WO2022262127A1 - 智慧物联网综合感知系统及方法 - Google Patents

智慧物联网综合感知系统及方法 Download PDF

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WO2022262127A1
WO2022262127A1 PCT/CN2021/116815 CN2021116815W WO2022262127A1 WO 2022262127 A1 WO2022262127 A1 WO 2022262127A1 CN 2021116815 W CN2021116815 W CN 2021116815W WO 2022262127 A1 WO2022262127 A1 WO 2022262127A1
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sensor device
node
layer
topology
data
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PCT/CN2021/116815
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English (en)
French (fr)
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邱铁
陈宁
王浩东
李克秋
周晓波
李涛
池建成
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天津大学
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Priority to US17/907,210 priority Critical patent/US20240214905A1/en
Publication of WO2022262127A1 publication Critical patent/WO2022262127A1/zh

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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/30—Services specially adapted for particular environments, situations or purposes
    • H04W4/38—Services specially adapted for particular environments, situations or purposes for collecting sensor information
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16Y—INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR THE INTERNET OF THINGS [IoT]
    • G16Y20/00—Information sensed or collected by the things
    • G16Y20/10—Information sensed or collected by the things relating to the environment, e.g. temperature; relating to location
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
    • H04W16/18—Network planning tools
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W40/00—Communication routing or communication path finding
    • H04W40/24—Connectivity information management, e.g. connectivity discovery or connectivity update
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W76/00—Connection management
    • H04W76/10—Connection setup
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W84/00—Network topologies
    • H04W84/18—Self-organising networks, e.g. ad-hoc networks or sensor networks

Definitions

  • the present invention relates to the field of the Internet of Things, in particular to a comprehensive perception system and method for the Smart Internet of Things.
  • Chinese invention patent application CN202110121948.2 provides a smart brain-based comprehensive management platform and method for the Internet of Things, the platform includes: an initial setting module, used to set a preset number of servers, associate the servers, and set Management function; the device connection module is used to connect the server to the corresponding IoT device, so that the server can control the IoT device while receiving the data transmitted by the corresponding IoT device; the data acquisition module is used to obtain multiple IoT devices.
  • the communication state data of the networked device and the device operation data in multiple Internet of Things devices determine that the communication state data corresponds to the first data list and the device operation data corresponds to the second data list; the comprehensive management module is used to combine the first data list and the second data list
  • the second data list is imported into the standard database, and the imported data is used for comprehensive management, so as to realize the overall analysis of the entire smart park, community, and building, and ensure the normal operation of the entire smart park, community, and building.
  • the perception level involved in the integrated management platform of the Internet of Things is single, and it is limited to obtaining the communication status data and Operating data; in addition, each IoT device is connected to the corresponding server through the device connection module, but there is a lack of interaction between different data acquisition modules, and these data acquisition modules cannot achieve intelligent self-learning and self-optimization, which is difficult to achieve Comprehensive perception for the Internet of Things.
  • the first technical problem to be solved by the present invention is to provide a smart Internet of Things comprehensive perception system that can meet the interaction requirements between different data acquisition modules and realize the comprehensive perception of multiple Internet of Things devices.
  • the second technical problem to be solved by the present invention is to provide a comprehensive sensing method for the smart Internet of Things based on the above-mentioned comprehensive sensing system for the smart Internet of Things.
  • a smart Internet of Things comprehensive perception system which is characterized in that it includes:
  • the data perception layer is formed by multiple sensor device nodes, and each sensor device node corresponds to monitoring different environmental data;
  • connection and transmission layer is formed by multiple wireless communication modules, each sensor device node is provided with at least one wireless communication module, so that the wireless communication modules located on all sensor device nodes together form a wireless sensor network, connection and transmission
  • the layer is connected to the data perception layer; among them, the connection and the transport layer receive the environmental monitoring data sent by the data perception layer, and the self-learning topology control mechanism and the hierarchical cooperative sensing node monitoring mechanism are adopted between the connection and all wireless communication modules in the transport layer ;
  • the edge computing layer is formed by multiple edge computing devices.
  • the edge computing layer is connected to the connection and transport layer, and receives the environmental monitoring data sent by the connection and transport layer;
  • the cloud computing layer is formed by the cloud computing center.
  • the cloud computing layer is connected to the edge computing layer.
  • the cloud computing center realizes data fusion processing according to the data provided by each edge computing device in the edge computing layer, and forms actions for different application devices respectively. instructions; and,
  • the application layer is formed by multiple application devices.
  • the application layer is connected to the cloud computing layer.
  • Each application device receives the action instructions from the cloud computing center to execute actions corresponding to the action instructions received by itself.
  • the sensor device nodes in the data perception layer adopt an ad hoc network method to realize data upload; wherein, the ad hoc network method includes the following steps 1-8:
  • Step 1 pre-store a list of directly connected nodes in each sensor device node; wherein, the list of directly connected nodes in any sensor device node includes the sensor device node directly connected to the any sensor device node, the sensor device node's Node address and the node address of the next hop sensor device node of the data directly forwarded by the sensor device node;
  • Step 2 use any sensor device node in the data perception layer as the starting node of the ad hoc network, and perform tokenization processing on any sensor device node; wherein, the tokenization process includes the any sensor device node
  • the state is marked as being connected to the network, the directly connected node list of any sensor device node is initialized to zero, and the node address of any sensor device node is set as the edge device address;
  • Step 3 when any new node is added to the data perception layer, the any new node performs a search and judgment process on the network-connected nodes within its own communication range:
  • step 4 When there is no network-connected node, go to step 4; otherwise, go to step 5;
  • Step 4 the any new node continues to search for the preset time again for the nodes already connected to the network within its own communication range, and makes a judgment process according to the search result again:
  • any new node marks itself as the starting node of the ad hoc network, and proceeds to step 2; otherwise, proceeds to step 5;
  • Step 5 the any new node locks the existing network-connected node, and performs interaction with the locked network-connected node
  • Step 6 Judging whether any new node can establish a connection relationship with the network-connected node:
  • Step 7 All sensor device nodes that have joined the network upload their monitored environmental data to the edge computing device through the connection and transport layers; among them, the sensor device nodes that have not uploaded environmental data are in a dormant state;
  • step 8 the any new node abandons the node that has joined the network, and proceeds to step 3.
  • the edge computing device performs self-learning topology control on the topology relationship between all sensor device nodes in the system according to the following steps a1-a6:
  • the edge device obtains the connection relationship of the new sensor device nodes added to the system in the data perception layer, and refreshes the global sensor network topology information in the edge device; wherein, the global sensor network topology information is the data perception layer The current topology connection relationship between all sensor device nodes in ;
  • Step a2 the edge device optimizes the topological connection relationship of all sensor device nodes in the data perception layer, and generates a new topological connection relationship;
  • step a3 the edge device compares the new topology connection relationship with the current topology connection relationship to obtain the difference in topology connection relationship, and sends the topology connection relationship difference as a topology relationship change command message to the sensor device that needs to change the topology connection relationship node;
  • Step a4 for each topological relationship change command information, the edge device calculates the optimal path from the sensor device node corresponding to each topology relationship change command information to the edge device;
  • Step a5 the sensor device node receiving the topological relationship change command information judges its own position in the corresponding optimal path, and makes processing according to the judged position:
  • the sensor device node When the sensor device node is not the last node in the corresponding optimal path, the sensor device node forwards the optimal path to the sensor device node at the next position to complete the topological relationship of the sensor device node Self-learning topology control; otherwise, the sensor device node changes its own directly connected node list and changes the node address of the next hop sensor device node of the data directly forwarded by the sensor device node according to the topology relationship change command information, so as to Complete the self-learning topology control of the topology relationship of the sensor device node;
  • steps a4-a5 are executed sequentially to complete the self-learning topology control of the topology relationship among all sensor device nodes in the system.
  • each sensor device node in the data perception layer implements node failure monitoring and reporting processing according to the node monitoring method of the following steps b1-b4:
  • Step b1 pre-store a node monitoring list in each sensor device node; wherein, the node monitoring list in any sensor device node includes the sensor device node sequence that any sensor device node needs to monitor, and at least Including a sensor device node that needs to be monitored by any sensor device node;
  • Step b2 when any sensor device node in the data perception layer performs node monitoring, the any sensor device node sends a detection frame to all sensor device nodes in the sensor device node sequence in the node monitoring list stored in it;
  • Step b3 after the sensor device node in the sensor device node sequence in the node monitoring list receives the detection frame, it will send a confirmation frame to the sensor device node that sent the detection frame;
  • Step b4 any sensor device node that sends the detection frame makes a judgment process according to the received confirmation frame fed back by each sensor device node in the sensor device node sequence:
  • any sensor device node has not received the confirmation frame of any sensor device node in the sensor device node sequence for N consecutive times, it is determined that the sensor device node in the sensor device node sequence has a failure; otherwise, it is determined that the sensor device node in the sensor device node sequence
  • the sensor device node of is not faulty; where, N ⁇ 2 and is a positive integer.
  • the edge device completes the deployment of the monitoring network topology of the data perception layer according to the following steps c1-c4:
  • Step c1 the edge device divides all sensor device nodes in the data perception layer into different levels
  • Step c2 the edge device generates a monitoring network topology relationship corresponding to the data perception layer according to the communication range of each sensor device node; wherein, the sensor device node monitors the sensor device nodes at the same level within its communication range;
  • Step c3 the edge device generates a change node monitoring list command for each sensor device node according to the generated monitoring network topology relationship;
  • Step c4 the edge device sends a command to change the node monitoring list to all sensor device nodes in the data perception layer to complete the deployment of the monitoring network topology.
  • the technical solution adopted by the present invention to solve the second technical problem is: the comprehensive perception method of the intelligent Internet of Things, using the comprehensive perception system of the intelligent Internet of Things, which is characterized in that it includes the following steps:
  • Each sensor device node in the data perception layer monitors the environmental data separately, and sends the monitored environmental monitoring data to the edge computing layer through the connection and transmission layer;
  • the edge computing layer processes the received environmental monitoring data sent through the connection and transport layer, and sends the fused data to the cloud computing layer; where, the edge computing layer processes all sensors in the intelligent IoT comprehensive perception system
  • the topology relationship between device nodes performs self-learning topology control
  • the cloud computing layer implements data fusion processing according to the data provided by each edge computing device in the edge computing layer, and generates action instructions provided to the application device;
  • the application device at the application layer executes an action corresponding to the received action command according to the action command sent by the cloud computing layer.
  • the edge computing device performs self-learning topology control on the topology relationship among all sensor device nodes in the system according to the following steps a1-a6:
  • the edge device obtains the connection relationship of the new sensor device nodes added to the system in the data perception layer, and refreshes the global sensor network topology information in the edge device; wherein, the global sensor network topology information is the data perception layer The current topology connection relationship between all sensor device nodes in ;
  • Step a2 the edge device optimizes the topological connection relationship of all sensor device nodes in the data perception layer, and generates a new topological connection relationship;
  • step a3 the edge device compares the new topology connection relationship with the current topology connection relationship to obtain the difference in topology connection relationship, and sends the topology connection relationship difference as a topology relationship change command message to the sensor device that needs to change the topology connection relationship node;
  • Step a4 for each topological relationship change command information, the edge device calculates the optimal path from the sensor device node corresponding to each topology relationship change command information to the edge device;
  • Step a5 the sensor device node receiving the topological relationship change command information judges its own position in the corresponding optimal path, and makes processing according to the judged position:
  • the sensor device node When the sensor device node is not the last node in the corresponding optimal path, the sensor device node forwards the optimal path to the sensor device node at the next position to complete the topological relationship of the sensor device node Self-learning topology control; otherwise, the sensor device node changes its own directly connected node list and changes the node address of the next hop sensor device node of the data directly forwarded by the sensor device node according to the topology relationship change command information, so as to Complete the self-learning topology control of the topology relationship of the sensor device node;
  • steps a4-a5 are executed sequentially to complete the self-learning topology control of the topology relationship among all sensor device nodes in the system.
  • the comprehensive sensing method of the smart Internet of Things further includes: the edge device completes the deployment of the monitoring network topology of the data sensing layer according to the following steps c1-c4:
  • Step c1 the edge device divides all sensor device nodes in the data perception layer into different levels
  • Step c2 the edge device generates a monitoring network topology relationship corresponding to the data perception layer according to the communication range of each sensor device node; wherein, the sensor device node monitors the sensor device nodes at the same level within its communication range;
  • Step c3 the edge device generates a change node monitoring list command for each sensor device node according to the generated monitoring network topology relationship;
  • Step c4 the edge device sends a command to change the node monitoring list to all sensor device nodes in the data perception layer to complete the deployment of the monitoring network topology.
  • the comprehensive sensing method for the smart Internet of Things further includes: each sensor device node in the data sensing layer implements node fault monitoring and reporting processing according to the following node monitoring methods of steps b1-b4:
  • Step b1 pre-store a node monitoring list in each sensor device node; wherein, the node monitoring list in any sensor device node includes the sensor device node sequence that any sensor device node needs to monitor, and at least Including a sensor device node that needs to be monitored by any sensor device node;
  • Step b2 when any sensor device node in the data perception layer performs node monitoring, the any sensor device node sends a detection frame to all sensor device nodes in the sensor device node sequence in the node monitoring list stored in it;
  • Step b3 after the sensor device node in the sensor device node sequence in the node monitoring list receives the detection frame, it will send a confirmation frame to the sensor device node that sent the detection frame;
  • Step b4 any sensor device node that sends the detection frame makes a judgment process according to the received confirmation frame fed back by each sensor device node in the sensor device node sequence:
  • any sensor device node has not received the confirmation frame of any sensor device node in the sensor device node sequence for N consecutive times, it is determined that the sensor device node in the sensor device node sequence has a failure; otherwise, it is determined that the sensor device node in the sensor device node sequence
  • the sensor device node of is not faulty; where, N ⁇ 2 and is a positive integer.
  • the comprehensive sensing method for the smart Internet of Things of the invention also includes: performing robust performance improvement optimization on the current topology among all sensor device nodes in the data sensing layer, so as to output the optimal topology operate.
  • the present invention has the advantages of:
  • the invention forms a data perception layer by a plurality of sensor device nodes collecting different types of environmental data, and sends the environmental data monitored by these sensor device nodes to the edge computing device in the edge computing layer through the connection and transmission layer for processing , and then the cloud computing layer performs fusion processing according to the data processed by the edge computing devices to form instructions for execution by different application devices in the application layer.
  • a data perception layer by a plurality of sensor device nodes collecting different types of environmental data, and sends the environmental data monitored by these sensor device nodes to the edge computing device in the edge computing layer through the connection and transmission layer for processing , and then the cloud computing layer performs fusion processing according to the data processed by the edge computing devices to form instructions for execution by different application devices in the application layer.
  • the invention adopts the self-organizing network method to upload data and the edge computing device performs self-learning topology control on the topology relationship between all sensor device nodes in the data perception layer.
  • the edge computing device performs self-learning topology control on the topology relationship between all sensor device nodes in the data perception layer.
  • the invention also sets up different levels of collaborative sensing and monitoring mechanisms for the cooperation between all sensor device nodes in the data perception layer, that is, by judging the communication range of different sensor device nodes themselves, respectively generating corresponding to different sensor device nodes. Change the node monitoring list command to complete the deployment of the monitoring network topology for the data perception layer and improve the monitoring efficiency between sensor device nodes.
  • FIG. 1 is a schematic diagram of a comprehensive perception system of the smart Internet of Things in an embodiment of the present invention
  • FIG. 2 is a schematic flowchart of a method for comprehensive perception of the smart Internet of Things in an embodiment of the present invention.
  • the intelligent IoT comprehensive perception system of this embodiment includes a data perception layer 1, a connection and transmission layer 2, an edge computing layer 3, a cloud computing layer 4 and an application layer 5, and the connection and transmission layer 2 are respectively connected with
  • the data perception layer 1 is connected to the edge computing layer 3, and the cloud computing layer 4 is connected to the edge computing layer 3 and the application layer 5 respectively.
  • the data perception layer 1 is formed by multiple (the meaning of "multiple” in this embodiment refers to including at least two, that is, two or more) sensor device nodes, and each sensor device node corresponds to monitoring different environmental data and, in the data perception layer, the ad hoc network method of the following steps 1 to 8 is used to upload data between each sensor device node, and each sensor device node realizes node fault monitoring according to the node monitoring method of the following steps b1 to b4 and report processing, these sensor device nodes may be ultrasonic sensors, temperature sensors, humidity sensors, gas sensors, light sensors, smoke sensors, atmospheric pressure sensors, or sound sensors.
  • the ad hoc network method includes the following steps 1-8:
  • Step 1 pre-store a list of directly connected nodes in each sensor device node; wherein, the list of directly connected nodes in any sensor device node includes the sensor device node directly connected to the any sensor device node, the sensor device node's Node address and the node address of the next hop sensor device node of the data directly forwarded by the sensor device node;
  • Step 2 use any sensor device node in the data perception layer as the starting node of the ad hoc network, and perform tokenization processing on any sensor device node; wherein, the tokenization process includes the any sensor device node
  • the state is marked as being connected to the network, the directly connected node list of any sensor device node is initialized to zero, and the node address of any sensor device node is set as the edge device address;
  • Step 3 when any new node is added to the data perception layer, the any new node performs a search and judgment process on the network-connected nodes within its own communication range:
  • step 4 When there is no network-connected node, go to step 4; otherwise, go to step 5; wherein, the new node mentioned here refers to a new sensor device node;
  • Step 4 Any new node continues to search for the preset time for the network-connected nodes within its own communication range.
  • the preset time for re-searching here is set to 5-10s, and the judgment process is made according to the re-searching result:
  • Step 5 the any new node locks the existing network-connected node, and performs interaction with the locked network-connected node
  • Step 6 Judging whether any new node can establish a connection relationship with the network-connected node:
  • the network-connected node When the two can establish a connection relationship, it means that the network-connected node is working normally, and the network-connected node can perform the receiving and forwarding of data packets, and any new node can set the network-connected node as the target of data transmission , so as to complete the function of data upload, and mark the status of any new node as already connected to the network, that is, any new node has joined the data perception layer, and set the node address of the locked node that has been connected to the network to pass through this task.
  • the node address of the next hop sensor device node of the data directly forwarded by a new node turn to step 7; otherwise, step 8;
  • Step 7 All sensor device nodes that have joined the network upload their monitored environmental data to the edge computing device through the connection and transport layers; among them, the sensor device nodes that have not long-transmitted environmental data are in a dormant state;
  • step 8 any new node abandons the network-connected node, and proceeds to step 3, so as to search and judge the network-connected nodes within its own communication range again.
  • the node monitoring method performed by the sensor device node in this embodiment includes the following steps b1-b4:
  • Step b1 pre-store a node monitoring list in each sensor device node; wherein, the node monitoring list in any sensor device node includes the sensor device node sequence that any sensor device node needs to monitor, and at least Including a sensor device node that needs to be monitored by any sensor device node;
  • Step b2 when any sensor device node in the data perception layer performs node monitoring, the any sensor device node sends a detection frame to all sensor device nodes in the sensor device node sequence in the node monitoring list stored in it;
  • Step b3 after the sensor device node in the sensor device node sequence in the node monitoring list receives the detection frame, it will send a confirmation frame to the sensor device node that sent the detection frame;
  • Step b4 any sensor device node that sends the detection frame makes a judgment process according to the received confirmation frame fed back by each sensor device node in the sensor device node sequence:
  • any sensor device node has not received the confirmation frame of any sensor device node in the sensor device node sequence for N consecutive times, it is determined that the sensor device node in the sensor device node sequence has a failure; otherwise, it is determined that the sensor device node in the sensor device node sequence
  • the sensor device node of is not faulty; among them, N ⁇ 2 and is a positive integer;
  • connection and transmission layer 2 is formed by multiple wireless communication modules. At least one wireless communication module is installed on each sensor device node to form a wireless sensor network by wireless communication modules located on all sensor device nodes.
  • the transport layer is connected to the data sensing layer; among them, the connection and the transport layer receive the environmental monitoring data sent by the data sensing layer, and the self-learning topology control mechanism and hierarchical cooperative sensing node monitoring are adopted between the connection and all wireless communication modules in the transport layer mechanism;
  • the edge computing layer 3 is formed by a plurality of edge computing devices, the edge computing layer is connected to the connection and transport layer, and receives the environmental monitoring data sent by the connection and transport layer; wherein, the edge computing device in this embodiment follows the following steps a1
  • the method of ⁇ a6 performs self-learning topology control on the topology relationship between all sensor device nodes in the system:
  • the edge device obtains the connection relationship of the new sensor device nodes added to the system in the data perception layer, and refreshes the global sensor network topology information in the edge device; wherein, the global sensor network topology information is the data perception layer The current topology connection relationship between all sensor device nodes in ;
  • Step a2 the edge device optimizes the topological connection relationship of all sensor device nodes in the data perception layer, and generates a new topological connection relationship;
  • step a3 the edge device compares the new topology connection relationship with the current topology connection relationship to obtain the difference in topology connection relationship, and sends the topology connection relationship difference as a topology relationship change command message to the sensor device that needs to change the topology connection relationship node;
  • Step a4 for each topological relationship change command information, the edge device calculates the optimal path from the sensor device node corresponding to each topology relationship change command information to the edge device through the Dijkstra algorithm (ie Dijkstra algorithm);
  • Step a5 the sensor device node receiving the topological relationship change command information judges its own position in the corresponding optimal path, and makes processing according to the judged position:
  • the sensor device node forwards the optimal path to the sensor device node at the next position, and the sensor device node at the next position executes The same self-position judgment processing operation as the sensor device node, until the last node transmitted to the optimal path, that is, the destination node, completes the self-learning topology control of the topology relationship of the sensor device node; otherwise, the sensor device node According to the topological relationship change command information, change its own list of directly connected nodes and change the node address of the next hop sensor device node of the data directly forwarded by the sensor device node, so as to complete the automatic modification of the topological relationship of the sensor device node Learning topology control; wherein, the new list of directly connected nodes corresponding to the destination node will be given in the topological relationship change command information, and the list in the destination node will be directly overwritten with the new list, and the topological relationship change command information will be given The new node address of the overwrites the original no
  • steps a4-a5 are executed sequentially to complete the self-learning topology control of the topology relationship among all sensor device nodes in the system.
  • the cloud computing layer 4 is formed by the cloud computing center.
  • the cloud computing layer is connected to the edge computing layer.
  • the cloud computing center realizes data fusion processing according to the data provided by each edge computing device in the edge computing layer, and respectively forms data for different application devices. action command;
  • the application layer 5 is formed by multiple application devices.
  • the application layer is connected to the cloud computing layer.
  • Each application device receives action instructions from the cloud computing center to execute actions corresponding to the action instructions it receives.
  • the edge device on the edge computing layer 3 completes the deployment of the monitoring network topology of the data perception layer according to the following steps c1-c4:
  • Step c1 the edge device divides all sensor device nodes in the data perception layer into different levels; where the division of levels can be based on the number of hops between the node and the edge device address and the direct connection of the node during the data upload process
  • the number of child nodes that is, how many nodes set the node as the "next hop" address
  • Step c2 the edge device generates a monitoring network topology corresponding to the data perception layer according to the communication range of each sensor device node; wherein, the sensor device node monitors the sensor device nodes at the same level within its communication range;
  • Step c3 the edge device generates a change node monitoring list command for each sensor device node according to the generated monitoring network topology relationship; wherein, the generated monitoring network topology relationship will provide each node that needs to monitor the node, that is, the node monitoring list; For each node, the edge device will compare whether the new node monitoring list of the node is the same as the original node monitoring list: if they are the same, no command will be sent to the node; otherwise, the new node monitoring list will be placed in the changed node monitoring list In the list command, the optimal path is obtained from the node topology connection relationship, and the monitoring list command is sent to the node according to the optimal path;
  • Step c4 the edge device sends the command to change the node monitoring list to all sensor device nodes in the data perception layer, and completes the deployment of the monitoring network topology.
  • the edge device actually completes the hierarchical cooperative sensing node monitoring mechanism by performing the above steps c1 ⁇ c4, and each node monitors its status with each other, that is, adopts the collaborative sensing method, which can comprehensively , Timely discover the faulty nodes in the system and report them quickly, improve the efficiency of node monitoring, and maintain the normal operation of the monitoring system.
  • This embodiment also provides a comprehensive sensing method for the smart Internet of Things realized by using the above-mentioned comprehensive sensing system for the smart Internet of Things. Specifically, as shown in FIG. 2, the comprehensive perception method for the smart Internet of Things of this embodiment includes the following steps:
  • Step S1 each sensor device node in the data perception layer monitors the environmental data respectively, and sends the monitored environmental monitoring data to the edge computing layer through the connection and transmission layer; wherein, each sensor device node in the data perception layer is as follows
  • the node monitoring method of steps b1 ⁇ b4 realizes node fault monitoring and reporting processing:
  • Step b1 pre-store a node monitoring list in each sensor device node; wherein, the node monitoring list in any sensor device node includes the sensor device node sequence that any sensor device node needs to monitor, and at least Including a sensor device node that needs to be monitored by any sensor device node;
  • Step b2 when any sensor device node in the data perception layer performs node monitoring, the any sensor device node sends a detection frame to all sensor device nodes in the sensor device node sequence in the node monitoring list stored in it;
  • Step b3 after the sensor device node in the sensor device node sequence in the node monitoring list receives the detection frame, it will send a confirmation frame to the sensor device node that sent the detection frame;
  • Step b4 any sensor device node that sends the detection frame makes a judgment process according to the received confirmation frame fed back by each sensor device node in the sensor device node sequence:
  • any sensor device node has not received the confirmation frame of any sensor device node in the sensor device node sequence for N consecutive times, it is determined that the sensor device node in the sensor device node sequence has a failure; otherwise, it is determined that the sensor device node in the sensor device node sequence
  • the sensor device node of is not faulty; among them, N ⁇ 2 and is a positive integer;
  • Step S2 the edge computing layer processes the received environmental monitoring data sent through the connection and transport layer, and sends the fused data to the cloud computing layer; wherein, the edge computing layer senses the smart IoT comprehensive perception system Perform self-learning topology control on the topology relationship between all sensor device nodes in the system; specifically, the edge computing device performs self-learning topology control on the topology relationship between all sensor device nodes in the system according to the following steps a1-a6:
  • the edge device obtains the connection relationship of the new sensor device nodes added to the system in the data perception layer, and refreshes the global sensor network topology information in the edge device; wherein, the global sensor network topology information is the data perception layer The current topology connection relationship between all sensor device nodes in ;
  • Step a2 the edge device optimizes the topological connection relationship of all sensor device nodes in the data perception layer, and generates a new topological connection relationship;
  • step a3 the edge device compares the new topology connection relationship with the current topology connection relationship to obtain the difference in topology connection relationship, and sends the topology connection relationship difference as a topology relationship change command message to the sensor device that needs to change the topology connection relationship node;
  • Step a4 for each topological relationship change command information, the edge device calculates the optimal path from the sensor device node corresponding to each topology relationship change command information to the edge device;
  • Step a5 the sensor device node receiving the topological relationship change command information judges its own position in the corresponding optimal path, and makes processing according to the judged position:
  • the sensor device node When the sensor device node is not the last node in the corresponding optimal path, the sensor device node forwards the optimal path to the sensor device node at the next position to complete the topological relationship of the sensor device node Self-learning topology control; otherwise, the sensor device node changes its own directly connected node list and the node address of the next hop sensor device node of the data directly forwarded by the sensor device node according to the topology relationship change command information, and completes the Self-learning topology control of the topology relationship of sensor device nodes;
  • Step a6 for all sensor device nodes in the data perception layer, perform steps a4 to a5 in sequence to complete the self-learning topology control of the topology relationship among all sensor device nodes in the system;
  • Step S3 the cloud computing layer implements data fusion processing according to the data provided by each edge computing device in the edge computing layer, and generates an action instruction provided to the application device;
  • step S4 the application layer executes an action corresponding to the received action command according to the action command sent by the cloud computing layer.
  • the edge computing device in this embodiment can optimize the network topology according to the new node status at any time, so as to realize the automatic and dynamic adjustment of the network topology and keep the monitoring system in good working condition.
  • the average transmission of data packets can also be reduced Length, so that the average transmission length of the data packet is maintained at an optimal state, balances energy and prolongs the life of each node in the monitoring system, and ensures the maintenance of the life of the node network built by the entire IoT monitoring system.
  • the comprehensive perception method of the smart Internet of Things in this embodiment also includes: all sensor device nodes in the data perception layer Perform robust performance-enhancing optimizations between the current topologies to output operations that yield the optimal topologies.
  • performing robust performance improvement optimization on the current topology among all sensor device nodes in the data perception layer to output the operation of obtaining the optimal topology includes the following steps d1-d7:
  • Step d1 generate an initialized Internet of Things topology based on the rules of the scale-free network model, and randomly deploy multiple network topology nodes in the Internet of Things topology; wherein, in the initialized Internet of Things topology, each network The topological nodes (that is, the sensor device nodes in the data perception layer) correspond to a fixed geographic location, and all network topological nodes have the same attributes; The probability of a node is positively correlated with the degree of the previous network topology node;
  • the mth network topology node is marked as G m
  • the geographic location coordinates of the network topology node are
  • Step d2 according to the network Motif, extract all network Motifs conforming to 4 nodes in the initialized Internet of Things topology structure, and use each extracted network Motif as the minimum operation unit in the process of optimizing the Internet of Things topology structure; , in this technical field, network Motif or Motif is a technical term well known to those skilled in the art, Motif refers to a type of subgraph, the number of certain interconnected patterns found in the subgraph in a complex network Significantly higher than the number of such interconnected patterns in random networks.
  • the network Motif conforming to 4 nodes mentioned here refers to an undirected graph composed of 4 nodes (ie, four network topology nodes);
  • step 2 of extracting network motifs matching 4 nodes Q network motifs matching 4 nodes are obtained, and the qth network motif matching 4 nodes is marked as Motif q , 1 ⁇ q ⁇ Q,
  • Step d3 select all minimum operation units with reconnectable edges from all the extracted minimum operation units as operation units;
  • step 2 Through the extraction operation in step 2, it is assumed that Q minimum operation units unit 1 ⁇ unit Q are obtained, and then in step 3, all the minimum operation units with reconnectable edges are selected from the Q minimum operation units as operation units, Assuming that all the selected minimum operation units with reconnectable edges are unit 1 , unit 3 , unit 4 , and unit 5 , then, here, the minimum operation unit unit 1 is used as the operation unit Unit 1 , and the minimum operation unit Unit 3 is used as the operating unit Unit 3 , the smallest operating unit unit 4 is used as the operating unit Unit 4 , and the smallest operating unit unit unit 5 is used as the operating unit Unit 5 ;
  • Step d4 changing the edge connection relationship of some operating units extracted in the initialized Internet of Things topology structure, and using the Internet of Things topology structure after changing the edge connection relationship as a new Internet of Things topology structure;
  • the two operation units Unit 4 and Unit 5 among the four operation units selected above perform the process of changing the edge connection relationship.
  • the first change operation in this way, the topology structure of the initialized Internet of Things topology C 0 will change after this change operation, and then the Internet of Things topology structure after changing the edge connection relationship will be used as a new thing Networking topology, and mark the new Internet of Things topology obtained after the first change operation as C 1 ;
  • step d5 the operation of step d4 is repeated multiple times to obtain multiple new IoT topological structures, and a population is formed from the multiple new IoT topological structures; wherein, the change operation for the edge connection relationship in each operation is not Exactly the same, and treat each new IoT topology in the population as an individual;
  • Step d6 constructing a robust performance measurement index for measuring the robust performance of the Internet of Things topology; wherein, the construction process of the robust performance measurement index here includes the following steps d61-d65:
  • Step d61 making cumulative statistics on the number of network Motifs that conform to 3 nodes contained in the initialized Internet of Things topology structure after each network attack;
  • Step d62 obtain the total number of edges of the initialized Internet of Things topology and the total number of network topology nodes of the initialized Internet of Things topology; wherein, the total number of edges of the initialized Internet of Things topology is marked as E, and the initialized Internet of Things The total number of network topology nodes of the topology is marked as V, V>3;
  • Step d63 after the k-th network attack, the total number of edges of the union of all the network Motifs conforming to 3 nodes in the topology of the Internet of Things is obtained; wherein, all the network Motifs conforming to 3 nodes here are The total number of sides of the formed union is marked as MC(k), k ⁇ 1;
  • the topology of the Internet of Things has undergone the k-th network attack, and we perform a union operation on all the edge sets of the network motifs that match the three nodes, and then remove duplicate edges to obtain a network topology , counting the number of edges included in the network topology, the number of edges obtained by the statistics is the total number of edges MC(k) of the union of all the network Motifs that meet the three nodes here;
  • Step d64 make a judgment process according to the number of network Motifs that match the 3 nodes obtained through statistics:
  • Step d65 normalize the number of network Motifs that meet the 3 nodes obtained through statistics, and use the value obtained after normalization as the robust performance index; wherein, the robust performance index is marked as I:
  • Step d7 using the distributed artificial immune optimization algorithm to optimize each new Internet of Things topology in the population, and output the Internet of Things topology with the best robust performance measurement index as the optimal Internet of Things topology.
  • the topological structure of the Internet of Things with the optimal robust performance measurement index as the output process of the optimal topological structure of the Internet of Things includes the following steps d71-d77:
  • Step d71 set up N local optimization programs and 1 global optimization program; among them, each local optimization program is independent of each other, each local program runs a population P, and each local program runs population P separately Crossover operation, mutation operation and selection operation, the nth local optimizer is marked as L n , 1 ⁇ n ⁇ N, and the global optimizer is marked as GL;
  • Step d72 define the cross operation strategy:
  • motif i motif j ⁇ G i (, loc), G j (, loc);
  • G i (, loc) and G j (, loc) respectively represent two different individuals whose intercrossing position is at loc in the same population, and select the short side of the chromosome at the crossing position to search, and the chromosome is composed of all It is composed of network motifs conforming to 4 nodes, and one of the motifs in the chromosome is called a motif base.
  • Motif i indicates one of the network motifs conforming to 4 nodes in a type of individual
  • motif j indicates that it is in the same pattern as Motif i belongs to another type of individual of the same individual type, one of which conforms to the network Motif of 4 nodes and can intersect the network Motif with motif i ;
  • Step d73 define mutation operation strategy:
  • Step d74 define selection operation strategy:
  • P GL ⁇ L r , L t , L, L z ⁇ ;
  • P GL represents the population in which the global optimization program GL operates, and the population P GL is composed of elite population individuals L r , L t , ..., L z selected by the ontology optimization program GL using different selection strategies; when the local After the optimization program GL executes the cross-mutation operation, it calculates the robust performance index of each individual, and selects the two elite individuals with the largest robust performance index value and sends them to the global optimization program GL, and the global optimization program GL receives the elite population Individuals, and then continue to optimize operations; at the same time, the global optimization program GL sets up a communication queue Q to store the elite population individuals selected by the local optimization program; among them, the elite population individuals are individuals with the largest robust performance index in a population , which is the best IoT topology;
  • the initial global optimization program directly selects two populations in the communication queue Q; then, selects a population individual from the communication queue Q;
  • the robustness performance index of the selected population individual is better than the average robustness performance index of the global population, select the population individual; otherwise, continue to select the next population individual in the communication queue Q;
  • Step d75 define "federal-state" communication mechanism and global optimization mechanism:
  • a communication queue Q is set to store the elite population individuals selected by the local optimization program; and, in each iteration process, the global optimization program GL selects an elite population individual from the communication queue Q, and the selected The elite population individual replaces the population individual with the lowest robustness performance indicator in the original population corresponding to the global optimization program GL; the "iteration" mentioned here refers to the operation of repeatedly executing step d7;
  • Step d76 interpreting the output robust performance metrics and the number of iterations:
  • the preset floating range is not higher than 0.001
  • the number of iterations currently executed does not exceed the preset maximum number of iterations (for example, the preset The maximum number of iterations is set to 1000)
  • save the robust performance index and turn to step d77; otherwise, continue to perform iterations until the number of iterations performed reaches the preset maximum number of iterations, then terminate the iteration process;
  • step d77 the saved IoT topology corresponding to the robust performance index is used as the IoT topology with the optimal robust performance index.
  • the network Motif conforming to 4 nodes is used as the genetic composition of the individual (that is, each new Internet of Things topology), reducing the search overhead of subsequent crossover and mutation, and the invention uses Distributed artificial immune algorithm can reduce computing overhead, increase population diversity, and search for the global optimal solution faster (that is, the topology of the Internet of Things with the best robust performance measurement index), realizing the full measurement of the network topology

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Abstract

本发明涉及一种智慧物联网综合感知系统及方法，智慧物联网综合感知系统包括数据感知层、连接与传输层、边缘计算层、云计算层和应用层，数据感知层由多个采集不同类型环境数据的传感器设备节点形成，数据感知层将环境监测数据经连接与传输层发送给边缘计算层的边缘计算设备处理，云计算层根据边缘计算设备处理后的数据做融合，分别形成供应用层中不同应用设备执行的指令，实现多层次的物联网综合感知，满足对于物联网中不同类型数据的综合化处理需要，该发明通过在数据感知层中引入自组网机制和针对不同层级传感器设备节点的协同感知监测机制，完成针对数据感知层的监测网络拓扑的部署，提高传感器设备节点之间的监测效率及整个系统的感知效率。

Description

智慧物联网综合感知系统及方法 技术领域
本发明涉及物联网领域，尤其涉及一种智慧物联网综合感知系统及方法。
背景技术
物联网作为融合多学科的复杂综合体，其应用对象正不断融入到社会的各个领域，并且在实现万物互联中发挥着越来越重要的作用。
中国发明专利申请CN202110121948.2提供了一种基于智慧大脑的物联网综合管理平台及方法，所述平台包括：初始设置模块，用于设置预设数量台服务器，将服务器进行关联，为服务器设定管理功能；设备连接模块，用于将服务器和与其对应的物联网设备实现连接，使服务器接收到对应的物联网设备传输的数据的同时控制物联网设备；数据获取模块，用于获取多个物联网设备的通信状态数据和多个物联网设备中的设备运行数据，确定通信状态数据对应第一数据清单和设备运行数据对应第二数据清单；综合管理模块，用于将第一数据清单和第二数据清单导入标准数据库中，并利用导入数据进行综合管理，从而实现对整个智慧园区、社区、楼宇的全局性分析，确保整个智慧园区、社区、楼宇的正常运行。
但是，上述发明专利申请CN202110121948.2中的物联网综合管理平台存在一些问题：该物联网综合管理平台所涉及到的感知层次是单一的，仅局限于获取多个物联网设备的通信状态数据和运行数据；另外，各物联网设备是通过设备连接模块实现与对应的服务器进行连接，但是不同的数据获取模块之间缺少交互，这些数据获取模块无法达到智能化的自学习和自优化，难以实现针对物联网的综合感知。
发明内容
本发明所要解决的第一个技术问题是针对上述现有技术提供一种能够满足不同数据获取模块之间交互需求，并实现多物联网设备综合感知的智慧物联网综合感知系统。
本发明所要解决的第二个技术问题是提供一种基于上述智慧物联网综合感知系统实现的智慧物联网综合感知方法。
本发明解决第一个技术问题所采用的技术方案为：智慧物联网综合感知系统，其特征在于，包括：
数据感知层，由多个传感器设备节点所形成，各传感器设备节点分别对应监测不同的环境数据；
连接与传输层，由多个无线通信模块所形成，每一个传感器设备节点上至少设置有 一个无线通信模块，以由位于所有传感器设备节点上的无线通信模块一起形成无线传感网络，连接与传输层和数据感知层相连；其中，连接与传输层接收数据感知层发送来的环境监测数据，该连接与传输层内的所有无线通信模块之间采用自学习拓扑控制机制和层级协同感知节点监测机制；
边缘计算层，由多个边缘计算设备所形成，边缘计算层和连接与传输层相连，接收连接与传输层发送来的环境监测数据；
云计算层，由云计算中心所形成，云计算层与边缘计算层相连，该云计算中心根据边缘计算层内各边缘计算设备所提供的数据实现数据融合处理，分别形成针对不同应用设备的动作指令；以及，
应用层，由多个应用设备所形成，应用层与云计算层相连，各应用设备分别接收云计算中心的动作指令，以执行与自身所接收动作指令相对应的动作。
改进地，在所述智慧物联网综合感知系统中，所述数据感知层中的各传感器设备节点之间采用自组网方法实现数据上传；其中，该自组网方法包括如下步骤1～8：
步骤1，在每一个传感器设备节点内预先存储直连节点列表；其中，任一个传感器设备节点内的直连节点列表包括与该任一个传感器设备节点直接相连的传感器设备节点、该传感器设备节点的节点地址以及经该传感器设备节点直接转发的数据的下一跳的传感器设备节点的节点地址；
步骤2，将数据感知层内的任一个传感器设备节点作为自组网的起始节点，并且对该任一个传感器设备节点做标记化处理；其中，标记化处理包括将该任一个传感器设备节点的状态标记为已入网、将该任一个传感器设备节点的直连节点列表初始化为零以及将该任一个传感器设备节点的节点地址设置为边缘设备地址；
步骤3，任一个新节点加入到数据感知层内时，该任一个新节点对其自身通信范围内的已入网节点做搜索判断处理：
当不存在已入网节点时，转入步骤4；否则，转入步骤5；
步骤4，该任一个新节点对其自身通信范围内的已入网节点再次持续搜索预设时间，并且根据该再次搜索结果做判断处理：
当不存在已入网节点时，该任一个新节点将自身标记为自组网的起始节点，转入步骤2；否则，转入步骤5；
步骤5，该任一个新节点锁定存在的该已入网节点，并且与其锁定的该已入网节点执行交互；
步骤6，对该任一个新节点与该已入网节点能否建立连接关系做出判断处理：
当两者能建立连接关系时，将该任一个新节点状态标记为已入网，并且将其锁定的该已入网节点的节点地址设置为经过该任一个新节点直接转发的数据的下一跳的传感器设备节点的节点地址，转入步骤7；否则，转入步骤8；
步骤7，已入网的所有传感器设备节点分别将各自监测到的环境数据经连接与传输层上传至边缘计算设备；其中，未上传环境数据的传感器设备节点处于休眠状态；
步骤8，该任一个新节点放弃该已入网节点，并且转入步骤3。
进一步地，在所述智慧物联网综合感知系统中，所述边缘计算设备按照如下步骤a1～a6的方式对该系统内所有传感器设备节点之间的拓扑关系做自学习拓扑控制：
步骤a1，边缘设备获取加入到该系统内的新传感器设备节点在数据感知层中的连接关系，并且刷新该边缘设备中的全局传感器网络拓扑信息；其中，该全局传感器网络拓扑信息为数据感知层中所有传感器设备节点之间的当前拓扑连接关系；
步骤a2，边缘设备对数据感知层中所有传感器设备节点的拓扑连接关系做优化处理，并产生新拓扑连接关系；
步骤a3，边缘设备将该新拓扑连接关系与所述当前拓扑连接关系做比较，得到拓扑连接关系差别，并且将该拓扑连接关系差别作为拓扑关系更改命令信息发送给需要更改拓扑连接关系的传感器设备节点；
步骤a4，针对每一条拓扑关系更改命令信息，边缘设备计算各拓扑关系更改命令信息所对应传感器设备节点到该边缘设备的最优路径；
步骤a5，接收到拓扑关系更改命令信息的传感器设备节点判断自身在其所对应最优路径中的位置，并且根据判断出的该位置做出处理：
当该传感器设备节点为其所对应最优路径中的非最后一个节点时，该传感器设备节点将该最优路径转发给其后一个位置的传感器设备节点，完成对该传感器设备节点的拓扑关系的自学习拓扑控制；否则，该传感器设备节点根据该拓扑关系更改命令信息来更改自身的直连节点列表以及更改经该传感器设备节点直接转发的数据的下一跳的传感器设备节点的节点地址，以完成对该传感器设备节点的拓扑关系的自学习拓扑控制；
步骤a6，针对数据感知层中的所有传感器设备节点，依次执行步骤a4～a5，完成对该系统内所有传感器设备节点之间的拓扑关系做自学习拓扑控制。
再进一步地，在所述智慧物联网综合感知系统中，所述数据感知层中的各传感器设备节点按照如下步骤b1～b4的节点监测方式实现节点故障监测和上报处理：
步骤b1，在每一个传感器设备节点内预先存储节点监测列表；其中，任一个传感器设备节点内的节点监测列表包括该任一个传感器设备节点所需要监测的传感器设备节点序列，传感器设备节点序列内至少包括一个需要该任一个传感器设备节点监测的传感器设备节点；
步骤b2，数据感知层中的任一个传感器设备节点执行节点监测时，该任一个传感器设备节点向其存储的节点监测列表内传感器设备节点序列中的所有传感器设备节点发送探测帧；
步骤b3，节点监测列表内传感器设备节点序列中的传感器设备节点接收到探测帧 后，将发送确认帧给发送该探测帧的传感器设备节点；
步骤b4，发送探测帧的该任一个传感器设备节点根据接收到传感器设备节点序列中各传感器设备节点反馈的确认帧情况做出判断处理：
当该任一个传感器设备节点连续N次未收到传感器设备节点序列中任一传感器设备节点的确认帧时，判定传感器设备节点序列中的该传感器设备节点出现故障；否则，判定传感器设备节点序列中的该传感器设备节点未出现故障；其中，N≥2且为正整数。
再改进，在所述智慧物联网综合感知系统中，所述边缘设备按照如下步骤c1～c4的方法完成对数据感知层的监测网络拓扑的部署：
步骤c1，所述边缘设备将数据感知层中的所有传感器设备节点分为不同的层级；
步骤c2，所述边缘设备根据各传感器设备节点的通信范围，生成与数据感知层相对应的监测网络拓扑关系；其中，传感器设备节点对其通信范围内的同层级的传感器设备节点进行监测；
步骤c3，所述边缘设备根据生成的监测网络拓扑关系，生成针对每一个传感器设备节点的改变节点监测列表命令；
步骤c4，所述边缘设备将改变节点监测列表命令发送给数据感知层中的所有传感器设备节点，完成监测网络拓扑的部署。
本发明解决第二个技术问题所采用的技术方案为：智慧物联网综合感知方法，利用所述的智慧物联网综合感知系统，其特征在于，包括如下步骤：
数据感知层中的各传感器设备节点分别监测环境数据，并且将各自监测到的环境监测数据经连接与传输层发送给边缘计算层；
边缘计算层根据接收到的经连接与传输层发送来的环境监测数据做出处理，并且将融合后所得数据发送给云计算层；其中，边缘计算层对该智慧物联网综合感知系统内所有传感器设备节点之间的拓扑关系执行自学习拓扑控制；
云计算层根据边缘计算层内各边缘计算设备所提供的数据实现数据融合处理，生成提供给应用设备的动作指令；
以及，应用层的应用设备根据云计算层发送来的动作指令，执行与接收到的该动作指令相对应的动作。
改进地，在所述智慧物联网综合感知方法中，所述边缘计算设备按照如下步骤a1～a6的方式对该系统内所有传感器设备节点之间的拓扑关系做自学习拓扑控制：
步骤a1，边缘设备获取加入到该系统内的新传感器设备节点在数据感知层中的连接关系，并且刷新该边缘设备中的全局传感器网络拓扑信息；其中，该全局传感器网络拓扑信息为数据感知层中所有传感器设备节点之间的当前拓扑连接关系；
步骤a2，边缘设备对数据感知层中所有传感器设备节点的拓扑连接关系做优化处理，并产生新拓扑连接关系；
步骤a3，边缘设备将该新拓扑连接关系与所述当前拓扑连接关系做比较，得到拓扑连接关系差别，并且将该拓扑连接关系差别作为拓扑关系更改命令信息发送给需要更改拓扑连接关系的传感器设备节点；
步骤a4，针对每一条拓扑关系更改命令信息，边缘设备计算各拓扑关系更改命令信息所对应传感器设备节点到该边缘设备的最优路径；
步骤a5，接收到拓扑关系更改命令信息的传感器设备节点判断自身在其所对应最优路径中的位置，并且根据判断出的该位置做出处理：
当该传感器设备节点为其所对应最优路径中的非最后一个节点时，该传感器设备节点将该最优路径转发给其后一个位置的传感器设备节点，完成对该传感器设备节点的拓扑关系的自学习拓扑控制；否则，该传感器设备节点根据该拓扑关系更改命令信息来更改自身的直连节点列表以及更改经该传感器设备节点直接转发的数据的下一跳的传感器设备节点的节点地址，以完成对该传感器设备节点的拓扑关系的自学习拓扑控制；
步骤a6，针对数据感知层中的所有传感器设备节点，依次执行步骤a4～a5，完成对该系统内所有传感器设备节点之间的拓扑关系做自学习拓扑控制。
再改进，在该发明中，所述智慧物联网综合感知方法还包括：所述边缘设备按照如下步骤c1～c4的方法完成对数据感知层的监测网络拓扑的部署：
步骤c1，所述边缘设备将数据感知层中的所有传感器设备节点分为不同的层级；
步骤c2，所述边缘设备根据各传感器设备节点的通信范围，生成与数据感知层相对应的监测网络拓扑关系；其中，传感器设备节点对其通信范围内的同层级的传感器设备节点进行监测；
步骤c3，所述边缘设备根据生成的监测网络拓扑关系，生成针对每一个传感器设备节点的改变节点监测列表命令；
步骤c4，所述边缘设备将改变节点监测列表命令发送给数据感知层中的所有传感器设备节点，完成监测网络拓扑的部署。
进一步地，在该发明中，所述智慧物联网综合感知方法还包括：所述数据感知层中的各传感器设备节点按照如下步骤b1～b4的节点监测方式实现节点故障监测和上报处理：
步骤b1，在每一个传感器设备节点内预先存储节点监测列表；其中，任一个传感器设备节点内的节点监测列表包括该任一个传感器设备节点所需要监测的传感器设备节点序列，传感器设备节点序列内至少包括一个需要该任一个传感器设备节点监测的传感器设备节点；
步骤b2，数据感知层中的任一个传感器设备节点执行节点监测时，该任一个传感器设备节点向其存储的节点监测列表内传感器设备节点序列中的所有传感器设备节点发送探测帧；
步骤b3，节点监测列表内传感器设备节点序列中的传感器设备节点接收到探测帧后，将发送确认帧给发送该探测帧的传感器设备节点；
步骤b4，发送探测帧的该任一个传感器设备节点根据接收到传感器设备节点序列中各传感器设备节点反馈的确认帧情况做出判断处理：
当该任一个传感器设备节点连续N次未收到传感器设备节点序列中任一传感器设备节点的确认帧时，判定传感器设备节点序列中的该传感器设备节点出现故障；否则，判定传感器设备节点序列中的该传感器设备节点未出现故障；其中，N≥2且为正整数。
再改进地，该发明的所述智慧物联网综合感知方法还包括：对所述数据感知层中所有传感器设备节点之间的当前拓扑结构执行鲁棒性能提高优化，以输出得到最优拓扑结构的操作。
与现有技术相比，本发明的优点在于：
首先，该发明通过由采集不同类型环境数据的多个传感器设备节点形成数据感知层，并且将这些传感器设备节点所监测到的环境数据经连接与传输层发送给边缘计算层中的边缘计算设备处理，而后再由云计算层根据边缘计算设备处理后的数据做出融合处理，以分别形成供应用层中不同应用设备执行的指令，实现了多层次的物联网综合感知，满足了对于物联网中不同类型数据的综合化和一体化处理需要；
其次，该发明针对多个传感器设备节点所形成的数据感知层，采用了自组网方法上传数据以及由边缘计算设备对数据感知层中所有传感器设备节点之间的拓扑关系做自学习拓扑控制，以在保证网络连通性和覆盖性的前提下，充分考虑由多个无线通信模块所形成的无线传感网络特点，根据不同应用场景，通过针对不同传感器设备节点的选择来优化网络结构，保证完成预定的数据传输任务；
最后，该发明还对数据感知层中所有传感器设备节点之间的协作方式设置了不同层级的协同感知监测机制，即通过对不同传感器设备节点自身通信范围的判断，分别生成对应不同传感器设备节点的改变节点监测列表命令，以完成针对数据感知层的监测网络拓扑的部署，提高传感器设备节点之间的监测效率。
附图说明
图1为本发明实施例中的智慧物联网综合感知系统示意图；
图2为本发明实施例中的智慧物联网综合感知方法流程示意图。
具体实施方式
以下结合附图实施例对本发明作进一步详细描述。
本实施例首先提供一种智慧物联网综合感知系统。参见图1所示，该实施例的智慧物联网综合感知系统包括有数据感知层1、连接与传输层2、边缘计算层3、云计算层4 和应用层5，连接与传输层2分别与数据感知层1和边缘计算层3相连，云计算层4则分别与边缘计算层3和应用层5相连。其中：
数据感知层1由多个(本实施例中“多个”的含义是指包括至少两个，即两个以及两个以上)传感器设备节点所形成，各传感器设备节点分别对应监测不同的环境数据；并且，在该数据感知层中，各传感器设备节点之间采用如下步骤1～8的自组网方法实现数据上传，以及各传感器设备节点按照如下步骤b1～b4的节点监测方式实现节点故障监测和上报处理，这些传感器设备节点可以是超声波传感器、温度传感器、湿度传感器、气体传感器、光照传感器、烟雾传感器、大气压力传感器或者声音传感器等。
具体地，该自组网方法包括如下步骤1～8：
步骤1，在每一个传感器设备节点内预先存储直连节点列表；其中，任一个传感器设备节点内的直连节点列表包括与该任一个传感器设备节点直接相连的传感器设备节点、该传感器设备节点的节点地址以及经该传感器设备节点直接转发的数据的下一跳的传感器设备节点的节点地址；
步骤2，将数据感知层内的任一个传感器设备节点作为自组网的起始节点，并且对该任一个传感器设备节点做标记化处理；其中，标记化处理包括将该任一个传感器设备节点的状态标记为已入网、将该任一个传感器设备节点的直连节点列表初始化为零以及将该任一个传感器设备节点的节点地址设置为边缘设备地址；
步骤3，任一个新节点加入到数据感知层内时，该任一个新节点对其自身通信范围内的已入网节点做搜索判断处理：
当不存在已入网节点时，转入步骤4；否则，转入步骤5；其中，此处所说的新节点指新的传感器设备节点；
步骤4，该任一个新节点对其自身通信范围内的已入网节点再次持续搜索预设时间，例如此处再次搜索预设时间设置为5～10s，并且根据该再次搜索结果做判断处理：
当不存在已入网节点时，说明数据感知层中没有已入网节点，或者是可能存在的已入网节点由于故障不能通信，此时就转入步骤2；否则，转入步骤5；
步骤5，该任一个新节点锁定存在的该已入网节点，并且与其锁定的该已入网节点执行交互；
步骤6，对该任一个新节点与该已入网节点能否建立连接关系做出判断处理：
当两者能建立连接关系时，说明该已入网节点是工作正常的，该已入网节点可以执行数据包的接收和转发工作，该任一个新节点可以将该已入网节点设为数据发送的目标，从而完成数据上传的功能，将该任一个新节点状态标记为已入网，即该任一个新节点已经加入到了数据感知层，并且将其锁定的该已入网节点的节点地址设置为经过该任一个新节点直接转发的数据的下一跳的传感器设备节点的节点地址，转入步骤7；否则，步骤8；
步骤7，已入网的所有传感器设备节点分别将各自监测到的环境数据经连接与传输层上传至边缘计算设备；其中，未长传环境数据的传感器设备节点处于休眠状态；
步骤8，该任一个新节点放弃该已入网节点，并且转入步骤3，以对其自身通信范围内的已入网节点再次做搜索判断处理。
另外，该实施例中的传感器设备节点所执行的节点监测方式包括如下步骤b1～b4：
步骤b1，在每一个传感器设备节点内预先存储节点监测列表；其中，任一个传感器设备节点内的节点监测列表包括该任一个传感器设备节点所需要监测的传感器设备节点序列，传感器设备节点序列内至少包括一个需要该任一个传感器设备节点监测的传感器设备节点；
步骤b2，数据感知层中的任一个传感器设备节点执行节点监测时，该任一个传感器设备节点向其存储的节点监测列表内传感器设备节点序列中的所有传感器设备节点发送探测帧；
步骤b3，节点监测列表内传感器设备节点序列中的传感器设备节点接收到探测帧后，将发送确认帧给发送该探测帧的传感器设备节点；
步骤b4，发送探测帧的该任一个传感器设备节点根据接收到传感器设备节点序列中各传感器设备节点反馈的确认帧情况做出判断处理：
当该任一个传感器设备节点连续N次未收到传感器设备节点序列中任一传感器设备节点的确认帧时，判定传感器设备节点序列中的该传感器设备节点出现故障；否则，判定传感器设备节点序列中的该传感器设备节点未出现故障；其中，N≥2且为正整数；
连接与传输层2，由多个无线通信模块所形成，每一个传感器设备节点上至少设置有一个无线通信模块，以由位于所有传感器设备节点上的无线通信模块一起形成无线传感网络，连接与传输层与数据感知层相连；其中，连接与传输层接收数据感知层发送来的环境监测数据，该连接与传输层内的所有无线通信模块之间采用自学习拓扑控制机制和层级协同感知节点监测机制；
边缘计算层3，由多个边缘计算设备所形成，边缘计算层与连接与传输层相连，接收连接与传输层发送来的环境监测数据；其中，该实施例中的边缘计算设备按照如下步骤a1～a6的方式对该系统内所有传感器设备节点之间的拓扑关系做自学习拓扑控制：
步骤a1，边缘设备获取加入到该系统内的新传感器设备节点在数据感知层中的连接关系，并且刷新该边缘设备中的全局传感器网络拓扑信息；其中，该全局传感器网络拓扑信息为数据感知层中所有传感器设备节点之间的当前拓扑连接关系；
步骤a2，边缘设备对数据感知层中所有传感器设备节点的拓扑连接关系做优化处理，并产生新拓扑连接关系；
步骤a3，边缘设备将该新拓扑连接关系与所述当前拓扑连接关系做比较，得到拓扑连接关系差别，并且将该拓扑连接关系差别作为拓扑关系更改命令信息发送给需要更改 拓扑连接关系的传感器设备节点；
步骤a4，针对每一条拓扑关系更改命令信息，边缘设备通过Dijkstra算法(即戴克斯特拉算法)计算各拓扑关系更改命令信息所对应传感器设备节点到该边缘设备的最优路径；
步骤a5，接收到拓扑关系更改命令信息的传感器设备节点判断自身在其所对应最优路径中的位置，并且根据判断出的该位置做出处理：
当该传感器设备节点为其所对应最优路径中的非最后一个节点时，该传感器设备节点将该最优路径转发给其后一个位置的传感器设备节点，由该后一个位置的传感器设备节点执行与该传感器设备节点相同的自身位置判断处理操作，直到传输到该最优路径的最后一个节点，即目的节点，完成对该传感器设备节点的拓扑关系的自学习拓扑控制；否则，该传感器设备节点根据该拓扑关系更改命令信息来更改自身的直连节点列表以及更改经该传感器设备节点直接转发的数据的下一跳的传感器设备节点的节点地址，以完成对该传感器设备节点的拓扑关系的自学习拓扑控制；其中，拓扑关系更改命令信息中将给出对应目的节点的新的直连节点列表，用该新列表直接覆盖掉目的节点中的该列表，以及，拓扑关系更改命令信息中给出的新节点地址覆盖原节点地址；
步骤a6，针对数据感知层中的所有传感器设备节点，依次执行步骤a4～a5，完成对该系统内所有传感器设备节点之间的拓扑关系做自学习拓扑控制。
云计算层4，由云计算中心所形成，云计算层与边缘计算层相连，该云计算中心根据边缘计算层内各边缘计算设备所提供的数据实现数据融合处理，分别形成针对不用应用设备的动作指令；
应用层5，由多个应用设备所形成，应用层与云计算层相连，各应用设备分别接收云计算中心的动作指令，以执行与自身所接收动作指令相对应的动作。
需要说明的是，在该实施例中，边缘计算层3上的边缘设备按照如下步骤c1～c4的方法完成对数据感知层的监测网络拓扑的部署：
步骤c1，边缘设备将数据感知层中的所有传感器设备节点分为不同的层级；其中，此处层级的划分可以根据在数据上传过程中，节点与边缘设备地址的跳数和该节点的直连子节点数(即有多少个节点将该节点设为“下一跳”的地址)进行划分，例如，假设某一个节点离边缘设备地址跳数越少，并且该某一个节点的直连子节点数越多，就认为该某一个节点越重要，然后该某一个节点的层级也就越高；
步骤c2，边缘设备根据各传感器设备节点的通信范围，生成与数据感知层相对应的监测网络拓扑关系；其中，传感器设备节点对其通信范围内的同层级的传感器设备节点进行监测；
步骤c3，边缘设备根据生成的监测网络拓扑关系，生成针对每一个传感器设备节点的改变节点监测列表命令；其中，生成的监测网络拓扑关系会给出每个节点需要监测节 点，即节点监测列表；针对每个节点，边缘设备会对比该节点的新节点监测列表和原来的节点监测列表是否相同：如果相同，则不给该节点发送命令；否则，则将新的节点监测列表置于改变节点监测列表命令中，然后从节点拓扑连接关系中获取最优路径，根据该最优路径将监测列表命令发送至该节点；
步骤c4，边缘设备将改变节点监测列表命令发送给数据感知层中的所有传感器设备节点，完成监测网络拓扑的部署。
在实际的智慧物联网综合感知过程中，边缘设备通过执行上述步骤c1～c4，实际上完成了层级协同感知节点监测机制，各节点之间互相监测其状态，即采用协同感知的方式，可以全面、及时地发现系统中的故障节点并快速上报，提高节点监测效率，以维持监测系统的正常运行。该实施例还提供了一种利用上述智慧物联网综合感知系统所实现的智慧物联网综合感知方法。具体地，参见图2所示，该实施例的智慧物联网综合感知方法，包括如下步骤：
步骤S1，数据感知层中的各传感器设备节点分别监测环境数据，并且将各自监测到的环境监测数据经连接与传输层发送给边缘计算层；其中，数据感知层中的各传感器设备节点按照如下步骤b1～b4的节点监测方式实现节点故障监测和上报处理：
步骤b1，在每一个传感器设备节点内预先存储节点监测列表；其中，任一个传感器设备节点内的节点监测列表包括该任一个传感器设备节点所需要监测的传感器设备节点序列，传感器设备节点序列内至少包括一个需要该任一个传感器设备节点监测的传感器设备节点；
步骤b2，数据感知层中的任一个传感器设备节点执行节点监测时，该任一个传感器设备节点向其存储的节点监测列表内传感器设备节点序列中的所有传感器设备节点发送探测帧；
步骤b3，节点监测列表内传感器设备节点序列中的传感器设备节点接收到探测帧后，将发送确认帧给发送该探测帧的传感器设备节点；
步骤b4，发送探测帧的该任一个传感器设备节点根据接收到传感器设备节点序列中各传感器设备节点反馈的确认帧情况做出判断处理：
当该任一个传感器设备节点连续N次未收到传感器设备节点序列中任一传感器设备节点的确认帧时，判定传感器设备节点序列中的该传感器设备节点出现故障；否则，判定传感器设备节点序列中的该传感器设备节点未出现故障；其中，N≥2且为正整数；
步骤S2，边缘计算层根据接收到的经连接与传输层发送来的环境监测数据做出处理，并且将融合后所得数据发送给云计算层；其中，边缘计算层对该智慧物联网综合感知系统内所有传感器设备节点之间的拓扑关系执行自学习拓扑控制；具体地，边缘计算设备按照如下步骤a1～a6的方式对该系统内所有传感器设备节点之间的拓扑关系做自学习拓扑控制：
步骤a1，边缘设备获取加入到该系统内的新传感器设备节点在数据感知层中的连接关系，并且刷新该边缘设备中的全局传感器网络拓扑信息；其中，该全局传感器网络拓扑信息为数据感知层中所有传感器设备节点之间的当前拓扑连接关系；
步骤a2，边缘设备对数据感知层中所有传感器设备节点的拓扑连接关系做优化处理，并产生新拓扑连接关系；
步骤a3，边缘设备将该新拓扑连接关系与所述当前拓扑连接关系做比较，得到拓扑连接关系差别，并且将该拓扑连接关系差别作为拓扑关系更改命令信息发送给需要更改拓扑连接关系的传感器设备节点；
步骤a4，针对每一条拓扑关系更改命令信息，边缘设备计算各拓扑关系更改命令信息所对应传感器设备节点到该边缘设备的最优路径；
步骤a5，接收到拓扑关系更改命令信息的传感器设备节点判断自身在其所对应最优路径中的位置，并且根据判断出的该位置做出处理：
当该传感器设备节点为其所对应最优路径中的非最后一个节点时，该传感器设备节点将该最优路径转发给其后一个位置的传感器设备节点，完成对该传感器设备节点的拓扑关系的自学习拓扑控制；否则，该传感器设备节点根据该拓扑关系更改命令信息更改自身的直连节点列表以及经该传感器设备节点直接转发的数据的下一跳的传感器设备节点的节点地址，完成对该传感器设备节点的拓扑关系的自学习拓扑控制；
步骤a6，针对数据感知层中的所有传感器设备节点，依次执行步骤a4～a5，完成对该系统内所有传感器设备节点之间的拓扑关系做自学习拓扑控制；
步骤S3，云计算层根据边缘计算层内各边缘计算设备所提供的数据实现数据融合处理，生成提供给应用设备的动作指令；
步骤S4，应用层根据云计算层发送来的动作指令，执行与接收到的该动作指令相对应的动作。
需要说明的是，该实施例的边缘计算设备通过执行自学习拓扑控制机制，可以随时针对新的节点状态，对网络拓扑进行优化，从而实现网络拓扑的自动动态调整，使监测系统保持良好的工作状态，避免实际部署中物联网监测系统内节点随时发生变化或者新节点接入该监测系统而引起的节点状态变化问题；不仅如此，通过执行自学习拓扑控制机制，还可以减小数据包平均传输长度，使数据包的平均传输长度维持在最优状态，均衡能量以及延长监测系统中的各节点的寿命，确保整个物联网监测系统所构建节点网络的维持寿命。
为了提高智慧物联网综合感知系统中的数据感知层抵抗网络攻击的能力，确保数据传输的效率和可靠性，该实施例的智慧物联网综合感知方法还包括：对数据感知层中所有传感器设备节点之间的当前拓扑结构执行鲁棒性能提高优化，以输出得到最优拓扑结构的操作。具体地，在该实施例中，对数据感知层中所有传感器设备节点之间的当前拓 扑结构执行鲁棒性能提高优化，以输出得到最优拓扑结构的操作包括如下步骤d1～d7：
步骤d1，基于无标度网络模型的规则生成初始化的物联网拓扑结构，并在该物联网拓扑结构内随机部署多个网络拓扑节点；其中，在该初始化的物联网拓扑结构中，每一个网络拓扑节点(即数据感知层中的传感器设备节点)分别对应有一个固定的地理位置，并且所有的网络拓扑节点具有相同的属性；并且，加入物联网拓扑结构中的新网络拓扑节点连接之前网络拓扑节点的概率与该之前网络拓扑节点的度数大小呈正相关；
例如，假设在该初始化的物联网拓扑结构中，随机部署了M个网络拓扑节点，第m个网络拓扑节点标记为G m，该网络拓扑节点的地理位置坐标是
Figure PCTCN2021116815-appb-000001
步骤d2，按照网络Motif，在初始化的物联网拓扑结构中提取出所有符合4个节点的网络Motif，且将提取到的每一个网络Motif分别作为物联网拓扑结构优化过程中的最小操作单元；其中，在该技术领域中，网络Motif或称Motif是一个本领域技术人员熟知的技术术语，Motif是指一种类型的子图，该子图在复杂网络中发现的某种相互连接的模式个数显著高于随机网络中该某种相互连接的模式个数。此处所说的符合4个节点的网络Motif就是指，由4个节点(即四个网络拓扑节点)所组成的无向图；
假设经过该步骤2针对符合4个节点的网络Motif的提取操作，得到了Q个符合4个节点的网络Motif，第q个符合4个节点的网络Motif标记为Motif q，1≤q≤Q，每一个Motif q均被作为物联网拓扑结构优化过程中的最小操作单元，此处的最小操作单元标记为unit q，即unit q＝Motif q；
步骤d3，在提取的所有最小操作单元中选取出所有具有可重连接边的最小操作单元作为操作单元；
通过步骤2的提取操作，假设得到了Q个最小操作单元unit 1～unit Q，然后该步骤3再在这Q个最小操作单元中选取出所有具有可重连接边的最小操作单元作为操作单元，假设选出的所有具有可重连接边的最小操作单元分别是unit 1、unit 3、unit 4和unit 5，那么，此处就再将最小操作单元unit 1作为操作单元Unit 1、将最小操作单元unit 3作为操作单元Unit 3、将最小操作单元unit 4作为操作单元Unit 4以及将最小操作单元unit 5作为操作单元Unit 5；
步骤d4，对在初始化的物联网拓扑结构中已提取的部分操作单元更改边连接关系，且将更改了边连接关系后的物联网拓扑结构作为新物联网拓扑结构；
假设该实施例中的初始化的物联网拓扑结构标记为C 0，然后针对上述已选出的四个操作单元中的操作单元Unit 4和操作单元Unit 5这两个操作单元执行更改边连接关系的第一次更改操作，如此，初始化的物联网拓扑结构C 0在经过这次更改操作后，拓扑结构就会发生变化，然后此时就将更改了边连接关系后的物联网拓扑结构作为新物联网拓扑结构，并且将该第一次更改操作后得到的该新物联网拓扑结构标记为C 1；
步骤d5，分别多次重复执行步骤d4的操作，得到多个新物联网拓扑结构，且由该多个新物联网拓扑结构组成一个种群；其中，每次操作中针对边连接关系的更改操作不完全相同，且将该种群中的每一个新物联网拓扑结构作为一个个体；
然后，按照上述步骤d4所示例的，在针对初始化的物联网拓扑结构C 0执行针对部分操作单元的第二次更改操作，并且将该第一次更改操作后得到的该新物联网拓扑结构标记为C 2；假设经过了5次更改操作，并且每次操作均不完全相同，就会得到5个新物联网拓扑结构，分别是新物联网拓扑结构C 1、新物联网拓扑结构C 2、新物联网拓扑结构C 3、新物联网拓扑结构C 4和新物联网拓扑结构C 5，并且再由这5个新物联网拓扑结构C 1～C 5一起组成一个种群S，S＝{C 1,C 2,C 3,C 4,C 5}；这样，该种群S中的每一个新物联网拓扑结构C 1～C 5作为一个个体；
步骤d6，构建衡量物联网拓扑结构鲁棒性能的鲁棒性能衡量指标；其中，此处鲁棒性能衡量指标的构建过程包括如下步骤d61～d65：
步骤d61，对初始化的物联网拓扑结构在遭受每次网络攻击后的物联网拓扑结构中所包含符合3个节点的网络Motif数量做累计统计；
步骤d62，获取初始化的物联网拓扑结构的总边数以及该初始化的物联网拓扑结构的网络拓扑节点总数量；其中，初始化的物联网拓扑结构的总边数标记为E，该初始化的物联网拓扑结构的网络拓扑节点总数量标记为V，V>3；
步骤d63，获取经第k次网络攻击后，物联网拓扑结构中的所有符合3个节点的网络Motif所组成的并集的总边数；其中，此处的该所有符合3个节点的网络Motif所组成的并集的总边数标记为MC(k)，k≥1；
需要说明的是，在该步骤d63中，物联网拓扑结构经过第k次网络攻击，我们将所有符合3个节点的网络motif的边集进行并集操作，然后去除重复边，得到一个网络拓扑结构，统计该网络拓扑结构包含的边数，统计所得到的该边数就是此处的该所有符合 3个节点的网络Motif所组成的并集的总边数MC(k)；
步骤d64，根据统计所得符合3个节点的网络Motif数量做出判断处理：
当符合3个节点的网络Motif数量为零时，转入步骤d65；否则，转入步骤d61；
步骤d65，对统计所得符合3个节点的网络Motif数量做归一化，并且将归一化后所得到的数值作为所述的鲁棒性能衡量指标；其中，鲁棒性能衡量指标标记为I：
Figure PCTCN2021116815-appb-000002
步骤d7，利用分布式人工免疫优化算法对形成的该种群内每一个新物联网拓扑结构做优化，将具有最优鲁棒性能衡量指标的物联网拓扑结构作为最优物联网拓扑结构输出。其中，此处具有最优鲁棒性能衡量指标的物联网拓扑结构作为最优物联网拓扑结构的输出过程包括如下步骤d71～d77：
步骤d71，设置N个本地优化程序和1个全局优化程序；其中，各本地优化程序之间相互独立，每一个本地程序运行一个种群P，且每一个本地程序对其运行的种群P分别进行种群交叉操作、变异操作和选择操作，第n个本地优化程序标记为L n，1≤n≤N，全局优化程序标记为GL；
步骤d72，定义交叉操作策略：
motif i,motif j←G i(,loc),G j(,loc)；
其中，G i(,loc)和G j(,loc)分别表示同一个种群中的相互交叉位置在loc处的两个不同个体，并且选择交叉位置染色体短的一侧进行搜索，染色体是由所有符合4个节点的网络Motif组成的，且将该染色体中的其中一个Motif称之为Motif碱基，motif i表示在一个类型个体中的其中一个符合4个节点的网络Motif，motif j表示在与motif i所处个体类型相同的另一个类型个体中，其中的一个符合4个节点的网络Motif且与motif i可交叉的网络Motif；
对两个网络Motifmotif i与motif j的类型做出判断处理：当motif i与motif j为同类型时，对该两个网络Motif执行交叉操作；否则，继续在同一个种群中搜索可交叉操作的两个网络Motif；
步骤d73，定义变异操作策略：
针对种群P中的一个个体G，提取其所有符合4个网络Motif的操作单元组成一个染色体；
随机指定部分可变异的染色体Motif碱基位置；其中，如果该染色体Motif碱基位置为具有可重复连接边关系的Motif，则进行重连边；否则，继续随机指定下一个碱基位置做判断；
步骤d74，定义选择操作策略：
P GL＝{L r,L t,L,L z}；
其中，P GL表示全局优化程序GL运行的种群，该种群P GL由本体优化程序GL分别对应采用不同的选择策略选出来的精英种群个体L r、L t、…、L z所组成；当本地优化程序GL执行完交叉变异操作后，计算每一个个体的鲁棒性能指标，并且，选择鲁棒性能指标值最大的2个精英个体传送到全局优化程序GL中，由全局优化程序GL接收精英种群个体，再继续进行优化操作；同时，全局优化程序GL设置了一个通信队列Q存放本地优化程序选择出来的精英种群个体；其中，精英种群个体是在在一个种群里面具有最大鲁棒性能指标的个体，也即最好的物联网拓扑结构；
在全局优化程序GL中，初始的全局优化程序直接选择通信队列Q中的2个种群；然后，从该通信队列Q中选出一个种群个体；
如果选出的该种群个体的鲁棒性能衡量指标优于全局种群的鲁棒性能衡量指标平均值，则将该种群个体选出来；否则，继续选择该通信队列Q中的下一个种群个体；
步骤d75，定义“联邦-州”通信机制与全局优化机制：
在全局优化程序GL设置一个通信队列Q存放本地优化程序选择出来的精英种群个体；以及，在每次迭代过程中，全局优化程序GL从通信队列Q中选择一个精英种群个体，并且将选择的该精英种群个体替换掉全局优化程序GL所对应原始种群中具有最低鲁棒性能衡量指标的种群个体；此处所说的“迭代”为重复执行步骤d7的操作；
步骤d76，对输出的鲁棒性能衡量指标和迭代次数做出判读处理：
当输出的鲁棒性能衡量指标的浮动范围位于预设浮动范围内，例如预设浮动范围不高于0.001，并且当前已执行的迭代次数未超过预设的最大迭代次数时(例如将该预设的最大迭代次数设置为1000次)，保存该鲁棒性能衡量指标，转入步骤d77；否则，继续执行迭代，直达已执行的迭代次数达到预设的最大迭代次数时，终止迭代过程；
步骤d77，将保存的该鲁棒性能衡量指标所对应的物联网拓扑结构作为具有最优鲁棒性能衡量指标的物联网拓扑结构。
需要说明的是，传统的物联网拓扑结构优化方案通常采用利用集中式计算方式的遗传优化算法，存在计算开销大、种群多样性差且容易陷入早熟收敛状态的缺点。不同于传统遗传算法的个体组成，该实施例中采用符合4个节点的网络Motif作为个体(即每一个新物联网拓扑结构)的基因组成，减少后续交叉和变异的搜索开销，而且该发明采用分布式人工免疫算法，可以降低计算开销，提高种群多样性，更快地搜索到全局最优解(即具有最优鲁棒性能衡量指标的物联网拓扑结构)，实现了在充分衡量网络拓扑结构基础上，有效地提高物联网拓扑结构抵抗恶意攻击的能力，降低了物联网因遭受攻击被瘫痪的风险，进而确保数据传输效率和可靠性。
尽管以上详细地描述了本发明的优选实施例，但是应该清楚地理解，对于本领域的技术人员来说，本发明可以有各种更改和变化。凡在本发明的精神和原则之内所作的任何修改、等同替换、改进等，均应包含在本发明的保护范围之内。

Claims (10)

  1. 智慧物联网综合感知系统，其特征在于，包括：
    数据感知层(1)，由多个传感器设备节点所形成，各传感器设备节点分别对应监测不同的环境数据；
    连接与传输层(2)，由多个无线通信模块所形成，每一个传感器设备节点上至少设置有一个无线通信模块，以由位于所有传感器设备节点上的无线通信模块一起形成无线传感网络，连接与传输层(2)和数据感知层(1)相连；其中，连接与传输层接收数据感知层发送来的环境监测数据，该连接与传输层内的所有无线通信模块之间采用自学习拓扑控制机制和层级协同感知节点监测机制；
    边缘计算层(3)，由多个边缘计算设备所形成，边缘计算层(3)和连接与传输层(2)相连，接收连接与传输层发送来的环境监测数据；
    云计算层(4)，由云计算中心所形成，云计算层(4)与边缘计算层(3)相连，该云计算中心根据边缘计算层内各边缘计算设备所提供的数据实现数据融合处理，分别形成针对不同应用设备的动作指令；以及，
    应用层(5)，由多个应用设备所形成，应用层(5)与云计算层(4)相连，各应用设备分别接收云计算中心的动作指令，以执行与自身所接收动作指令相对应的动作。
  2. 根据权利要求1所述的智慧物联网综合感知系统，其特征在于，所述数据感知层(1)中的各传感器设备节点之间采用自组网方法实现数据上传；其中，该自组网方法包括如下步骤1～8：
    步骤1，在每一个传感器设备节点内预先存储直连节点列表；其中，任一个传感器设备节点内的直连节点列表包括与该任一个传感器设备节点直接相连的传感器设备节点、该传感器设备节点的节点地址以及经该传感器设备节点直接转发的数据的下一跳的传感器设备节点的节点地址；
    步骤2，将数据感知层内的任一个传感器设备节点作为自组网的起始节点，并且对该任一个传感器设备节点做标记化处理；其中，标记化处理包括将该任一个传感器设备节点的状态标记为已入网、将该任一个传感器设备节点的直连节点列表初始化为零以及将该任一个传感器设备节点的节点地址设置为边缘设备地址；
    步骤3，任一个新节点加入到数据感知层内时，该任一个新节点对其自身通信范围内的已入网节点做搜索判断处理：
    当不存在已入网节点时，转入步骤4；否则，转入步骤5；
    步骤4，该任一个新节点对其自身通信范围内的已入网节点再次持续搜索预设时间，并且根据该再次搜索结果做判断处理：
    当不存在已入网节点时，该任一个新节点将自身标记为自组网的起始节点，转入步 骤2；否则，转入步骤5；
    步骤5，该任一个新节点锁定存在的该已入网节点，并且与其锁定的该已入网节点执行交互；
    步骤6，对该任一个新节点与该已入网节点能否建立连接关系做出判断处理：
    当两者能建立连接关系时，将该任一个新节点状态标记为已入网，并且将其锁定的该已入网节点的节点地址设置为经过该任一个新节点直接转发的数据的下一跳的传感器设备节点的节点地址，转入步骤7；否则，步骤8；
    步骤7，已入网的所有传感器设备节点分别将各自监测到的环境数据经连接与传输层上传至边缘计算设备；其中，未上传环境数据的传感器设备节点处于休眠状态；
    步骤8，该任一个新节点放弃该已入网节点，并且转入步骤3。
  3. 根据权利要求1所述的智慧物联网综合感知系统，其特征在于，所述边缘计算设备按照如下步骤a1～a6的方式对该系统内所有传感器设备节点之间的拓扑关系做自学习拓扑控制：
    步骤a1，边缘设备获取加入到该系统内的新传感器设备节点在数据感知层中的连接关系，并且刷新该边缘设备中的全局传感器网络拓扑信息；其中，该全局传感器网络拓扑信息为数据感知层中所有传感器设备节点之间的当前拓扑连接关系；
    步骤a2，边缘设备对数据感知层中所有传感器设备节点的拓扑连接关系做优化处理，并产生新拓扑连接关系；
    步骤a3，边缘设备将该新拓扑连接关系与所述当前拓扑连接关系做比较，得到拓扑连接关系差别，并且将该拓扑连接关系差别作为拓扑关系更改命令信息发送给需要更改拓扑连接关系的传感器设备节点；
    步骤a4，针对每一条拓扑关系更改命令信息，边缘设备计算各拓扑关系更改命令信息所对应传感器设备节点到该边缘设备的最优路径；
    步骤a5，接收到拓扑关系更改命令信息的传感器设备节点判断自身在其所对应最优路径中的位置，并且根据判断出的该位置做出处理：
    当该传感器设备节点为其所对应最优路径中的非最后一个节点时，该传感器设备节点将该最优路径转发给其后一个位置的传感器设备节点，完成对该传感器设备节点的拓扑关系的自学习拓扑控制；否则，该传感器设备节点根据该拓扑关系更改命令信息来更改自身的直连节点列表，以及经该传感器设备节点直接转发的数据的下一跳的传感器设备节点的节点地址来完成对该传感器设备节点的拓扑关系的自学习拓扑控制；
    步骤a6，针对数据感知层中的所有传感器设备节点，依次执行步骤a4～a5，完成对该系统内所有传感器设备节点之间的拓扑关系做自学习拓扑控制。
  4. 根据权利要求1所述的智慧物联网综合感知系统，其特征在于，所述数据感知 层中的各传感器设备节点按照如下步骤b1～b4的节点监测方式实现节点故障监测和上报处理：
    步骤b1，在每一个传感器设备节点内预先存储节点监测列表；其中，任一个传感器设备节点内的节点监测列表包括该任一个传感器设备节点所需要监测的传感器设备节点序列，传感器设备节点序列内至少包括一个需要该任一个传感器设备节点监测的传感器设备节点；
    步骤b2，数据感知层中的任一个传感器设备节点执行节点监测时，该任一个传感器设备节点向其存储的节点监测列表内传感器设备节点序列中的所有传感器设备节点发送探测帧；
    步骤b3，节点监测列表内传感器设备节点序列中的传感器设备节点接收到探测帧后，将发送确认帧给发送该探测帧的传感器设备节点；
    步骤b4，发送探测帧的该任一个传感器设备节点根据接收到传感器设备节点序列中各传感器设备节点反馈的确认帧情况做出判断处理：
    当该任一个传感器设备节点连续N次未收到传感器设备节点序列中任一传感器设备节点的确认帧时，判定传感器设备节点序列中的该传感器设备节点出现故障；否则，判定传感器设备节点序列中的该传感器设备节点未出现故障；其中，N≥2。
  5. 根据权利要求1所述的智慧物联网综合感知系统，其特征在于，所述边缘设备按照如下步骤c1～c4的方法完成对数据感知层的监测网络拓扑的部署：
    步骤c1，所述边缘设备将数据感知层中的所有传感器设备节点分为不同的层级；
    步骤c2，所述边缘设备根据各传感器设备节点的通信范围，生成与数据感知层相对应的监测网络拓扑关系；其中，传感器设备节点对其通信范围内的同层级的传感器设备节点进行监测；
    步骤c3，所述边缘设备根据生成的监测网络拓扑关系，生成针对每一个传感器设备节点的改变节点监测列表命令；
    步骤c4，所述边缘设备将改变节点监测列表命令发送给数据感知层中的所有传感器设备节点，完成监测网络拓扑的部署。
  6. 智慧物联网综合感知方法，采用如权利要求1所述的智慧物联网综合感知系统，其特征在于，包括如下步骤：
    数据感知层中的各传感器设备节点分别监测环境数据，并且将各自监测到的环境监测数据经连接与传输层发送给边缘计算层；
    边缘计算层根据接收到的经连接与传输层发送来的环境监测数据做出处理，并且将融合后所得数据发送给云计算层；其中，边缘计算层对该智慧物联网综合感知系统内所有传感器设备节点之间的拓扑关系执行自学习拓扑控制；
    云计算层根据边缘计算层内各边缘计算设备所提供的数据实现数据融合处理，生成提供给应用设备的动作指令；
    以及，应用层的应用设备根据云计算层发送来的动作指令，执行与接收到的该动作指令相对应的动作。
  7. 根据权利要求6所述的智慧物联网综合感知方法，其特征在于，所述边缘计算设备按照如下步骤a1～a6的方式对该系统内所有传感器设备节点之间的拓扑关系做自学习拓扑控制：
    步骤a1，边缘设备获取加入到该系统内的新传感器设备节点在数据感知层中的连接关系，并且刷新该边缘设备中的全局传感器网络拓扑信息；其中，该全局传感器网络拓扑信息为数据感知层中所有传感器设备节点之间的当前拓扑连接关系；
    步骤a2，边缘设备对数据感知层中所有传感器设备节点的拓扑连接关系做优化处理，并产生新拓扑连接关系；
    步骤a3，边缘设备将该新拓扑连接关系与所述当前拓扑连接关系做比较，得到拓扑连接关系差别，并且将该拓扑连接关系差别作为拓扑关系更改命令信息发送给需要更改拓扑连接关系的传感器设备节点；
    步骤a4，针对每一条拓扑关系更改命令信息，边缘设备计算各拓扑关系更改命令信息所对应传感器设备节点到该边缘设备的最优路径；
    步骤a5，接收到拓扑关系更改命令信息的传感器设备节点判断自身在其所对应最优路径中的位置，并且根据判断出的该位置做出处理：
    当该传感器设备节点为其所对应最优路径中的非最后一个节点时，该传感器设备节点将该最优路径转发给其后一个位置的传感器设备节点，完成对该传感器设备节点的拓扑关系的自学习拓扑控制；否则，该传感器设备节点根据该拓扑关系更改命令信息来更改自身的直连节点列表，以及经该传感器设备节点直接转发的数据的下一跳的传感器设备节点的节点地址来完成对该传感器设备节点的拓扑关系的自学习拓扑控制；
    步骤a6，针对数据感知层中的所有传感器设备节点，依次执行步骤a4～a5，完成对该系统内所有传感器设备节点之间的拓扑关系做自学习拓扑控制。
  8. 根据权利要求6所述的智慧物联网综合感知方法，其特征在于，还包括：所述边缘设备按照如下步骤c1～c4的方法完成对数据感知层的监测网络拓扑的部署：
    步骤c1，所述边缘设备将数据感知层中的所有传感器设备节点分为不同的层级；
    步骤c2，所述边缘设备根据各传感器设备节点的通信范围，生成与数据感知层相对应的监测网络拓扑关系；其中，传感器设备节点对其通信范围内的同层级的传感器设备节点进行监测；
    步骤c3，所述边缘设备根据生成的监测网络拓扑关系，生成针对每一个传感器设备 节点的改变节点监测列表命令；
    步骤c4，所述边缘设备将改变节点监测列表命令发送给数据感知层中的所有传感器设备节点，完成监测网络拓扑的部署。
  9. 根据权利要求6所述的智慧物联网综合感知方法，其特征在于，还包括：所述数据感知层中的各传感器设备节点按照如下步骤b1～b4的节点监测方式实现节点故障监测和上报处理：
    步骤b1，在每一个传感器设备节点内预先存储节点监测列表；其中，任一个传感器设备节点内的节点监测列表包括该任一个传感器设备节点所需要监测的传感器设备节点序列，传感器设备节点序列内至少包括一个需要该任一个传感器设备节点监测的传感器设备节点；
    步骤b2，数据感知层中的任一个传感器设备节点执行节点监测时，该任一个传感器设备节点向其存储的节点监测列表内传感器设备节点序列中的所有传感器设备节点发送探测帧；
    步骤b3，节点监测列表内传感器设备节点序列中的传感器设备节点接收到探测帧后，将发送确认帧给发送该探测帧的传感器设备节点；
    步骤b4，发送探测帧的该任一个传感器设备节点根据接收到传感器设备节点序列中各传感器设备节点反馈的确认帧情况做出判断处理：
    当该任一个传感器设备节点连续N次未收到传感器设备节点序列中任一传感器设备节点的确认帧时，判定传感器设备节点序列中的该传感器设备节点出现故障；否则，判定传感器设备节点序列中的该传感器设备节点未出现故障；其中，N≥2。
  10. 根据权利要求6所述的智慧物联网综合感知方法，其特征在于，还包括对所述数据感知层中所有传感器设备节点之间的当前拓扑结构执行鲁棒性能提高优化，以输出得到最优拓扑结构的操作。
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