Internet of Things devices, where the expression of the conflict detection function is:

[0129] ;

[0130] in, This indicates the coordinated actions of the c-th and v-th neighboring IoT devices under the same coordinated message. The conflict detection results, among which For the coordinated action of the c-th neighboring IoT device, For the coordinated action of the v-th neighboring IoT device, These represent the linked actions in sequence. Category tags for action forms, These represent the linked actions in sequence. The duration of the completed action in the middle. Indicates the standard value for duration, set It lasts for 10 seconds. These represent the linked actions in sequence. The quantitative representation of the intensity of the action in the text; N represents the number of linked actions of neighboring IoT devices under the same linked message;

[0131] As an embodiment of the present invention, based on the conflict detection results and the device topology relationship between neighboring IoT devices and IoT devices associated with linkage messages, the formula for generating the action priority of linkage actions of neighboring IoT devices is as follows:

[0132] As an embodiment of the present invention, based on the conflict detection results and the device topology relationship between neighboring IoT devices and IoT devices associated with linkage messages, the formula for generating the action priority of linkage actions of neighboring IoT devices is as follows:

[0133] ;

[0134] in, This indicates the action priority of the linkage action of the c-th neighboring IoT device. This represents the device topology relationship between the c-th neighboring IoT device and the IoT device associated with the linkage message. This represents the average conflict detection result between the c-th neighboring IoT device and the linkage actions of different neighboring IoT devices under the same linkage message. Indicates selection The maximum value between;

[0135] The conflict detection results of the linkage actions of any two different neighboring IoT devices are formed into a matrix as the conflict matrix corresponding to the linkage message. The linkage actions of the neighboring IoT devices are sorted in descending order of action priority as the priority vector corresponding to the linkage message.

[0136] It should be noted that this invention introduces a contrastive function to extract the semantic reverse relationship of action forms, transforming the traditional action conflict identification process that relies on expert experience into a structured judgment, significantly improving the objectivity and universality of conflict detection. Based on the dual difference terms of action completion time and action intensity, the conflict detection results can simultaneously reflect two types of contradictions: action response speed conflict and action effect conflict. The obtained conflict detection results are continuous and comparable, suitable for automatic calculation in a large-scale IoT device environment. The finally generated conflict matrix and priority vector are clearly structured, facilitating parallel scheduling and policy adjudication by the cloud collaboration platform, achieving conflict minimization and collaborative action optimization in multi-device linkage scenarios, and improving the overall reliability, response efficiency, and security of end-to-cloud collaborative linkage.

[0137] S4: The cloud-based collaborative management platform uses action conflict matrix and priority vector as constraints to intelligently control the linkage actions of IoT devices in different neighborhoods, and sends the intelligently controlled linkage actions to the corresponding neighborhood IoT devices. The neighborhood IoT devices then execute the received intelligently controlled linkage actions.

[0138] Specifically, the cloud-based collaborative management platform uses an action conflict matrix and priority vector as constraints to intelligently control the coordinated actions of IoT devices in different neighborhoods, including:

[0139] The cloud-based collaborative management platform extracts linkage actions sequentially based on the priority vector corresponding to the linkage action sorting order. During the extraction process, it detects in real time whether the conflict detection result of any two groups of linkage actions extracted so far is higher than the allowable conflict threshold (e.g., 0.6). If it is not higher than the allowable conflict threshold, extraction continues. If it is higher than the allowable conflict threshold, extraction of the current linkage action is stopped, and the currently extracted linkage actions are merged into linkage action clusters. The execution time of the linkage action cluster is allocated and used as the execution time of all linkage actions within the linkage action cluster.

[0140] After allocating execution time, the linked actions that are not in the linked action cluster are extracted based on the sorting order of the linked actions until all linked actions have been merged into the linked action cluster. The time interval between the execution times of adjacent merged linked action clusters is 1 second.

[0141] The linkage action at the execution time will be added as the linkage action after intelligent control.

[0142] It should be noted that by allocating the execution time of the linkage action clusters and setting the minimum time interval between clusters, the cloud-based collaborative management platform can coordinate the execution rhythm of large-scale IoT devices at the second-level scheduling granularity. It can prioritize the parallel execution of action clusters with the least conflict, while naturally dispersing actions with higher potential conflicts to subsequent times, effectively reducing the system risks brought about by the linkage execution of device behaviors. Example

[0143] For reference Figure 3 The schematic diagram shown illustrates a cloud IoT device intelligent linkage system structure with end-to-cloud collaboration, including a data transmission device 101, a computing unit 102, and a cloud collaborative management platform 103, to realize a cloud IoT device intelligent linkage method with end-to-cloud collaboration as described in Embodiment 1:

[0144] The data transmission device 101 is used to collect device event data of IoT devices, generate device information data packets, and upload the device information data packets to the cloud collaborative management platform through edge nodes. The cloud collaborative management platform distributes linkage messages to neighboring IoT devices and sends the linkage actions after intelligent control to the corresponding neighboring IoT devices based on the device topology.

[0145] The computing unit 102 is located on the cloud-based collaborative management platform. It is used to calculate the topological relationship of the devices, perform conflict detection and priority quantification on the linkage actions of all neighboring IoT devices that receive the same linkage message, and intelligently control the linkage actions of different neighboring IoT devices by using the action conflict matrix and priority vector as constraints.

[0146] The cloud-based collaborative management platform 103 is used to generate linkage information for device information data packets and to encapsulate messages.

[0147] It should be understood that the embodiments described are for illustrative purposes only and are not limited to this structure in terms of the scope of the patent invention.

[0148] It should be noted that the sequence numbers of the above embodiments of the present invention are merely for descriptive purposes and do not represent the superiority or inferiority of the embodiments. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, apparatus, article, or method that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, apparatus, article, or method. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, apparatus, article, or method that includes that element.

[0149] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0150] The above are merely preferred embodiments of the present invention and do not limit the scope of the patent. Any equivalent structural or procedural transformations made based on the description and drawings of the present invention, or direct or indirect applications in other related technical fields, are similarly included within the scope of patent protection of the present invention.

Claims

1. A method for intelligent linkage of cloud IoT devices with edge-cloud collaboration, characterized in that, The method includes: S1: Collect device event data from IoT devices, generate device information data packets, and upload the device information data packets to the cloud collaborative management platform through edge nodes; S2: The cloud-based collaborative management platform generates linkage information for device information data packets and encapsulates messages, then distributes the linkage messages to neighboring IoT devices based on device topology relationships; S3: Based on the received linkage message, the neighboring IoT devices generate the linkage action corresponding to the linkage message and upload it to the cloud collaborative management platform. The linkage actions of all neighboring IoT devices that receive the same linkage message are subjected to conflict detection and priority quantification to obtain the action conflict matrix and priority vector corresponding to the linkage message. S4: The cloud-based collaborative management platform uses action conflict matrix and priority vector as constraints to intelligently control the linkage actions of IoT devices in different neighborhoods, and sends the intelligently controlled linkage actions to the corresponding neighborhood IoT devices. The neighborhood IoT devices then execute the received intelligently controlled linkage actions. Based on the received linkage messages, the neighborhood IoT devices generate corresponding linkage actions, including: The neighborhood IoT device receives the linkage message and extracts the linkage trigger condition information, trust level, environmental data and event information from the device information data packet. It verifies whether the linkage trigger condition information is True. If the linkage trigger condition information is True, it concatenates the trust level, environmental data and event information from the device information data packet into an event state vector, generates the corresponding linkage action using rule matching, and packages the generated linkage action, the current timestamp and the device number of the neighborhood IoT device into a linkage data packet. The linkage data packet is then uploaded to the cloud collaborative management platform in real time through the edge node. Conflict detection and priority quantification are performed on the coordinated actions of all neighboring IoT devices that receive the same coordinated message, resulting in an action conflict matrix and priority vector corresponding to the coordinated message, including: The cloud-based collaborative management platform extracts the linkage actions of all neighboring IoT devices that receive the same linkage message, uses a conflict detection function to perform conflict detection on the linkage actions of any two different neighboring IoT devices, and generates the action priority of the linkage actions of the neighboring IoT devices based on the conflict detection results and the device topology relationship between the neighboring IoT devices and the IoT devices associated with the linkage message. The conflict detection results of the linkage actions of any two different neighboring IoT devices are formed into a matrix as the conflict matrix corresponding to the linkage message. The linkage actions of the neighboring IoT devices are sorted in descending order of action priority as the priority vector corresponding to the linkage message. The cloud-based collaborative management platform uses action conflict matrices and priority vectors as constraints to intelligently control the coordinated actions of IoT devices in different neighborhoods, including: The cloud-based collaborative management platform extracts linkage actions sequentially based on the priority vector corresponding to the linkage action sorting order. During the extraction process, it detects in real time whether the conflict detection result of any two groups of linkage actions extracted so far is higher than the allowable conflict threshold. If it is not higher than the allowable conflict threshold, extraction continues. If it is higher than the allowable conflict threshold, extraction of the current linkage action is stopped, and the currently extracted linkage actions are merged into linkage action clusters. The execution time of the linkage action cluster is allocated and used as the execution time of all linkage actions within the linkage action cluster. After allocating execution time, the linked actions that are not in the linked action cluster are extracted based on the sorting order of the linked actions, until all linked actions have been merged into the linked action cluster. The linkage action at the execution time will be added as the linkage action after intelligent control.

2. The intelligent linkage method for cloud IoT devices with edge-cloud collaboration as described in claim 1, characterized in that, Collect device event data from IoT devices and generate device information data packets, including: IoT devices periodically collect their own device event data, which includes data collection timestamps, device status parameters, event information, and environmental data. Device status parameters are data reflecting the IoT device's own operating status, event information represents specific action events triggered by the IoT device, and environmental data describes the environmental information data of the IoT device's deployment location. The device ID and device event information of the IoT device are encapsulated into a data packet as a device information data packet. The IoT device sends the device information data packet to the edge node through the local communication protocol.

3. The intelligent linkage method for cloud IoT devices with edge-cloud collaboration as described in claim 2, characterized in that, Upload device information data packets to the cloud-based collaborative management platform via edge nodes, including: Edge nodes periodically aggregate and package the received device information data packets to obtain batch data packets containing device information data packets transmitted by multiple IoT devices. The aggregation and packaging process includes data integrity verification, caching, and encryption encapsulation. Edge nodes use a transmission protocol to transmit batch data packets to the cloud-based collaborative management platform.

4. The intelligent linkage method for cloud IoT devices with edge-cloud collaboration as described in claim 1, characterized in that, Step S2 includes: The cloud-based collaborative management platform monitors the packet loss rate of the communication link between the edge nodes and the cloud-based collaborative management platform in real time within a unit of time, which serves as the communication reliability of the device information data packets transmitted by the edge nodes. The cloud-based collaborative management platform generates linkage information for device information data packets. These linkage messages include the device number, data acquisition timestamp, linkage triggering conditions, trust level, environmental data from the device information data packet, and event information. The generation process for these linkage messages is as follows: S201: Extract the device number, data acquisition timestamp, event information, and environmental data from the device information data packet; S202: Based on the communication reliability of device information data packets and the historical device information data packets sent by IoT devices with the same device number within a preset time range, the device stability score, data integrity score, communication reliability score, and environmental noise interference coefficient of the device information data packets are calculated in sequence as trust score indicators. The trust score indicators are then weighted and calculated as the trust level of the device information data packets. S203: Extract device status parameters and event information from the device information data packet, and generate linkage triggering condition information based on the linkage triggering logic; If the linkage trigger condition information is True, it means that the event information of the IoT device in the device information data packet will cause the operating status of the neighboring IoT devices to change, and the linkage message of the device information data packet will be encapsulated. If the linkage trigger condition message is False, then skip the linkage message in the current device information data packet; The cloud-based collaborative management platform distributes linkage messages to neighboring IoT devices based on device topology relationships.

5. The intelligent linkage method for cloud IoT devices with edge-cloud collaboration as described in claim 4, characterized in that, The process of constructing the device topology includes: Obtain the deployment location, device function set, number of times the device information data packets sent within the sliding time window are in the same batch of data packets, and number of times the device information data packets sent within the sliding time window generate linkage messages; Calculate the device topology relationship between any two IoT devices: ； in, Indicates the first Device topology relationships between IoT devices. Indicates the first The distance between the deployment locations of each IoT device Indicates the distance control coefficient. This represents an exponential function with the natural constant as its base. Indicates the first A collection of device functions for an Internet of Things (IoT) device. Indicates the first A collection of device functions for an Internet of Things (IoT) device. This represents the intersection operator. This represents the union operator. This indicates the number of device functions in the set. Indicates the first The number of times a device information data packet sent by an IoT device falls within the same batch of data packets within a sliding time window. Indicates the first The number of times each IoT device generates a linkage message within a sliding time window for sending device information data packets. All represent the device topology control coefficient; , Indicates the total number of IoT devices; If the device topology If the value exceeds the preset topological relationship threshold, then the first... Each IoT device is a neighboring IoT device.

6. A cloud IoT device intelligent linkage system with edge-cloud collaboration, characterized in that, The edge-cloud collaborative cloud IoT device intelligent linkage system includes a data transmission device, a computing unit, and a cloud collaborative management platform to realize the edge-cloud collaborative cloud IoT device intelligent linkage method as described in any one of claims 1-5.

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