Communication integrated monitoring management system and method based on Internet of Things

Abstract

The invention relates to the technical field of Internet of Things communication, in particular to a communication integrated monitoring management system and method based on the Internet of Things, and the method comprises the steps: determining a to-be-selected node set of abnormal nodes, and obtaining an associated node set which has a communication relationship with the abnormal nodes and each to-be-selected node, determining the replaceability of each to-be-selected node based on the relation between the association node set of the abnormal nodes and the association node set of each to-be-selected node; according to the replaceability of each to-be-selected node and the network load condition of each to-be-selected node, the target to-be-selected node is screened from the to-be-selected node set to serve as the replacement node of the abnormal node, so that the acquisition accuracy of the replacement node of the abnormal node is improved, and the influence on the stable transmission of data of the Internet of Things is reduced as much as possible.

Description

Technical Field

[0001] This invention relates to the field of Internet of Things (IoT) communication technology, and specifically to an integrated communication monitoring and management system and method based on IoT. Background Technology

[0002] In an IoT system, data acquisition terminals, such as sensors, need to transmit the collected data to the IoT platform. If single-hop coverage is insufficient, intermediate nodes are needed to form a chain for data transmission. These nodes may include routers, gateways, or other relay devices, which are responsible for transmitting data from one network segment to another. After the data arrives at the IoT platform, it is stored and processed on a cloud platform or local server.

[0003] If nodes in a network link experience abnormalities or are maliciously attacked, it may be necessary to rely on neighboring nodes as replacement nodes for data transmission. However, there may be more than one neighboring node, and due to their own specific circumstances, such as high network load, they may be unsuitable as replacement nodes, or their performance might be negatively impacted, thus affecting the transmission efficiency of neighboring nodes. Current methods for selecting replacement nodes rely solely on the network load of neighboring nodes, or the IoT platform randomly assigns a neighboring node as a replacement node. This approach has low reliability and may fail to obtain an accurate replacement node, thereby affecting the stability of data transmission. Summary of the Invention

[0004] To address the technical problem of low reliability in existing replacement node selection methods, the present invention aims to provide an integrated communication monitoring and management system and method based on the Internet of Things (IoT). The specific technical solution adopted is as follows: In a first aspect of the present invention, a communication integrated monitoring and management method based on the Internet of Things is provided, comprising: Determine a set of candidate nodes for the abnormal node, wherein each candidate node in the set of candidate nodes has the potential to replace the abnormal node; Obtain the sets of associated nodes that have communication relationships with the abnormal node and each candidate node respectively, and determine the replaceability of each candidate node based on the relationship between the set of associated nodes of the abnormal node and the set of associated nodes of each candidate node. Based on the replaceability of each candidate node and the network load of each candidate node, a target candidate node is selected from the candidate node set and used as a replacement node for the abnormal node.

[0005] In an exemplary embodiment, before determining the candidate node set for abnormal nodes, the integrated communication monitoring and management method further includes: The network traffic time series of each node in the Internet of Things is decomposed to obtain the trend component curve and the seasonal component curve; Based on the seasonal component curves of each node, the degree of fluctuation in the data transmission cycle of each node is obtained; The trend component curves of each node are fitted with straight lines to obtain fitted straight lines, and the degree of trend change fluctuation of each node is obtained based on the difference between the trend component curves of each node and the fitted straight lines. The degree of network anomaly at each node is obtained based on the degree of fluctuation in the data transmission cycle and the degree of fluctuation in the trend change. The abnormal nodes are identified by filtering based on the degree of network anomaly at each node.

[0006] In one exemplary embodiment, the associated node set includes a receiving node set and a sending node set; The process of obtaining the candidate node set of the abnormal node includes: taking the nodes that overlap in the receiving node set and sending node set of the abnormal node as candidate nodes of the abnormal node, thus forming the candidate node set.

[0007] In an exemplary embodiment, determining the substitutability of each candidate node based on the relationship between the set of associated nodes of the abnormal node and the set of associated nodes of each candidate node includes: Obtain the intersection of the set of receiving nodes of the abnormal node and the set of receiving nodes of the first candidate node to obtain the first intersection; the first candidate node can be any candidate node. Obtain the intersection of the set of sending nodes of the abnormal nodes and the set of sending nodes of the first candidate nodes to obtain the second intersection; Based on the first intersection, the second intersection, the number of communications between the first candidate node and the corresponding set of receiving nodes, and the number of communications between the first candidate node and the corresponding set of sending nodes, the substitutability of the first candidate node is obtained.

[0008] In one exemplary embodiment, the formula for calculating substitutability is as follows: ; in, This indicates the replaceability of the v-th candidate node. M1 represents the number of receiving nodes in the first intersection corresponding to the v-th candidate node, and M1 represents the number of receiving nodes in the receiving node set of the abnormal node. M1 represents the number of sending nodes in the second intersection corresponding to the v-th candidate node, and M2 represents the number of sending nodes in the set of sending nodes of the abnormal node. This represents the sum of the number of communications between the v-th candidate node and each of the corresponding sending nodes in the set of sending nodes within the current preset time period. This represents the sum of the number of communications between the v-th candidate node and each receiving node in the corresponding set of receiving nodes within the current preset time period.

[0009] In an exemplary embodiment, the process of obtaining the network load status is as follows: Obtain the network traffic data between the first candidate node and its set of receiving nodes to obtain the first network traffic data; the first candidate node can be any candidate node. Obtain the network traffic data between the first candidate node and its sending node set to obtain the second network traffic data; The maximum value between the first network traffic data and the second network traffic data is taken as the network load of the first candidate node.

[0010] In an exemplary embodiment, the process of acquiring the first network traffic data includes: taking the sum of the network traffic data of the first candidate node and each receiving node in the set of receiving nodes as the first network traffic data; The process of obtaining the second network traffic data includes: taking the sum of the network traffic data of the first candidate node and each sending node in the set of sending nodes as the second network traffic data.

[0011] In an exemplary embodiment, the step of selecting target candidate nodes from the candidate node set based on the replaceability of each candidate node and the network load of each candidate node includes: The replacement probability of each candidate node is obtained based on the replaceability of each candidate node and the network load of each candidate node; the replacement probability is directly proportional to the replaceability and inversely proportional to the network load. The candidate node corresponding to the highest replacement probability is selected as the target candidate node.

[0012] In an exemplary embodiment, the process of obtaining the substitution possibilities includes: The replaceability of the first candidate node is normalized, and the network load of the first candidate node is negatively correlated and normalized. The replacement probability of the first candidate node is calculated by multiplying the normalized replaceability of the first candidate node by the negatively correlated normalized network load of the first candidate node.

[0013] In a second aspect of the present invention, an integrated communication monitoring and management system based on the Internet of Things is provided, comprising: a memory and a processor; the memory is connected to the processor; the memory is used to store program instructions; the processor is used to implement the above-described integrated communication monitoring and management method based on the Internet of Things when the program instructions are executed.

[0014] The present invention has the following beneficial effects: First, it is necessary to determine the set of candidate nodes for abnormal nodes. Then, based on the relationship between the abnormal nodes and the associated node sets of each candidate node, the replaceability of each candidate node is determined. At the same time, combined with the network load of each candidate node, the set of candidate nodes is comprehensively screened to obtain the replacement nodes for the abnormal nodes. In addition to considering the network load, the relationship between the abnormal nodes and the associated node sets of each candidate node is also considered, which improves the accuracy of obtaining the replacement nodes for abnormal nodes and minimizes the impact on the stable transmission of IoT data. Attached Figure Description

[0015] Figure 1 This is a flowchart of an integrated communication monitoring and management method based on the Internet of Things provided in one embodiment of the present invention; Figure 2 This is a flowchart of the process for obtaining abnormal nodes provided in one embodiment of the present invention; Figure 3 This is a schematic diagram of data component decomposition of the STL algorithm provided in an embodiment of the present invention; Figure 4 This is a flowchart illustrating the process of obtaining substitutability according to an embodiment of the present invention; Figure 5 This is a flowchart illustrating the process of obtaining the network load status of candidate nodes according to an embodiment of the present invention; Figure 6 This is a flowchart of the process for obtaining target candidate nodes provided in one embodiment of the present invention; Figure 7 This is a flowchart illustrating the process of obtaining replacement possibilities according to an embodiment of the present invention. Detailed Implementation

[0016] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the specific implementation methods, structures, features, and effects of the present invention are described in detail below with reference to the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0017] Unless otherwise defined, all technical and scientific terms used in this embodiment have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. All data and information collected in this application have been obtained with full consent, and the collection, use, and processing of such information must comply with the relevant laws, regulations, and standards of the relevant countries and regions.

[0018] This embodiment provides an integrated communication monitoring and management method based on the Internet of Things (IoT). The applicable scenario is as follows: In an IoT system, multiple sensors collect relevant data. Multiple network nodes are set up within the IoT system to transmit the collected data to the IoT platform through multiple hops. The IoT platform has a data processing center for data processing. These nodes may include routers, gateways, or other relay devices, responsible for transmitting data from one network segment to another. Data may require multiple hops before being uploaded to the IoT platform. If an abnormal node exists, a replacement node needs to be identified to replace the current abnormal node, ensuring continuous and normal data transmission.

[0019] During data transmission, depending on the data transmission path in each node, if there is direct data transmission between two nodes, it indicates that there is a connection between the two nodes.

[0020] The overall concept of the IoT-based integrated communication monitoring and management method provided in this embodiment is as follows: First, identify the network nodes that are abnormal; determine the data flow direction in the nodes according to the network topology, and simultaneously determine the data sending and receiving nodes of the abnormal nodes. Since the replacement node needs to replace the current abnormal node for data transmission, a set of candidate nodes is determined according to the data sending and receiving situation in the network. The current replacement node is determined according to the replaceability of the candidate node set and the network load situation, and the replacement node is used to replace the current abnormal node.

[0021] like Figure 1 As shown in the figure, the communication integrated monitoring and management method based on the Internet of Things provided in this embodiment includes the following steps: Step 1: Determine the set of candidate nodes for the abnormal node. Each candidate node in the set has the potential to replace the abnormal node.

[0022] Step 2: Obtain the set of associated nodes that have communication relationships with the abnormal node and each candidate node, and determine the replaceability of each candidate node based on the relationship between the set of associated nodes of the abnormal node and the set of associated nodes of each candidate node.

[0023] Step 3: Based on the replaceability of each candidate node and the network load of each candidate node, select the target candidate node from the candidate node set as the replacement node for the abnormal node.

[0024] The following is a detailed explanation of each step.

[0025] Step 1: Determine the set of candidate nodes for the abnormal node. Each candidate node in the set has the potential to replace the abnormal node.

[0026] Because sensor data in the Internet of Things (IoT) is sampled periodically, and data transmission follows a minimum hop count, the data generally remains relatively stable with a fixed cycle. Therefore, analyzing the data transmission cycle and stability of each node in the current IoT system can help identify nodes exhibiting abnormal behavior.

[0027] In one exemplary embodiment, before determining the candidate node set for abnormal nodes, it is necessary to first determine the abnormal nodes, such as... Figure 2 As shown, a specific process for determining abnormal nodes includes: Step 1-1: Decompose the time series of network traffic of each node in the Internet of Things to obtain the trend component curve and the seasonal component curve.

[0028] Set a current preset time period, with the current time as the end time of this period. The length of this time period can be set according to actual needs.

[0029] Obtain the network traffic time-series sequence of each node in the Internet of Things (IoT) within the current preset time period, and derive the corresponding network traffic curve based on the network traffic time-series sequence. Specifically, network traffic refers to data transmission traffic.

[0030] STL (Seasonal-Trend Decomposition using LOESS) is performed on the network traffic time series curves of each node to obtain the trend component curves and seasonal component curves of each node.

[0031] It should be understood that by applying the STL algorithm, network traffic curves are decomposed into trend components, seasonal components, and residual components. The trend component reflects long-term trends, the seasonal component reveals periodic changes, and the residual component includes random fluctuations in addition to the trend and seasonality components. Figure 3 The figure shows a schematic diagram of the data component decomposition of the STL algorithm, with the horizontal axis representing time and the vertical axis representing magnitude. Figure 3 The four curves shown are, from top to bottom, the original data curve, the trend component, the seasonal component, and the random fluctuation.

[0032] Step 1-2: Obtain the degree of fluctuation in the data transmission cycle of each node based on the seasonal component curve of each node.

[0033] Since sensor data is typically sampled periodically, network traffic data generally exhibits periodic variations. Furthermore, since the seasonal component curve reflects the periodic changes in the network traffic curve, the periodic stability of each node, i.e., the degree of fluctuation in the data transmission cycle, can be obtained based on the seasonal component curve of each node.

[0034] In an exemplary embodiment, for any given node, the maximum value in the seasonal component curve of that node is obtained, and the time interval between any two adjacent maximum values ​​is taken as a period, thereby obtaining the time interval of each period. Then, based on the fluctuation of the time interval of each period, the degree of fluctuation of the data transmission cycle is obtained.

[0035] The formula for calculating the degree of fluctuation in data transmission cycles is as follows: ; ; ; Where c represents the degree of fluctuation in the data transmission cycle. and Let represent the time points corresponding to the i-th and (i+1)-th maxima in the seasonal component curve, respectively. This represents the time interval between the i-th maximum and the (i+1)-th maximum, i.e., the period. The value represents the average value over the period, n represents the number of maxima, and n-1 represents the number of periods.

[0036] Steps 1-3: Perform linear fitting on the trend component curves of each node to obtain the fitted straight line, and obtain the degree of trend change fluctuation of each node based on the difference between the trend component curves and the fitted straight line.

[0037] Since the trend changes of the sensor are relatively stable, it is impossible to determine whether the data changes are in an upward, stable, or downward state. Therefore, it is necessary to perform linear fitting (such as least squares fitting) on ​​the trend component curves of each node to obtain the fitted line and determine the nature of the fitted line.

[0038] For any given node, the difference between the trend component curve and the fitted straight line is calculated. A larger difference indicates greater fluctuations in the trend, while a smaller difference indicates a more stable trend. Therefore, the degree of trend fluctuation at a node can be determined by the difference between the trend component curve and the fitted straight line.

[0039] In an exemplary embodiment, the difference between the amplitude of the trend component curve and the amplitude of the fitted line at each time point of the node (specifically, the absolute value of the difference) is obtained. That is, the absolute value of the difference between the vertical coordinate value of the trend component curve and the vertical coordinate value of the fitted line at each horizontal coordinate point of the node is obtained, and then the average value of the absolute value of the difference is calculated. This average value is the difference between the trend component curve and the fitted line of the node, that is, the degree of fluctuation of the trend change of the node.

[0040] Steps 1-4: Based on the degree of fluctuation in data transmission cycle and the degree of fluctuation in trend change, obtain the degree of network anomaly of each node.

[0041] The greater the fluctuation in data transmission cycle, the greater the network anomaly. Furthermore, the greater the fluctuation in trend change, the greater the network anomaly. Therefore, the network anomaly level of each node can be obtained based on the fluctuation in its data transmission cycle and the fluctuation in its trend change. The network anomaly level is directly proportional to both the fluctuation in data transmission cycle and the fluctuation in trend change.

[0042] In one exemplary embodiment, the formula for calculating the degree of network anomaly is as follows: ; in, This indicates the degree of network anomaly at the k-th node. This indicates the degree of fluctuation in the data transmission cycle of the k-th node. This indicates the degree of fluctuation in the trend at the k-th node; This represents the normalization function. The normalization and norm normalization functions in this embodiment can be specifically set according to the actual situation. For example, a maximum / minimum value normalization method can be used, or the following common methods can be used: , The object being processed is represented by exp, which represents an exponential function with the natural constant e as its base.

[0043] Using the above method, the degree of network anomaly at each node can be obtained.

[0044] Steps 1-5: Based on the degree of network anomaly of each node, filter out the abnormal nodes.

[0045] The higher the network anomaly level of a node, the more abnormal the node is. Therefore, abnormal nodes are selected based on the network anomaly level of each node. In an exemplary embodiment, a preset threshold is set, with a value ranging from 0 to 1, and the specific value is set according to actual needs, such as 0.8. The network anomaly level of each node is compared with this threshold; if it is greater than the threshold, it indicates a high degree of network anomaly, and the corresponding node is very abnormal. Therefore, nodes with a network anomaly level greater than the threshold are identified as abnormal nodes.

[0046] After obtaining the abnormal node, it is necessary to obtain a set of candidate nodes for the abnormal node. Each candidate node in the candidate node set has the potential to replace the abnormal node, but the probability varies. The purpose of the following data processing is to determine the candidate node with the highest probability from the candidate node set, as the replacement node for the abnormal node.

[0047] In an exemplary embodiment, it is also necessary to obtain the set of associated nodes for each node in the Internet of Things (IoT). For any given node, the set of associated nodes is the set of nodes that have communication relationships with that node. Obtaining the set of associated nodes requires determining the network topology of the IoT. It should be understood that the network topology of the IoT includes the individual nodes and the data transmission relationships between them.

[0048] If two nodes have a communication relationship—specifically, a direct data transmission relationship where no other node acts as an intermediary and the two nodes are directly connected—then a connection exists between them. Communication relationships are divided into data sending and receiving relationships, representing the data flow between the two nodes. For example, when node 1 sends data to node 2 and receives it directly without any other node intermediaries, node 1 acts as the sending node for node 2. Conversely, when node 2 sends data to node 1 and receives it directly without any other node intermediaries, node 1 acts as the receiving node for node 2.

[0049] Therefore, it is necessary to determine the set of associated nodes for each node in the Internet of Things (IoT). This set includes both receiving and sending nodes. Specifically, for any given node, the nodes that directly receive data from it are identified as its receiving nodes, resulting in a set of receiving nodes. Similarly, the nodes that directly receive data from it are identified as its sending nodes, resulting in a set of sending nodes. This process yields the set of associated nodes for each node in the IoT. Therefore, the set of associated nodes for abnormal nodes is also obtained through this method. This set includes both receiving and sending nodes. If the receiving and sending node sets have a high degree of overlap with the abnormal node, they can be preferentially selected as replacement nodes. Selecting replacement nodes from these sets minimizes the impact on the normal operation of the IoT network after node replacement. It should be understood that in extreme cases, an abnormal node may not have an associated node set. In this case, no further data processing is performed; an alarm signal is directly output to promptly notify personnel to take other remedial measures.

[0050] Since the data flow between two nodes can be bidirectional, meaning they act as both receivers and senders for each other, this type of node is more likely to be considered as a candidate node for identifying abnormal nodes.

[0051] Since we need to select nodes with the highest possible overlap with the abnormal node as candidate nodes, we need to find the nodes that overlap between the receiving and sending node sets of the abnormal node. This means finding the intersection of the receiving and sending node sets of the abnormal node. Nodes in this intersection have both data sending and receiving relationships with the abnormal node. Therefore, we select these overlapping nodes from the receiving and sending node sets of the abnormal node as candidate nodes, forming the candidate node set for the abnormal node.

[0052] Step 2: Obtain the set of associated nodes that have communication relationships with the abnormal node and each candidate node, and determine the replaceability of each candidate node based on the relationship between the set of associated nodes of the abnormal node and the set of associated nodes of each candidate node.

[0053] By using the process in step 1, the set of associated nodes for each candidate node can be obtained. Based on the relationship between the set of associated nodes of the abnormal node and the set of associated nodes of each candidate node, the replaceability of each candidate node can be determined.

[0054] For any candidate node, the more overlapping nodes there are between the candidate node's receiving and sending nodes and those of the abnormal node, the stronger the candidate node's replaceability, meaning a higher probability of it replacing the abnormal node. Simultaneously, the more communications the candidate node has with its receiving and sending nodes, the more active the candidate node is, and thus, the stronger its replaceability. Therefore, in an exemplary embodiment, such as... Figure 4 As shown, a specific process for obtaining substitutability is given: Step 2-1: Obtain the intersection of the set of receiving nodes of the abnormal node and the set of receiving nodes of the first candidate node to obtain the first intersection.

[0055] For ease of explanation, let's define any candidate node as the first candidate node. We obtain the first intersection by finding the intersection of the set of receiving nodes for the abnormal nodes and the set of receiving nodes for the first candidate node. The formula is as follows: ; in, This represents the intersection of the abnormal node and the set of receiving nodes of the v-th candidate node, i.e., the first intersection; This represents the set of receiving nodes for the abnormal node. Let v represent the set of receiving nodes for the v-th candidate node. This indicates the intersection operation.

[0056] Step 2-2: Obtain the intersection of the sending node set of the abnormal node and the sending node set of the first candidate node to obtain the second intersection.

[0057] The second intersection is obtained by finding the intersection of the set of sending nodes of the abnormal nodes and the set of sending nodes of the first candidate nodes. The formula is as follows: ; in, This represents the intersection of the set of sending nodes of the abnormal node and the v-th candidate node, i.e., the second intersection; This represents the set of sending nodes of the abnormal node. This represents the set of sending nodes for the v-th candidate node.

[0058] Steps 2-3: Based on the first intersection, the second intersection, the number of communications between the first candidate node and the corresponding set of receiving nodes, and the number of communications between the first candidate node and the corresponding set of sending nodes, the substitutability of the first candidate node is obtained.

[0059] The higher the proportion of the first intersection, the higher the overlap between the receiving node of the abnormal node and the receiving node of the first candidate node. The higher the proportion of the second intersection, the higher the overlap between the sending node of the abnormal node and the sending node of the first candidate node. During data transmission, the first candidate node is more likely to replace the abnormal node. At the same time, it can reduce the number of times the abnormal node jumps.

[0060] Within the current preset time period, the number of communications between the first candidate node and each sending node in the first candidate node's sending node set is obtained; that is, the number of times each sending node sends data to the first candidate node within the current preset time period. Then, the sum of the sending counts of each sending node is calculated. Similarly, within the current preset time period, the number of communications between the first candidate node and each receiving node in the first candidate node's receiving node set is obtained; that is, the number of times each receiving node receives data sent by the first candidate node within the current preset time period. Then, the sum of the receiving counts of each receiving node is calculated. The larger the sum of the sending counts of each sending node and the sum of the receiving counts of each receiving node, the more active the first candidate node is, and the stronger its replaceability.

[0061] Therefore, based on the first intersection, the second intersection, the number of communications between the first candidate node and its corresponding set of receiving nodes, and the number of communications between the first candidate node and its corresponding set of sending nodes, the substitutability of the first candidate node is obtained. In an exemplary embodiment, the formula for calculating substitutability is as follows: ; in, To represent the replaceability of the v-th candidate node, use... Let M1 represent the number of receiving nodes in the first intersection set corresponding to the v-th candidate node, and M1 represent the number of receiving nodes in the receiving node set of the abnormal node. This represents the proportion of the first intersection of the v-th candidate nodes, characterizing the substitutability of the receiving node when interacting with it; using Let M1 represent the number of sending nodes in the second intersection set corresponding to the v-th candidate node, and M2 represent the number of sending nodes in the set of sending nodes of the abnormal node. This represents the proportion of the second intersection of the v-th candidate nodes, and represents the substitutability of the data sent by the v-th candidate node when interacting with the sending node. This represents the sum of the number of communications between the v-th candidate node and each of the corresponding sending nodes in the set of sending nodes within the current preset time period. This represents the sum of the number of communications between the v-th candidate node and each receiving node in the corresponding set of receiving nodes within the current preset time period.

[0062] Step 3: Based on the replaceability of each candidate node and the network load of each candidate node, select the target candidate node from the candidate node set as the replacement node for the abnormal node.

[0063] Because different candidate nodes transmit different amounts of data, when determining the replacement node, due to network congestion, nodes with lower network loads are prioritized as replacement nodes. Since the replacement node needs to transmit its own current network data volume while also adding data transmission to the faulty node, using a node with a high network load would cause network congestion.

[0064] Therefore, it is necessary to obtain the network load information of each candidate node. In an exemplary embodiment, such as... Figure 5 The diagram illustrates a specific process for obtaining the network load information of candidate nodes: Step 3-1: Obtain the network traffic data between the first candidate node and its receiving node set to obtain the first network traffic data.

[0065] A preset time period is defined, with the current time as the end point. The length of this time period can be set as needed. The purpose of setting a time period is to obtain the network load of the candidate nodes within that time period. It should be understood that the time period can be less than or equal to the preset time period.

[0066] The network traffic data of the first candidate node and each receiving node in the set of receiving nodes of the first candidate node is obtained. Specifically, the traffic value of data sent by the first candidate node to each receiving node of the first candidate node within a preset unit time period is obtained, and then the sum of the traffic values ​​of data sent to all receiving nodes is calculated as the first network traffic data corresponding to the first candidate node. The first network traffic data represents the network load of the first candidate node in sending data to other nodes, that is, the total amount of traffic sent by the first candidate node.

[0067] Step 3-2: Obtain the network traffic data between the first candidate node and its sending node set to obtain the second network traffic data.

[0068] The network traffic data of the first candidate node and each sending node in the set of sending nodes of the first candidate node is obtained. Specifically, the traffic value of the data sent by each sending node received by the first candidate node within a preset unit time period is obtained, and then the sum of the traffic values ​​of the data sent by all sending nodes is calculated as the second network traffic data corresponding to the first candidate node. The second network traffic data represents the network load of the first candidate node receiving data sent by other nodes, that is, the total amount of traffic received by the first candidate node.

[0069] Step 3-3: Take the maximum value between the first network traffic data and the second network traffic data as the network load status of the first candidate node.

[0070] The higher the network traffic data corresponding to the first candidate node, and the more nodes communicating with it, the more network traffic the first candidate node utilizes during data transmission. Therefore, based on the first and second network traffic data of the first candidate node, its network load is determined. Since the total received traffic and total sent traffic may deviate slightly due to network instability, the maximum value between the first and second network traffic data is taken as the network load of the first candidate node. The formula is as follows: ; in, This indicates the network load of the v-th candidate node. This represents the number of sending nodes corresponding to the v-th candidate node. This represents the flow rate of data received by the v-th candidate node from the i-th sending node. This represents the total traffic sent by the v-th candidate node; This represents the number of receiving nodes for the v-th candidate node. This represents the data flow value sent from the v-th candidate node to the j-th receiving node. This represents the total traffic received by the v-th candidate node; This represents the function that takes the maximum value.

[0071] Then, based on the replaceability of each candidate node and the network load of each candidate node, target candidate nodes are selected from the candidate node set, such as... Figure 6 As shown, it includes: Steps 3-4: Based on the replaceability of each candidate node and the network load of each candidate node, obtain the replacement probability of each candidate node.

[0072] The higher the substitutability, the higher the likelihood of replacing the candidate node, and the lower the network load. In other words, the lower the network load, the higher the likelihood of replacing the candidate node. Therefore, the likelihood of replacement is directly proportional to substitutability and inversely proportional to network load.

[0073] In one exemplary embodiment, such as Figure 7 As shown, the process of obtaining the substitution possibilities is as follows: Step 3-4-1: Normalize the replaceability of the first candidate node and perform negative correlation normalization on the network load of the first candidate node.

[0074] Step 3-4-2: The product of the normalized replaceability of the first candidate node and the negatively correlated normalized network load of the first candidate node is used as the replacement probability of the first candidate node.

[0075] In one exemplary embodiment, the formula for calculating the substitution probability is as follows: ; in, Let represent the replacement probability of the v-th candidate node, and exp represent an exponential function with the natural constant e as the base. Indicates to The negative correlation normalization process.

[0076] Steps 3-5: Select the candidate node with the highest replacement probability as the target candidate node.

[0077] The higher the replacement probability, the more likely it is to be used as a replacement node. Therefore, the candidate node with the highest replacement probability is taken as the target candidate node, and the target candidate node is the replacement node for the abnormal node.

[0078] In the subsequent steps, the obtained replacement node replaces the abnormal node in the IoT, and the IoT network link is reconstructed based on the replacement node for data transmission. In an exemplary embodiment, the steps are as follows: (1) Once an abnormal node is identified, measures should be taken immediately to block the abnormal node in order to prevent it from having a further impact on the network.

[0079] (2) After blocking the abnormal nodes, determine the replacement nodes according to the above process.

[0080] (3) Rebuild the network links using the identified replacement nodes. This may involve adjusting the routing strategy to ensure the optimality of the data transmission path and minimize the impact on network performance.

[0081] (4) After the new network link is built, resume data transmission, monitor the performance of the new link, and ensure that the data can be transmitted to the IoT platform stably and securely.

[0082] This embodiment also provides an integrated communication monitoring and management system based on the Internet of Things, including: a memory and a processor; the memory is connected to the processor, and the memory is used to store program instructions; the processor is used to implement the steps in the above-described embodiment of the integrated communication monitoring and management method based on the Internet of Things when the program instructions are executed.

[0083] In one exemplary embodiment, the present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps described in the embodiment of the Internet of Things-based integrated communication monitoring and management method.

[0084] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0085] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

Claims

1. A communication-integrated monitoring and management method based on the Internet of Things, characterized in that, include: Determine a set of candidate nodes for the abnormal node, wherein each candidate node in the set of candidate nodes has the potential to replace the abnormal node; Obtain the sets of associated nodes that have communication relationships with the abnormal node and each candidate node respectively, and determine the replaceability of each candidate node based on the relationship between the set of associated nodes of the abnormal node and the set of associated nodes of each candidate node. Based on the replaceability of each candidate node and the network load of each candidate node, a target candidate node is selected from the candidate node set and used as a replacement node for the abnormal node.

2. The communication integrated monitoring and management method based on the Internet of Things as described in claim 1, characterized in that, Before determining the candidate node set for abnormal nodes, the integrated communication monitoring and management method further includes: The network traffic time series of each node in the Internet of Things is decomposed to obtain the trend component curve and the seasonal component curve; Based on the seasonal component curves of each node, the degree of fluctuation in the data transmission cycle of each node is obtained; The trend component curves of each node are fitted with straight lines to obtain fitted straight lines, and the degree of trend change fluctuation of each node is obtained based on the difference between the trend component curves of each node and the fitted straight lines. The degree of network anomaly at each node is obtained based on the degree of fluctuation in the data transmission cycle and the degree of fluctuation in the trend change. The abnormal nodes are identified by filtering based on the degree of network anomaly at each node.

3. The communication integrated monitoring and management method based on the Internet of Things as described in claim 1, characterized in that, The associated node set includes a receiving node set and a sending node set; The process of obtaining the candidate node set of the abnormal node includes: taking the nodes that overlap in the receiving node set and sending node set of the abnormal node as candidate nodes of the abnormal node, thus forming the candidate node set.

4. The communication integrated monitoring and management method based on the Internet of Things as described in claim 3, characterized in that, The relationship between the set of associated nodes based on abnormal nodes and the set of associated nodes for each candidate node determines the substitutability of each candidate node, including: Obtain the intersection of the set of receiving nodes of the abnormal node and the set of receiving nodes of the first candidate node to obtain the first intersection; the first candidate node can be any candidate node. Obtain the intersection of the set of sending nodes of the abnormal nodes and the set of sending nodes of the first candidate nodes to obtain the second intersection; The substitutability of the first candidate node is obtained based on the first intersection, the second intersection, the number of communications between the first candidate node and the corresponding set of receiving nodes, and the number of communications between the first candidate node and the corresponding set of sending nodes.

5. The communication integrated monitoring and management method based on the Internet of Things as described in claim 4, characterized in that, The formula for calculating substitutability is as follows: ； in, This indicates the replaceability of the v-th candidate node. M1 represents the number of receiving nodes in the first intersection corresponding to the v-th candidate node, and M1 represents the number of receiving nodes in the receiving node set of the abnormal node. M1 represents the number of sending nodes in the second intersection corresponding to the v-th candidate node, and M2 represents the number of sending nodes in the set of sending nodes of the abnormal node. This represents the sum of the number of communications between the v-th candidate node and each of the corresponding sending nodes in the set of sending nodes within the current preset time period. This represents the sum of the number of communications between the v-th candidate node and each receiving node in the corresponding set of receiving nodes within the current preset time period.

6. The communication integrated monitoring and management method based on the Internet of Things as described in claim 2, characterized in that, The process of obtaining the network load information is as follows: Obtain the network traffic data between the first candidate node and its set of receiving nodes to obtain the first network traffic data; the first candidate node can be any candidate node. Obtain the network traffic data between the first candidate node and its sending node set to obtain the second network traffic data; The maximum value between the first network traffic data and the second network traffic data is taken as the network load of the first candidate node.

7. The communication integrated monitoring and management method based on the Internet of Things as described in claim 6, characterized in that, The process of acquiring the first network traffic data includes: taking the sum of the network traffic data of the first candidate node and each receiving node in the set of receiving nodes as the first network traffic data; The process of obtaining the second network traffic data includes: taking the sum of the network traffic data of the first candidate node and each sending node in the set of sending nodes as the second network traffic data.

8. The communication integrated monitoring and management method based on the Internet of Things as described in claim 1, characterized in that, The step of selecting target candidate nodes from the candidate node set based on the replaceability of each candidate node and the network load of each candidate node includes: The replacement probability of each candidate node is obtained based on the replaceability of each candidate node and the network load of each candidate node; the replacement probability is directly proportional to the replaceability and inversely proportional to the network load. The candidate node corresponding to the highest replacement probability is selected as the target candidate node.

9. The communication integrated monitoring and management method based on the Internet of Things as described in claim 8, characterized in that, The process of obtaining the replacement possibilities includes: The replaceability of the first candidate node is normalized, and the network load of the first candidate node is negatively correlated and normalized. The replacement probability of the first candidate node is calculated by multiplying the normalized replaceability of the first candidate node by the negatively correlated normalized network load of the first candidate node.

10. An integrated communication monitoring and management system based on the Internet of Things, characterized in that, include: Memory and processor; The memory is connected to the processor; The memory is used to store program instructions; The processor is configured to implement the IoT-based integrated communication monitoring and management method as described in any one of claims 1-9 when program instructions are executed.

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