Communication integrated monitoring management system and method based on internet of things

Abstract

The application 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, a candidate node set of an abnormal node is determined, an associated node set existing a communication relationship with the abnormal node and each candidate node is respectively acquired, and the replaceability of each candidate node is determined based on the connection between the associated node set of the abnormal node and the associated node set of each candidate node; and a target candidate node is screened from the candidate node set according to the replaceability of each candidate node and the network load condition of each candidate node, so that the target candidate node is used as a replacement node of the abnormal node, the acquisition accuracy of the replacement node of the abnormal node is improved, and the influence on the stable transmission of Internet of Things data is reduced as much as possible.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of Internet of Things communication, and in particular to a communication integrated monitoring management system and method based on Internet of Things. BACKGROUND

[0002] In an Internet of Things system, data collection terminals such as sensors need to transmit the collected data information to an Internet of Things platform. If single-hop coverage is insufficient, intermediate nodes need to be chained to transmit information. These nodes can include routers, gateways or other relay devices, which are responsible for transmitting data from one network segment to another. After the data reaches the Internet of Things platform, it will be stored and processed on the cloud platform or local server.

[0003] If there are node abnormalities or malicious attacks in the nodes in the network link, although it may be necessary to rely on nearby nodes as replacement nodes for data transmission. However, there can be more than one nearby node, and the nearby node may not be able to serve as a replacement node due to its own actual situation, such as a large network load, which would affect its performance and thus the transmission efficiency of the nearby node. However, the existing selection method of replacement nodes only determines the replacement node according to the network load of the nearby node, or randomly specifies a nearby node as a replacement node by the Internet of Things platform, which has low reliability and may not be able to obtain an accurate replacement node, thereby affecting the stability of data transmission. SUMMARY

[0004] In order to solve the technical problem of low reliability of the existing selection method of replacement nodes, the purpose of the present application is to provide a communication integrated monitoring management system and method based on Internet of Things, and the technical solution adopted is as follows:

[0005] In the first aspect of the present application, a communication integrated monitoring management method based on Internet of Things is provided, comprising:

[0006] determining a set of candidate nodes for the abnormal node, each candidate node in the set of candidate nodes having a possibility of replacing the abnormal node;

[0007] respectively acquiring a set of associated nodes having a communication relationship with the abnormal node and each candidate node, and determining 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;

[0008] According to the replaceability of each candidate node and the network load condition of each candidate node, a target candidate node is selected from the set of candidate nodes as a replacement node for the abnormal node.

[0009] In an exemplary embodiment, before the determining the candidate node set of the abnormal node, the communication integration monitoring management method further comprises:

[0010] decomposing the network traffic time series of each node in the Internet of Things to obtain a trend component curve and a seasonal component curve;

[0011] obtaining a data transmission cycle fluctuation degree of each node according to the seasonal component curve of each node;

[0012] performing linear fitting on the trend component curve of each node to obtain a fitting straight line, and obtaining a trend change fluctuation degree of each node according to the difference between the trend component curve of each node and the fitting straight line;

[0013] obtaining a network abnormality degree of each node according to the data transmission cycle fluctuation degree and the trend change fluctuation degree;

[0014] obtaining the abnormal node according to the network abnormality degree of each node.

[0015] In an exemplary embodiment, the associated node set includes a receiving node set and a sending node set;

[0016] The obtaining process of the candidate node set of the abnormal node includes: taking the nodes that coincide in the receiving node set and the sending node set of the abnormal node as the candidate nodes of the abnormal node to constitute the candidate node set.

[0017] In an exemplary embodiment, the determination of the replaceability of each candidate node based on the association between the associated node set of the abnormal node and the associated node set of each candidate node includes:

[0018] obtaining a first intersection set by obtaining the intersection of the receiving node set of the abnormal node and the receiving node set of the first candidate node; the first candidate node is any one of the candidate nodes;

[0019] obtaining a second intersection set by obtaining the intersection of the sending node set of the abnormal node and the sending node set of the first candidate node;

[0020] obtaining the replaceability of the first candidate node according to the first intersection set, the second intersection set, the communication frequency between the first candidate node and the corresponding receiving node set, and the communication frequency between the first candidate node and the corresponding sending node set.

[0021] In an exemplary embodiment, the calculation formula of the replaceability is as follows:

[0022] ;

[0023] wherein, represents the replaceability of the vth candidate node, represents the number of receiving nodes in the first intersection corresponding to the vth candidate node, and M1 represents the number of receiving nodes in the receiving node set of the abnormal node; represents the number of sending nodes in the second intersection corresponding to the vth candidate node, and M2 represents the number of sending nodes in the sending node set of the abnormal node; represents the sum of the communication times between the vth candidate node and each sending node in the corresponding sending node set within the current preset time period, represents the sum of the communication times between the vth candidate node and each receiving node in the corresponding receiving node set within the current preset time period.

[0024] In an exemplary embodiment, the network load condition acquisition process is as follows:

[0025] Obtain the network traffic data between the first candidate node and its receiving node set to obtain first network traffic data; the first candidate node is any candidate node;

[0026] Obtain the network traffic data between the first candidate node and its sending node set to obtain second network traffic data;

[0027] The maximum value in the first network traffic data and the second network traffic data is taken as the network load condition of the first candidate node.

[0028] In an exemplary embodiment, the first network traffic data acquisition process includes: taking the sum of the network traffic data of each receiving node in the receiving node set of the first candidate node as the first network traffic data;

[0029] The second network traffic data acquisition process includes: taking the sum of the network traffic data of each sending node in the sending node set of the first candidate node as the second network traffic data.

[0030] In an exemplary embodiment, the target candidate node is obtained from the candidate node set according to the replaceability of each candidate node and the network load condition of each candidate node, including:

[0031] According to the replaceability of each candidate node and the network load condition of each candidate node, the replacement possibility of each candidate node is obtained; the replacement possibility is proportional to the replaceability and inversely proportional to the network load condition;

[0032] The candidate node corresponding to the maximum replacement possibility is taken as the target candidate node.

[0033] In one exemplary embodiment, the replacement possibility obtaining process comprises:

[0034] The replaceability of the first candidate node is normalized, and the network load condition of the first candidate node is negatively normalized;

[0035] The product of the normalized replaceability of the first candidate node and the negatively normalized network load condition of the first candidate node is taken as the replacement possibility of the first candidate node.

[0036] In a second aspect of the present application, a communication integration monitoring and management system based on Internet of Things is provided, comprising a memory and a processor; the memory is connected with the processor; the memory is used for storing program instructions; the processor is used for realizing the above-mentioned communication integration monitoring and management method based on Internet of Things when the program instructions are executed.

[0037] The present application has the following beneficial effects: firstly, the candidate node set of the abnormal node needs to be determined, then the replaceability of each candidate node is determined according to the connection between the abnormal node and the associated node set of each candidate node, and the candidate node set is comprehensively screened by combining the network load condition of each candidate node to obtain the replacement node of the abnormal node, in addition to considering the network load condition, the connection between the abnormal node and the associated node set of each candidate node is combined to improve the accuracy of obtaining the replacement node of the abnormal node, and the influence on the stable transmission of Internet of Things data is reduced as much as possible. BRIEF DESCRIPTION OF DRAWINGS

[0038] Figure 1 is a flowchart of a communication integration monitoring and management method based on Internet of Things provided by one embodiment of the present application;

[0039] Figure 2 is a flowchart of an abnormal node provided by one embodiment of the present application;

[0040] Figure 3 is a data component decomposition schematic diagram of an STL algorithm provided by one embodiment of the present application;

[0041] Figure 4 is a flowchart of replaceability provided by one embodiment of the present application;

[0042] Figure 5 is a flowchart of the network load condition of a candidate node provided by one embodiment of the present application;

[0043] Figure 6 is a flowchart of a target candidate node provided by one embodiment of the present application;

[0044] Figure 7is a replacement possibility acquisition flowchart provided by one embodiment of the present application. DETAILED DESCRIPTION

[0045] In order to further illustrate the technical means and effects adopted by the present application to achieve the predetermined inventive objectives, the specific embodiments, structures, features and effects of the present application are described in detail below in combination with the drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures or characteristics in one or more embodiments can be combined in any suitable form.

[0046] Unless otherwise defined, all technical and scientific terms used in the present embodiment have the same meaning as commonly understood by one of ordinary skill in the art to which the present application belongs. The data information collected in the present application has been fully authorized and obtained, and the collection, use and processing of relevant information need to comply with relevant laws, regulations and standards of relevant countries and regions.

[0047] The present embodiment provides a communication integration monitoring and management method based on Internet of Things. The applicable scenario is as follows: in an Internet of Things system, a plurality of sensors are responsible for collecting relevant data, a plurality of network nodes are set in the Internet of Things system, and are used to perform multi-level jumping of the collected data through the plurality of network nodes, so as to transmit the data to an Internet of Things platform. The Internet of Things platform is provided with a data processing center for realizing data processing. These nodes can include routers, gateways or other relay devices, which are responsible for transmitting data from one network segment to another network segment. The data can need multi-level jumping to be uploaded to the Internet of Things platform. If there is an abnormal node in the middle, a replacement node needs to be determined to replace the current abnormal node, so as to realize continuous normal transmission of data.

[0048] In the data transmission process, according to the transmission path of the data in each node, if there is direct data transmission between two nodes, it means that there is a connection relationship between the two nodes.

[0049] The overall concept of the communication integration monitoring and management method based on Internet of Things provided by the present embodiment is as follows: first, determine the network node that is abnormal; determine the flow direction of the data in the node according to the topological relationship in the network, and determine the data sending node and the receiving node of the abnormal node. Since the replacement node needs to replace the current abnormal node to transmit data, the set of candidate nodes is determined according to the data sending and receiving conditions in the network, the current replacement node is determined according to the replaceability in the set of candidate nodes and the network load condition, and the current abnormal node is replaced by the replacement node.

[0050] As shown in Figure 1 The communication integration monitoring and management method based on Internet of Things provided by the present embodiment includes the following steps:

[0051] Step 1: determining a candidate node set of the abnormal node, each candidate node in the candidate node set having a possibility of replacing the abnormal node.

[0052] Step 2: respectively acquiring an associated node set existing a communication relationship with the abnormal node and each candidate node, and determining the replaceability of each candidate node based on the connection between the associated node set of the abnormal node and the associated node set of each candidate node.

[0053] Step 3: according to the replaceability of each candidate node and the network load condition of each candidate node, screening a target candidate node from the candidate node set as the replacement node of the abnormal node.

[0054] Each step will be described in detail as follows.

[0055] Step 1: determining a candidate node set of the abnormal node, each candidate node in the candidate node set having a possibility of replacing the abnormal node.

[0056] Since the sensor data in the Internet of Things is periodically sampled, and the data is transmitted according to the minimum hop number in the transmission process, the data generally remains relatively stable and exists in a fixed cycle. Therefore, the cycle condition and stable state of the data transmission of each node in the current Internet of Things can be analyzed to screen the nodes with abnormal conditions in the current Internet of Things.

[0057] In an exemplary embodiment, before determining the candidate node set of the abnormal node, the abnormal node needs to be determined first, for example, as shown in the following table. Figure 2 The specific determination process of the abnormal node includes:

[0058] Step 1-1: decompose the network traffic time series of each node in the Internet of Things to obtain a trend component curve and a seasonal component curve.

[0059] Set a current preset time period, the end time of the time period is the current time, and the time length of the time period is set by actual needs.

[0060] Obtain the network traffic time series of each node in the Internet of Things in the current preset time period, and obtain the corresponding network traffic curve according to the network traffic time series. The network traffic is specifically the data transmission traffic.

[0061] STL (Seasonal-Trend Decomposition using LOESS) decompose the network traffic curve of the network traffic time series of each node to obtain the trend component curve and the seasonal component curve of each node.

[0062] It should be understood that by applying the STL algorithm, the network traffic curve is decomposed into a trend component, a seasonal component and a residual component. The trend component reflects the long-term trend, the seasonal component reveals the periodic change, and the residual component contains the random fluctuations other than the trend and the seasonality. As shown in FIG. 4, which is a schematic diagram of data component decomposition of the STL algorithm, the horizontal axis is time and the vertical axis is amplitude. Figure 3 As shown in FIG. 5, which is a schematic diagram of data component decomposition of the STL algorithm, the horizontal axis is time and the vertical axis is amplitude. Figure 3 As shown in FIG. 5, which is a schematic diagram of data component decomposition of the STL algorithm, the horizontal axis is time and the vertical axis is amplitude.

[0063] Step 1-2: Obtain the data transmission periodic fluctuation degree of each node according to the seasonal component curve of each node.

[0064] Since the data of the sensor is generally periodically sampled, the network traffic data generally has periodic changes, and since the seasonal component curve reflects the periodic change of the network traffic curve, the periodic stability of each node, i.e., the data transmission periodic fluctuation degree, is obtained according to the seasonal component curve of each node.

[0065] In an exemplary embodiment, for any node, the maximum value in the seasonal component curve of the node is obtained, the time interval between any two adjacent maximum values is taken as a period, and thus the time interval of each period is obtained. Then, according to the fluctuation of the time interval of each period, the data transmission periodic fluctuation degree is obtained.

[0066] The calculation formula of the data transmission periodic fluctuation degree is as follows:

[0067] ;

[0068] ;

[0069] ;

[0070] where c represents the data transmission periodic fluctuation degree, and represent the time points corresponding to the i-th maximum value and the i+1-th maximum value in the seasonal component curve, respectively, represents the time interval corresponding to the i-th maximum value and the i+1-th maximum value, i.e., the period. represents the average value of the period, n represents the number of maximum values, and n-1 represents the number of periods.

[0071] Step 1-3: Linearly fit the trend component curve of each node to obtain a fitting straight line, and obtain the trend change fluctuation degree of each node according to the difference between the trend component curve of each node and the fitting straight line.

[0072] The trend change of the sensor is also relatively stable, but it is unable to be determined whether the data change is in an ascending, stable or descending state. Therefore, a straight line fitting (such as least square fitting) is performed on the trend component curve of each node to obtain a fitting straight line and determine the case of the fitting straight line.

[0073] For any node, the difference between the trend component curve of the node and the fitting straight line of the node is obtained. The greater the difference, the greater the trend change fluctuation, and the smaller the difference, the more stable the trend change. Therefore, the trend change fluctuation degree of the node is obtained according to the difference between the trend component curve and the fitting straight line of the node.

[0074] In an exemplary embodiment, the difference (the difference is the absolute value of the difference) between the amplitude of the trend component curve and the amplitude of the fitting straight line at each time point of the node is obtained, that is, the absolute value of the difference between the ordinate value of the trend component curve and the ordinate value of the fitting straight line at each abscissa point of the node is obtained, and then the average value of the absolute value of the difference is calculated. The average value is the difference between the trend component curve and the fitting straight line of the node, that is, the trend change fluctuation degree of the node.

[0075] Steps 1-4: Obtain the network anomaly degree of each node according to the data transmission cycle fluctuation degree and the trend change fluctuation degree.

[0076] The greater the data transmission cycle fluctuation degree, the greater the network anomaly degree, and the greater the trend change fluctuation degree, the greater the network anomaly degree. Therefore, the network anomaly degree of each node is obtained according to the data transmission cycle fluctuation degree and the trend change fluctuation degree of each node. The network anomaly degree is proportional to the data transmission cycle fluctuation degree and proportional to the trend change fluctuation degree.

[0077] In an exemplary embodiment, the calculation formula of the network anomaly degree is as follows:

[0078] ;

[0079] wherein, represents the network anomaly degree of the kth node, represents the data transmission cycle fluctuation degree of the kth node, represents the trend change fluctuation degree of the kth node; represents a normalization function. The normalization in the embodiment and the normalization function norm can be specifically set according to actual conditions, for example, a maximum and minimum normalization method can be used, or the following commonly used method can be used: , represents a processing object, and exp represents an exponential function with natural constant e as the base.

[0080] In this way, the network anomaly degree of each node can be obtained.

[0081] Step 1-5: According to the network anomaly degree of each node, the abnormal node is screened out.

[0082] The higher the network anomaly degree of the node, the more abnormal the node is. Therefore, according to the network anomaly degree of each node, the abnormal node is screened out. In an exemplary embodiment, a threshold value is preset, and the numerical range of the threshold value is 0-1, and the specific numerical value is set by actual needs, such as 0.8. The network anomaly degree of each node is compared with the threshold value, and the network anomaly degree greater than the threshold value indicates that the network anomaly degree is high, and the corresponding node is very abnormal. Therefore, the node with the network anomaly degree greater than the threshold value is taken as the abnormal node.

[0083] After obtaining the abnormal node, the candidate node set of the abnormal node needs to be obtained. Each candidate node in the candidate node set has the possibility of replacing the abnormal node, but the possibility is high or low. The purpose of the following data processing process is to determine the candidate node with the highest possibility from the candidate node set as the replacement node to replace the abnormal node.

[0084] In an exemplary embodiment, the association node set of each node in the Internet of Things also needs to be obtained. For any one node, the association node set of the node is a node set that has a communication relationship with the node. Before obtaining the association node set, the network topology structure of the Internet of Things needs to be determined. It should be understood that the network topology structure of the Internet of Things includes each node and the data transmission relationship between each node.

[0085] If there is a communication relationship between two nodes, the communication relationship specifically refers to a direct data transmission relationship, that is, there is no transfer of other nodes, but a direct communication connection relationship between the two nodes. It indicates that there is an association relationship between the two nodes. The communication relationship is divided into data sending relationship and data receiving relationship, and the data sending relationship and the data receiving relationship represent the data flow direction between the two nodes, for example: when the data information of the first node is sent to the second node, and the data information of the first node is directly received by the second node without the transfer of other nodes, then the first node is the sending node of the second node, and when the data information of the second node is sent to the first node, and the data information of the second node is directly received by the first node without the transfer of other nodes, then the first node is the receiving node of the second node.

[0086] Therefore, the associated node set of each node in the Internet of Things is determined, and the associated node set includes a receiving node set and a sending node set. Specifically: for any one node, the nodes that directly receive data information of the node are obtained as receiving nodes of the node, thereby obtaining a receiving node set containing each receiving node; similarly, the nodes that directly receive data information of the node are obtained as sending nodes of the node, thereby obtaining a sending node set containing each sending node. Since the above process obtains the associated node set of each node in the Internet of Things, the associated node set of the abnormal node is obtained through the above-mentioned manner, and the associated node set includes a receiving node set and a sending node set. The receiving node set and the sending node set have a relatively high degree of coincidence with the abnormal node, and can be preferentially selected as replacement nodes. The replacement nodes are selected from these, and the influence on the normal operation of the Internet of Things network after the node replacement is relatively small. It should be understood that in an extreme case, the abnormal node can not have an associated node set, and in this case, subsequent data processing is no longer performed, and an alarm signal is directly output to timely inform the staff to take other remedial measures.

[0087] Since the data flow between two nodes can be bidirectional, that is, the two nodes mutually serve as receiving nodes and sending nodes of each other. Then, this type of node can be more suitable as a candidate node of an abnormal node.

[0088] Since it is necessary to obtain nodes with a high degree of coincidence with the abnormal node as candidate nodes, the nodes that coincide in the receiving node set and the sending node set of the abnormal node are obtained, that is, the intersection of the receiving node set and the sending node set of the abnormal node is obtained. The nodes in the intersection have both data sending relationship and data receiving relationship with the abnormal node. Then, the nodes that coincide in the receiving node set and the sending node set of the abnormal node are taken as candidate nodes of the abnormal node, thereby forming a candidate node set of the abnormal node.

[0089] Step 2: respectively obtaining the associated node set having a communication relationship with the abnormal node and each candidate node, and determining the replaceability of each candidate node based on the relationship between the associated node set of the abnormal node and the associated node set of each candidate node.

[0090] The process in step 1 is adopted to obtain the associated node set of each candidate node. The replaceability of each candidate node is determined according to the relationship between the associated node set of the abnormal node and the associated node set of each candidate node.

[0091] As any one of the candidate nodes, the more the number of coincident nodes between the receiving node and the sending node of the candidate node and the receiving node and the sending node of the abnormal node, the stronger the replaceability of the candidate node, that is, the higher the possibility of replacing the abnormal node. Meanwhile, if the candidate node communicates with its receiving node and sending node more frequently, it means that the candidate node is more active, and the replaceability of the candidate node is stronger. Then, in an exemplary embodiment, as shown in Figure 4 , a specific acquisition process of replaceability is given:

[0092] Step 2-1: Obtain the intersection of the receiving node set of the abnormal node and the receiving node set of the first candidate node, to obtain the first intersection.

[0093] For ease of illustration, set any one of the candidate nodes as the first candidate node. Obtain the intersection of the receiving node set of the abnormal node and the receiving node set of the first candidate node, to obtain the first intersection, which is expressed by the formula as follows:

[0094] ;

[0095] Among them, represents the intersection of the abnormal node and the receiving node set of the vth candidate node, that is, the first intersection; represents the receiving node set of the abnormal node, represents the receiving node set of the vth candidate node, represents the intersection operation.

[0096] 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.

[0097] 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, which is expressed by the formula as follows:

[0098] ;

[0099] Among them, represents the intersection of the abnormal node and the sending node set of the vth candidate node, that is, the second intersection; represents the sending node set of the abnormal node, represents the sending node set of the vth candidate node.

[0100] Step 2-3: Obtain the replaceability of the first candidate node according to the first intersection, the second intersection, the communication frequency of the first candidate node and the corresponding receiving node set, and the communication frequency of the first candidate node and the corresponding sending node set.

[0101] The higher the proportion of the first intersection is, the higher the coincidence degree of the abnormal node and the receiving node of the first candidate node is. The higher the proportion of the second intersection is, the higher the coincidence degree of the abnormal node and the sending node of the first candidate node is. In data transmission, the first candidate node is more likely to replace the abnormal node. At the same time, the abnormal node is more likely to reduce the jump situation.

[0102] The communication times of the first candidate node and each sending node in the sending node set of the first candidate node in the current preset time period are obtained, that is, the sending times of each sending node to the first candidate node in the current preset time period, and then the sum of the sending times of each sending node is calculated. Similarly, the communication times of the first candidate node and each receiving node in the receiving node set of the first candidate node in the current preset time period are obtained, that is, the receiving times of each receiving node to the first candidate node in the current preset time period, and then the sum of the receiving times of each receiving node is calculated. The greater the sum of the sending times of each sending node and the sum of the receiving times of each receiving node are, the more active the first candidate node is, and the stronger the replaceability of the first candidate node is.

[0103] Therefore, according to the first intersection, the second intersection, the communication times of the first candidate node and the corresponding receiving node set, and the communication times of the first candidate node and the corresponding sending node set, the replaceability of the first candidate node is obtained. In an exemplary embodiment, the calculation formula of the replaceability is as follows:

[0104] ;

[0105] wherein, represents the replaceability of the vth candidate node, uses represents the number of receiving nodes in the first intersection corresponding to the vth candidate node, and M1 represents the number of receiving nodes in the receiving node set of the abnormal node, represents the proportion of the first intersection of the vth candidate node, representing the replaceability degree of the receiving node when interacting with the receiving node; uses represents the number of sending nodes in the second intersection corresponding to the vth candidate node, and M2 represents the number of sending nodes in the sending node set of the abnormal node, represents the proportion of the second intersection of the vth candidate node, representing the replaceability degree of the sending data of the vth candidate node when interacting with the sending node; represents the sum of the communication times of the vth candidate node and each sending node in the corresponding sending node set in the current preset time period, represents the sum of the communication times of the vth candidate node and each receiving node in the corresponding receiving node set in the current preset time period.

[0106] Step 3: filtering the target candidate node from the candidate node set as the replacement node of the abnormal node according to the replaceability of each candidate node and the network load condition of each candidate node.

[0107] Since different candidate nodes have different amounts of data to be transmitted, when determining the replacement node, the node with a relatively low network load is preferred to be determined as the replacement node due to the network congestion. Since the replaced node needs to transmit its own network data amount and also needs to add the data transmission of the abnormal node, if a node with a relatively high network load is used, network congestion will occur.

[0108] Therefore, the network load condition of each candidate node needs to be obtained. In an exemplary embodiment, as shown in FIG. 3, a specific process for obtaining the network load condition of the candidate node is given as follows: Figure 5

[0109] Step 3-1: obtaining the network flow data between the first candidate node and the receiving node set of the first candidate node to obtain the first network flow data.

[0110] A unit time period is preset, the end time of the unit time period is the current time, and the length of the unit time period is set by actual needs. The purpose of setting the unit time period is to obtain the network load condition of the candidate node in the unit time period. It should be understood that the unit time period is less than or equal to the current preset time period.

[0111] The network flow data between the first candidate node and each receiving node in the receiving node set of the first candidate node is obtained, specifically: the flow value of the data sent by the first candidate node to each receiving node in the preset unit time period is obtained, and then the sum of the flow values of the data sent by all the receiving nodes is calculated as the first network flow data corresponding to the first candidate node. The first network flow data represents the network load condition of the first candidate node for sending data to other nodes, i.e., the total amount of flow sent by the first candidate node.

[0112] Step 3-2: obtaining the network flow data between the first candidate node and the sending node set of the first candidate node to obtain the second network flow data.

[0113] The network flow data between the first candidate node and each sending node in the sending node set of the first candidate node is obtained, specifically: the flow value of the data sent by each sending node in the preset unit time period is obtained, and then the sum of the flow values of the data sent by all the sending nodes is calculated as the second network flow data corresponding to the first candidate node. The second network flow data represents the network load condition of the first candidate node for receiving data sent by other nodes, i.e., the total amount of flow received by the first candidate node.​

[0114] Step 3-3: Taking the maximum value between the first network flow data and the second network flow data as the network load condition of the first candidate node.

[0115] The higher the network flow data corresponding to the first candidate node is, and the more nodes having a communication relationship with the first candidate node are, the more network transmission is used in the data transmission process of the first candidate node. Then, the network load condition of the first candidate node is obtained according to the first network flow data and the second network flow data of the first candidate node. Since the total number of received flows and the total number of sent flows may have a small deviation due to unstable network of nodes, the maximum value between the first network flow data and the second network flow data is taken as the network load condition of the first candidate node. The formula is as follows:

[0116]

[0117] represents the network load condition of the vth candidate node, represents the number of sending nodes corresponding to the vth candidate node, represents the flow value of data sent by the ith sending node and received by the vth candidate node, represents the total number of flow of sending nodes of the vth candidate node; represents the number of receiving nodes of the vth candidate node, represents the flow value of data sent by the vth candidate node to the jth receiving node, represents the total number of flow of receiving nodes of the vth candidate node; represents a maximum value function.

[0118] Then, according to the replaceability of each candidate node and the network load condition of each candidate node, a target candidate node is selected from the candidate node set, as shown in Figure 6 , including:

[0119] Step 3-4: Obtaining the replacement possibility of each candidate node according to the replaceability of each candidate node and the network load condition of each candidate node.

[0120] The higher the replaceability is, the higher the replacement possibility of the candidate node is, and the lower the network load condition is, that is, the lower the network load is, the higher the replacement possibility of the candidate node is. Therefore, the replacement possibility is proportional to the replaceability and inversely proportional to the network load condition.

[0121] In an exemplary embodiment, as shown in Figure 7 , the replacement possibility obtaining process is as follows:

[0122] ​​Step 3-4-1: Normalize the replaceability of the first candidate node, and negatively correlate the network load of the first candidate node.

[0123] Step 3-4-2: The product of the normalized replaceability of the first candidate node and the negatively correlated network load of the first candidate node is the replacement possibility of the first candidate node.

[0124] In an exemplary embodiment, the calculation formula of the replacement possibility is as follows:

[0125] ;

[0126] Wherein, represents the replacement possibility of the vth candidate node, exp represents the exponential function with natural constant e as the base, represents the negatively correlated normalization processing of .

[0127] Step 3-5: The candidate node corresponding to the maximum replacement possibility is taken as the target candidate node.

[0128] The higher the replacement possibility, the more likely it is to be a replacement node, so the candidate node corresponding to the maximum replacement possibility is taken as the target candidate node, and the target candidate node is the replacement node of the abnormal node.

[0129] In the following, the replacement node obtained is replaced with the abnormal node in the Internet of Things, and the network link of the Internet of Things is reconstructed according to the replacement node for data transmission. In an exemplary embodiment, the steps are as follows:

[0130] (1) Once the abnormal node is determined, measures should be taken immediately to shield the abnormal node to prevent it from further affecting the network.

[0131] (2) After shielding the abnormal node, determine the replacement node according to the process above.

[0132] (3) Using the determined replacement node, reconstruct the network link, which may involve adjusting the routing strategy to ensure the optimality of the data transmission path and minimize the impact on network performance.

[0133] (4) After the new network link is built, restore data transmission, monitor the performance of the new link, and ensure that data can be transmitted stably and safely to the Internet of Things platform.

[0134] The embodiment also provides a communication integrated monitoring management system based on the Internet of Things, comprising a memory and a processor; the memory is connected with the processor and is used for storing program instructions; the processor is used for implementing the steps in the method embodiment of the communication integrated monitoring management system based on the Internet of Things when the program instructions are executed.

[0135] In an exemplary embodiment, the present application provides a computer readable storage medium storing a computer program, which, when executed by a processor, implements the steps in the method embodiment of the communication integrated monitoring management system based on the Internet of Things.

[0136] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description, and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multi-task processing and parallel processing are also possible or can be advantageous.

[0137] Each of the embodiments in the specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other. Each embodiment mainly describes the differences from other embodiments.

Claims

1. A communication integration monitoring management method based on the Internet of Things, characterized in that, The method comprises the following steps: determining a candidate node set of an abnormal node, each candidate node in the candidate node set having a possibility of replacing the abnormal node; respectively acquiring an associated node set of the abnormal node and each candidate node, and determining the replaceability of each candidate node based on the association between the associated node set of the abnormal node and the associated node set of each candidate node; selecting a target candidate node from the candidate node set as a replacement node of the abnormal node according to the replaceability of each candidate node and the network load condition of each candidate node; the replaceability of each candidate node is determined based on the association between the associated node set of the abnormal node and the associated node set of each candidate node, comprising: the associated node set comprises a receiving node set and a sending node set; acquiring the intersection of the receiving node set of the abnormal node and the receiving node set of the first candidate node to obtain a first intersection; the first candidate node is any one of the candidate nodes; acquiring the intersection of the sending node set of the abnormal node and the sending node set of the first candidate node to obtain a second intersection; obtaining the replaceability of the first candidate node according to the first intersection, the second intersection, the communication frequency between the first candidate node and the corresponding receiving node set, and the communication frequency between the first candidate node and the corresponding sending node set; wherein the replaceability is calculated according to the following formula: ； wherein, represents the replaceability of the vth candidate node, represents the number of receiving nodes in the first intersection corresponding to the vth candidate node, and M1 represents the number of receiving nodes in the receiving node set of the abnormal node; represents the number of sending nodes in the second intersection corresponding to the vth candidate node, and M2 represents the number of sending nodes in the sending node set of the abnormal node; represents the sum of the communication times between the vth candidate node and each sending node in the corresponding sending node set within the current preset time period, represents the sum of the communication times between the vth candidate node and each receiving node in the corresponding receiving node set within the current preset time period.

2. The communication integrated monitoring management method based on the Internet of Things according to claim 1, characterized in that, Before determining the candidate node set of the abnormal node, the communication integrated monitoring and management method further comprises: decomposing the network flow time series of each node in the Internet of Things to obtain a trend component curve and a seasonal component curve; obtaining the data transmission cycle fluctuation degree of each node according to the seasonal component curve of each node; performing linear fitting on the trend component curve of each node to obtain a fitting straight line, and obtaining the trend change fluctuation degree of each node according to the difference between the trend component curve and the fitting straight line of each node; obtaining the network abnormality degree of each node according to the data transmission cycle fluctuation degree and the trend change fluctuation degree; obtaining the abnormal node according to the network abnormality degree of each node.

3. The communication integrated monitoring management method based on the Internet of Things according to claim 1, characterized in that, The acquisition process of the candidate node set of the abnormal node comprises: taking the nodes that coincide in the receiving node set and the sending node set of the abnormal node as the candidate nodes of the abnormal node to form the candidate node set.

4. The communication integrated monitoring management method based on the Internet of Things according to claim 2, characterized in that, The acquisition process of the network load condition is as follows: acquiring network flow data between the first candidate node and its receiving node set to obtain first network flow data; the first candidate node is any one of the candidate nodes; acquiring network flow data between the first candidate node and its sending node set to obtain second network flow data; taking the maximum value in the first network flow data and the second network flow data as the network load condition of the first candidate node.

5. The communication integrated monitoring management method based on the Internet of Things according to claim 4, characterized in that, The acquisition process of the first network flow data comprises: taking the sum of the network flow data of the first candidate node and each receiving node in its receiving node set as the first network flow data; The obtaining process of the second network traffic data comprises: taking a sum value of the network traffic data of the first candidate node and each sending node in the sending node set of the first candidate node as the second network traffic data.

6. The communication integrated monitoring management method based on the Internet of Things according to claim 1, characterized in that, The filtering of the target candidate node from the candidate node set according to the replaceability of each candidate node and the network load condition of each candidate node comprises: obtaining a replacement possibility of each candidate node according to the replaceability of each candidate node and the network load condition of each candidate node; the replacement possibility is directly proportional to the replaceability and inversely proportional to the network load condition; taking the candidate node corresponding to the maximum replacement possibility as the target candidate node.

7. The communication integrated monitoring management method based on the Internet of Things according to claim 6, characterized in that, The obtaining process of the replacement possibility comprises: normalizing the replaceability of the first candidate node and negatively correlating the network load condition of the first candidate node; taking a product of the normalized replaceability of the first candidate node and the negatively correlated network load condition of the first candidate node as the replacement possibility of the first candidate node.

8. An Internet of Things-based communication integration monitoring management system, characterized by comprising: a memory and a processor; the memory is connected with the processor; the memory is used for storing program instructions; the processor is used for implementing the communication integration monitoring management method based on Internet of Things when the program instructions are executed.

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