CN111835564B - Self-adaptive recovery method and system for power Internet of things communication link fault - Google Patents
Self-adaptive recovery method and system for power Internet of things communication link fault Download PDFInfo
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- H—ELECTRICITY
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- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
- H04L41/06—Management of faults, events, alarms or notifications
- H04L41/0654—Management of faults, events, alarms or notifications using network fault recovery
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
- H04L41/06—Management of faults, events, alarms or notifications
- H04L41/0654—Management of faults, events, alarms or notifications using network fault recovery
- H04L41/0668—Management of faults, events, alarms or notifications using network fault recovery by dynamic selection of recovery network elements, e.g. replacement by the most appropriate element after failure
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
- H04L41/06—Management of faults, events, alarms or notifications
- H04L41/0677—Localisation of faults
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
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- H04L47/00—Traffic control in data switching networks
- H04L47/10—Flow control; Congestion control
- H04L47/12—Avoiding congestion; Recovering from congestion
- H04L47/125—Avoiding congestion; Recovering from congestion by balancing the load, e.g. traffic engineering
Abstract
The embodiment of the invention provides a self-adaptive recovery method and a self-adaptive recovery system for a communication link fault of an electric power Internet of things. The method comprises the following steps: calculating the duty ratio of the preset network node, and judging the busy degree of the preset network node based on the duty ratio; determining a preset network node for acquiring redundant data according to the busy degree, determining a standby network node set, and acquiring all available communication links; according to network demand constraints, scoring all available communication links to obtain available values of the links; and sequencing all available communication links through an improved KM algorithm based on the link available scores, and matching the optimal communication link for network fault recovery. The embodiment of the invention provides a self-adaptive adjustment power Internet of things communication link fault recovery mechanism, which is used for sensing and quantifying business requirements, considering network load balancing and a method for communication task unloading by adopting key nodes, carrying out self-adaptive accurate matching on available communication links and selecting an optimal communication link.
Description
Technical Field
The invention relates to the technical field of videos, in particular to a self-adaptive recovery method and system for a communication link fault of an electric power internet of things.
Background
With the rapid development of the technology of the internet of things, the internet of things has achieved great achievement in smart cities, smart agriculture, smart medical treatment and the like. The realization of the internet of things requires low energy consumption, low cost and low complexity sensor equipment to complete long-distance transmission. LoRa is a low power wide area network technology. The method has the characteristics of supporting large-scale deployment of low-flow sensor equipment, low cost of the sensor equipment, low cost of production and development infrastructure and the like, has the advantages of ultra-long transmission, ultra-low power consumption and the like, meets the requirement of a large number of sensing sensors in the power internet of things sensing network, and becomes an ideal technical selection of the power internet of things sensing network.
LoRaWAN is a MAC layer protocol for wide area networks based on LoRa chips. The protocol is based on ALOHA and is suitable for low traffic and sporadic communication. In LoRaWAN, star-shaped networking is generally adopted, and compared with a mesh network architecture, the LoRaWAN is the simplest network structure with the lowest delay, but meanwhile, the simple structure has a plurality of problems in the power internet of things perception network. The sensing service of the power internet of things sensing network is various, the differentiation is obvious, the data volume requirement is large, and the data communication quality faces a threat. In addition, the power internet of things perception network sensor is mostly deployed outdoors and outdoors, the deployment environment is complex, severe and changeable, the perception sensor and the communication link are easily affected by the external environment, faults occur frequently, service interruption is caused, and continuous and effective operation of the power internet of things perception network is not facilitated.
The prior art proposes a technical solution to partially solve the above problems, but has the following disadvantages:
(1) the LoRa technology is adopted to collect mass parameters and upload the parameters to the server, but the star networking structure of Lora easily causes the problems of excessive quantity of perception sensors accessed by a single gateway, high data conflict risk and the like, so that the data communication quality faces threats;
(2) because the power internet of things perception network sensors are mostly deployed outdoors and outdoors, the deployment environment is complex, severe and changeable, the perception sensors and the communication link are easily affected by the external environment, faults occur frequently, service interruption is caused, and continuous and effective operation of the power internet of things perception network is not facilitated;
(3) the perception service of the power internet of things perception network is various, the differentiation is obvious, the data volume requirement is large, and the storage format is too single. The street lamp circuit breaker based on the ad hoc network module and with the metering operation has high requirements on data formats, can only process specific metering operation, and is difficult to adapt to the power internet of things perception network environment.
Disclosure of Invention
The embodiment of the invention provides a self-adaptive recovery method for a communication link fault of an electric power Internet of things, which is used for solving the problem that the communication link fault of the electric power Internet of things cannot be effectively positioned and recovered in the prior art.
In a first aspect, an embodiment of the present invention provides a power internet of things communication link fault adaptive recovery method, including:
calculating the duty ratio of a preset network node, and judging the busy degree of the preset network node based on the duty ratio;
determining a preset network node for acquiring redundant data according to the busy degree, determining a spare network node set by the preset network node for acquiring redundant data, and acquiring all available communication links in the spare network node set;
according to the preset network attributes of all the available communication links and the QoS (quality of service) demand constraint of the perception service of the sensor to be recovered, scoring all the available communication links to obtain available link scores;
and sequencing all the available communication links through an improved KM algorithm based on the link available scores, and matching the optimal communication link according to the sequencing result to recover the network fault.
Further, the calculating a duty ratio of a preset network node, and determining a busy degree of the preset network node based on the duty ratio specifically includes:
in a preset period, setting each sensor to send a data packet with a preset length LoRa at a random time;
setting a spread spectrum factor, and setting a value range of the spread spectrum factor according to the distance between a sensor and the preset network node;
calculating the transmission time of the preset length LoRa data packet based on the value range;
and obtaining the load rate of the preset network node according to the transmission time of the preset length LoRa data packet.
Further, the calculating the transmission time of the preset length LoRa data packet based on the value range specifically includes:
calculating to obtain preamble transmission time based on the set preamble length and the unit time transmission symbol number; the number of the transmission symbols in unit time is obtained based on the value range and the signal bandwidth;
calculating to obtain the transmission time of the data packet based on the byte number of the effective load, the length of the CRC byte, the judgment mark of the header, the coding rate, the value range and the signal bandwidth;
and summing the preamble transmission time and the data packet transmission time to obtain the transmission time of the data packet with the preset length LoRa.
Further, the obtaining of the load rate of the preset network node from the transmission time of the preset length LoRa data packet specifically includes:
obtaining the non-collision load rate of the LoRa data packet based on the transmission time of the LoRa data packet with the preset length and the overall transmission time of the LoRa data packet;
obtaining the collision load rate of the LoRa data packet based on the starting sending time of the previous data packet, the sending time of the next data packet and the overall transmission time of the LoRa data packet;
and summing the no-collision load rate of the LoRa data packets and the collision load rate of the LoRa data packets to obtain the load rate of the preset network nodes.
Further, the scoring all available communication links according to the preset network attributes of all available communication links and the QoS requirement constraint of the sensor to be restored to obtain the available link scores specifically includes:
acquiring the load rate of the preset network node, the distance between a plurality of sensors and the transmission time between the preset network node and the plurality of sensors;
respectively setting a first weight parameter, a second weight parameter and a third weight parameter;
and carrying out weighted summation on the first weight parameter, the second weight parameter and the third weight parameter, as well as the load rate of the preset network node, the distances among the plurality of sensors and the transmission time between the preset network node and the plurality of sensors to obtain the available link value.
Further, the ranking all the available communication links by using the improved KM algorithm based on the link availability scores and matching the optimal communication link according to the ranking result for network failure recovery, wherein the method further includes:
adding a plurality of virtual sensor nodes on the preset network node side;
based on a KM algorithm, a vertex labeling method is adopted to search the maximum weight in a weighted complete bipartite graph for problem matching;
acquiring a weight value of a connecting edge of any sensor and any gateway;
calculating the number of all nodes connected with each gateway based on the weight value;
and subtracting the number of the actually existing sensors connected with each gateway from the total number of the nodes to obtain the number of the virtual sensor nodes.
Further, the ranking all the available communication links through an improved KM algorithm based on the link available scores and matching an optimal communication link according to a ranking result for network failure recovery specifically includes:
obtaining the improved KM algorithm based on the link available score and the number of the plurality of virtual sensor nodes;
acquiring the weighted complete bipartite graph, wherein the weighted complete bipartite graph comprises a sensor side node set and a gateway side node set;
if the sum of the vertex label of any point in the sensor side node set and the vertex label of any point in the gateway side node set is greater than or equal to the weight value of a connecting edge between two points, judging that the vertex label of any point in the sensor side node set and the vertex label of any point in the gateway side node set are feasible vertex labels;
based on the feasible vertex labels, obtaining equal subgraphs of the feasible vertex labels;
and acquiring a maximum weight perfect matching result in the equal subgraph of the feasible vertex labels as the sequencing result based on the improved KM algorithm.
In a second aspect, an embodiment of the present invention provides a power internet of things communication link fault adaptive recovery system, including:
the calculation module is used for calculating the duty ratio of a preset network node and judging the busy degree of the preset network node based on the duty ratio;
the determining module is used for determining a preset network node for acquiring redundant data according to the busy degree, determining a standby network node set by the preset network node for acquiring the redundant data, and acquiring all available communication links in the standby network node set;
the evaluation module is used for scoring all the available communication links according to the preset network attributes of all the available communication links and the perception service QoS requirement constraint of the sensor to be recovered to obtain the available values of the links;
and the processing module is used for sequencing all the available communication links through an improved KM algorithm based on the link available scores and matching the optimal communication link according to the sequencing result to recover the network fault.
In a third aspect, an embodiment of the present invention provides an electronic device, including:
the self-adaptive recovery method for the power internet of things communication link failure comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor executes the program to realize the steps of any self-adaptive recovery method for the power internet of things communication link failure.
In a fourth aspect, an embodiment of the present invention provides a non-transitory computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the steps of any one of the power internet of things communication link failure adaptive recovery methods.
According to the self-adaptive recovery method for the communication link fault of the power internet of things, provided by the embodiment of the invention, the available communication link is subjected to self-adaptive accurate matching by perceiving and quantifying the service requirement, considering network load balance and adopting a method of communication task unloading of key nodes, and the optimal communication link is selected.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
Fig. 1 is a flowchart of a fault adaptive recovery method for a communication link of an internet of things for electric power according to an embodiment of the present invention;
fig. 2 is an architecture diagram of an electric power internet of things according to an embodiment of the present invention;
FIG. 3 is a diagram of an improved KM algorithm matching model provided by an embodiment of the present invention;
FIG. 4 is a schematic diagram of an actual scene provided by an embodiment of the present invention;
fig. 5 is a schematic diagram illustrating a comparison of link recovery rates in a sparse scene according to an embodiment of the present invention;
FIG. 6 is a schematic diagram illustrating the ratio of energy consumption of repeaters according to an embodiment of the present invention;
fig. 7 is a schematic diagram illustrating a comparison of link recovery rates in a dense scene according to an embodiment of the present invention;
fig. 8 is a schematic diagram illustrating a comparison of recovery rates of high-load links in a dense scene according to an embodiment of the present invention;
fig. 9 is a diagram of a fault adaptive recovery system for a communication link of the power internet of things according to an embodiment of the present invention;
fig. 10 is a block diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
According to the embodiment of the invention, the fault recovery mechanism of the power internet of things sensing network is researched by combining the characteristics of equipment, networking, service and the like of the power internet of things sensing network, the transmission quality of service data is ensured, the network fault is quickly and accurately recovered, the network fault processing capacity is improved, and the service continuity is ensured, so that the resource utilization rate of the power internet of things sensing network is optimized, the overall performance of the power internet of things sensing network is ensured, the effective development of the power internet of things sensing network service is further supported, the real-time and reliable full-interconnection bidirectional information interaction and situation sensing of a power grid are realized, and the intelligent development of the power grid is promoted.
Fig. 1 is a flowchart of a fault adaptive recovery method for a communication link of an internet of things for electric power according to an embodiment of the present invention, as shown in fig. 1, the method includes:
s1, calculating the duty ratio of a preset network node, and judging the busy degree of the preset network node based on the duty ratio;
s2, determining a preset network node for acquiring redundant data according to the busy degree, determining a spare network node set by the preset network node for acquiring redundant data, and acquiring all available communication links in the spare network node set;
s3, scoring all available communication links according to preset network attributes of all available communication links and the perception service QoS requirement constraint of the sensor to be recovered to obtain available link scores;
and S4, based on the link available scores, sequencing all the available communication links through an improved KM algorithm, and matching the optimal communication link according to the sequencing result to recover the network fault.
Specifically, as shown in fig. 2, the power internet of things perception network is a highly redundant network, and many repeaters or LoRa gateways receive data from the same sensor, wherein only one repeater or LoRa gateway will continue to process the data, and when the current link fails, the alternative set is the node receiving the redundant data. The connection mode makes full use of the existing equipment and improves the stability of the network.
Based on the network characteristics, the embodiment of the invention provides a communication link fault self-adaptive recovery mechanism based on link adjustment, which comprises the steps of firstly calculating the duty ratio of preset network nodes, namely each gateway or repeater, so as to determine the busy degree of each gateway or repeater; secondly, determining an alternative set through other network managers or repeaters which receive redundant data, and further discovering all available communication links; then, according to the delay, energy consumption and adaptive rate attribute of the communication links which are required to meet the requirements of the available communication links and the QoS requirement constraint of the perception service of the sensor to be recovered, scoring is carried out on each link; and finally, sequencing the available communication links meeting the conditions related to the same-frequency duty ratio limit of the gateway through an improved KM (Kuhn-Munkras) algorithm, and adaptively and accurately matching the optimal communication link according to the sequencing result to recover the network fault.
The embodiment of the invention carries out self-adaptive accurate matching on the available communication links by perceiving and quantifying the service requirements, considering network load balance and adopting a method of communication task unloading of key nodes, and selects the optimal communication link.
Based on the above embodiment, step S1 in the method specifically includes:
in a preset period, setting each sensor to send a data packet with a preset length LoRa at a random time;
setting a spread spectrum factor, and setting a value range of the spread spectrum factor according to the distance between a sensor and the preset network node;
calculating the transmission time of the preset length LoRa data packet based on the value range;
and obtaining the load rate of the preset network node according to the transmission time of the preset length LoRa data packet.
Wherein, based on the value range, calculating the transmission time of the preset length LoRa data packet specifically includes:
calculating to obtain preamble transmission time based on the set preamble length and the unit time transmission symbol number; the number of the transmission symbols in unit time is obtained based on the value range and the signal bandwidth;
calculating to obtain the transmission time of the data packet based on the byte number of the effective load, the length of the CRC byte, the judgment mark of the header, the coding rate, the value range and the signal bandwidth;
and summing the preamble transmission time and the data packet transmission time to obtain the transmission time of the data packet with the preset length LoRa.
Obtaining the load rate of the preset network node according to the transmission time of the preset length LoRa data packet, specifically including:
obtaining the non-collision load rate of the LoRa data packet based on the transmission time of the LoRa data packet with the preset length and the overall transmission time of the LoRa data packet;
obtaining the collision load rate of the LoRa data packet based on the starting sending time of the previous data packet, the sending time of the next data packet and the overall transmission time of the LoRa data packet;
and summing the no-collision load rate of the LoRa data packets and the collision load rate of the LoRa data packets to obtain the load rate of the preset network nodes.
Specifically, a total of N sensors, R repeaters, and 2 gateways are set in the network. In each preset period, the probability of all the links failing is k. For the convenience of calculation of the network model, it is assumed that each sensor sends a data packet with a preset length of payload at a random time in each period T, and then in each period T, the sensors related to Nk links need to be adjusted.
When the sensor is connected to a LoRa gateway, the spreading factor SF is limited to a range of 7-12. The embodiment of the invention sets the spreading factor by calculating the distance between the sensor and the repeater or the LoRa gateway. Generally, the adaptive rate is adjusted only when the sensor is moving, but in the scenario involved in the embodiment of the present invention, although the position of the sensor is fixed, due to the change of the link, the relative position of the LoRa gateway or the repeater and the sensor changes, so the rate also needs to be adjusted to match the current link condition, and the SF value is different according to the distance, as follows:
where dis is distance, in km.
According to the characteristics of LoRa, when the bandwidth and the code rate are fixed, the transmission rate of LoRa is determined by the spreading factor, and the gateway can receive data of different spreading factors at the same time. However, because the rates of different spreading factors are very different, the occupied time of the channel is also very different when the data with the same length is transmitted.
The LoRa packet time is equal to the sum of the preamble time and the packet transmission time, and the transmission time of the preamble is calculated by the following formula:
Tpreamble＝(npreamble+4.25)Tsym
wherein n ispreambleIndicates the set preamble length, TsymIs the number of symbols transmitted per unit time.
The transmission time of the data packet is calculated by the following formula:
where SF is the spreading factor, BW denotes the signal bandwidth, and CR denotes the coding rate.
Finally, the total transmission time T of the data packets with the preset length LoRapacketComprises the following steps:
Tpacket＝Tpreamble+Tpayload
in addition, because the LoRa is transmitted in a pure ALOHA manner, if two data packets collide with each other, the time occupied by the two data packets is the time when the transmission of the next data packet ends minus the time when the transmission of the previous data packet starts, and therefore the non-collision load rate, the collision load rate, and the total load rate are respectively:
P(sf,r)＝Pnot colliding+PCollision of vehicles
Wherein, TpacketFor a predetermined length of LoRa packet transmission time, TtotalThe overall transmission time of the LoRa data packet is shown.
Based on any of the above embodiments, step S3 in the method specifically includes:
acquiring the load rate of the preset network node, the distance between a plurality of sensors and the transmission time between the preset network node and the plurality of sensors;
respectively setting a first weight parameter, a second weight parameter and a third weight parameter;
and carrying out weighted summation on the first weight parameter, the second weight parameter and the third weight parameter, as well as the load rate of the preset network node, the distances among the plurality of sensors and the transmission time between the preset network node and the plurality of sensors to obtain the available link value.
Specifically, the QoS requirements of the sensing service of the sensor, such as the uplink and downlink data flow requirements, the time delay, the bandwidth, and the like, are quantified and scored. Let the function mark (r) be the final score for each gateway/repeater, with higher scores representing a busy node.
mark(r,n)＝αP(sf,r)+βdis(r,n)+θtime(r,n)
Wherein P (SF, r) represents the load rate of the gateway/repeater r on the channel with spreading factor SF, and α, β and θ are the first weight parameter, the second weight parameter and the third weight parameter, respectively; for the gateway, 6 different channels are shared and do not influence each other, the SF value is from 7 to 12, dis (r, n) indicates the distance from a sensor n to r, time (r, n) indicates the transmission time (adopting ADR) between r and the node, and time delay and bandwidth are reflected in the transmission time. For the requirements of uplink and downlink data streams, jitter and other hard limits, when a certain link does not meet the conditions, the mark value of the path is set to be positive infinity.
After each possible link of the Nk sensors is scored, links which do not accord with hard limits are eliminated, and the other links select the communication link which is matched with the best to recover the network fault according to the sequencing result and the improved KM algorithm.
The embodiment of the invention utilizes the characteristics that the communication link between the low-power-consumption wide area network sensor and the gateway has obvious channel delay and adaptive rate and is closely related to the Qos requirement of the sensing service, and when the potential available communication link is found through the redundant data analysis of the network server to recover the link failure, the factors of the same-frequency duty ratio, delay, adaptive rate limit and the Qos requirement of the sensing service of the sensor to be recovered of the communication link related to the gateway and the like are considered according to the discovery result of the communication link, so that the available communication link is subjected to adaptive accurate matching, and the optimal selection of the communication link is realized
Based on any of the above embodiments, the method includes, before step S4:
adding a plurality of virtual sensor nodes on the preset network node side;
based on a KM algorithm, a vertex labeling method is adopted to search the maximum weight in a weighted complete bipartite graph for problem matching;
acquiring a weight value of a connecting edge of any sensor and any gateway;
calculating the number of all nodes connected with each gateway based on the weight value;
and subtracting the number of the actually existing sensors connected with each gateway from the total number of the nodes to obtain the number of the virtual sensor nodes.
Specifically, the KM algorithm solves the problem of finding the maximum-weight perfect matching in the empowerment complete bipartite graph by using a vertex labeling method on the basis of the Hungarian algorithm, the traditional KM algorithm can only process the complete problem, in the scene of the embodiment of the invention, the number of sensors and gateways is obviously unequal, and a plurality of sensors can correspond to the same gateway, so that the model needs to be changed, and the problem can be solved by the maximum-weight perfect matching through the mode of adding virtual nodes on the gateway side and the sensor side. In addition, the improved KM algorithm is a minimum value, since a higher score represents a busier node, and thus embodiments of the present invention may wish to select a node with a lower score. If the sensor can not be connected with the gateway, the weight of the connected edge is positive infinity, and the mark values of the other edges are the weight of the edge. Meanwhile, the weights of the edges of the virtual sensor and any gateway are both 0.
When adding virtual nodes, the patent firstly considers gateway and repeater side nodes, because a gateway with higher duty ratio has more positions to connect new sensors. In the complemented matching model, the same gateway corresponds to virtual nodes with the number being the number of the rest positions of the gateway, and when the gateway side virtual nodes correspond to the same sensor side node, all the edge weights are the same.
It will be appreciated that it becomes critical to calculate the number of locations each gateway has, as shown in fig. 3, for each gateway r:
the number of virtual nodes needed to be added at the sensor side, Numvir, is:
Numvir＝Numplace(R)-N
n is the actual number of sensors present connected to each gateway.
Based on any of the above embodiments, step S4 in the method specifically includes:
obtaining the improved KM algorithm based on the link available score and the number of the plurality of virtual sensor nodes;
acquiring the weighted complete bipartite graph, wherein the weighted complete bipartite graph comprises a sensor side node set and a gateway side node set;
if the sum of the vertex label of any point in the sensor side node set and the vertex label of any point in the gateway side node set is greater than or equal to the weight value of a connecting edge between two points, judging that the vertex label of any point in the sensor side node set and the vertex label of any point in the gateway side node set are feasible vertex labels;
based on the feasible vertex labels, obtaining equal subgraphs of the feasible vertex labels;
and acquiring a maximum weight perfect matching result in the equal subgraph of the feasible vertex labels as the sequencing result based on the improved KM algorithm.
Specifically, let G (X, Y) be an empowerment complete bipartite graph, and | X | ═ Y |, ωijIs xiyjThe vertex index (l), (v) is the index of the vertex v, and a vertex index (l), (v) is such that when X ∈ X, Y ∈ Y, l (X) + l (Y) ≧ ω (xy) represents the weight of the edge xy, this vertex index is called the feasible vertex index.
Wherein X is the set of nodes at the sensor side, Y is the set of nodes at the gateway side, omegaijMark (r, n) represents the score of the link between the gateway or repeater r and the sensor n.
If l (v) is a feasible vertex designation, El(x) l (x) + l (y) ═ ω (xy) }, called Gl＝(V,El) Equal subgraphs are labeled l (v) for feasible vertices.
Kuhn-Munkras has demonstrated that: if G islContaining perfect match M*Then M is*For the maximum weight perfect matching of G, the specific process is as follows:
step 1: selecting initial normal vertex labels l (v) to construct graph GlAt GlOptionally a match M;
step 2: if the middle point of X is covered by M, stopping the algorithm, perfectly matching the maximum weights obtained by several M bits, otherwise, setting u as an M-unsaturated point in X, and making S equal to { u }, and T equal to phi;
and step 3: if it is
Turning to the step 4; otherwise
Taking:
and 4, step 4: selecting
If y is covered by M and yz ∈ M, then S ← S ≈ S { z }, T ← T { y }, go to step 3; otherwise, get GlOne of M can increase the way P (u, y), let M ← M Δ E (P), go to step 2.
After the matching of all sensors is completed, the recovered link is determined according to the matching edge obtained by the improved KM algorithm. And finally, checking the weight of the matching edge, and if the weight is positive infinity, determining that the matching fails or the sensor link fails to recover.
The embodiment of the invention considers that a plurality of key nodes exist in the power internet of things perception network due to the star networking characteristic of the low-power wide area network, and at the moment, the optimal link is selected for recovery, so that the data volume of the key nodes on the optimal link is increased suddenly, the energy consumption is accelerated, and the long-term effective operation of the network is not facilitated. Therefore, when a plurality of alternative recovery links with similar capabilities exist, network load balancing needs to be further considered, and the network communication link needs to be adaptively adjusted from the fault tolerance point of view.
The effect of the method of the embodiment of the invention is described by analyzing according to the actual application scene of the power internet of things.
Fig. 4 shows an actual schematic networking situation of the power internet of things, and as shown in fig. 4, the power internet of things perception network is a highly redundant network. According to the actual scene of the power internet of things perception network, two different situations exist, the area with less relay construction is a sparse scene, and the area with more intensive relay construction is a dense scene. In the initial state, all sensors are connected with the LoRa gateway. When a certain sensor breaks down, if the sensor is directly connected with the gateway, whether other LoRa gateways can be connected or not is firstly searched, if the sensor can be connected, all channels with SF values larger than or equal to the current SF are considered, and if all SF channels of other gateways do not meet the requirements, the repeater is searched.
In a sparse scenario, there are two LoRa gateways, six relays, and all sensors are evenly distributed in a rectangular area of 16km x 20 km. The period is 3600 seconds/packet, i.e. each sensor sends a data packet every hour, the payload is 9. Fig. 5 shows the link recovery rate for the 1000 to 8000 sensor cases, the upper three curves in the picture being a set, showing the effect of three different recovery strategies (LRAKM, distance first algorithm, idle first algorithm) on the link recovery rate at a failure rate of 0.01 (lower). As the number of sensors increases, the recovery rate gradually decreases. The lower three curves in the picture are a set showing the effect of three different recovery strategies on the link recovery rate at a failure rate of 0.03 (higher). The LRAKM algorithm has the most significant advantage when the sensor scale is 2000-7000. When there are less than 2000 sensors, the network load is light; when there are more than 7000 sensors, the network is too heavily loaded, each repeater and gateway has reached the load limit, and therefore the improvement of the algorithm is not ideal.
Figure 6 shows the proportion of energy consumed by each repeater. In the case of a large number of sensors and a large total energy consumption, the LRAKM algorithm is significantly improved in load balancing compared to the distance-first algorithm. The adaptive mechanism proposed in this patent utilizes the LRAKM algorithm to adaptively adjust some communication tasks connected to the critical gateways or repeaters to sub-optimal communication links to achieve network optimization. For dense relaying, in the initial state, all sensors are connected to the LoRa gateway. When a link fails, it is first determined whether other LoRa gateways can be connected. If a connection is possible, all channels with SF values greater than or equal to the current SF will be considered (the distance between the new gateway and the sensor will not be significantly smaller than the original distance). If all SF channels of other gateways do not meet the requirements, a repeater is searched.
In a more dense relay scenario, there are two LoRa gateways and twenty-four repeaters. All sensors are evenly distributed in a rectangular area of 16km x 20 km. The link failure rate is 1% (upper three curves in fig. 7 below) or 3% (lower three curves in fig. 7 below). The period is 3600 seconds/packet and the payload is 9, and fig. 7 shows the link recovery rate in the case of dense relaying. It can be seen that the link recovery rate of the mechanism using the LRAKM algorithm is higher than the other two algorithms. The link recovery rate of all algorithms is kept above 90%.
The embodiment of the invention researches a scheme with heavier network load to test whether the mechanism can be applied to the scheme. The link failure rate is 1% (upper three curves) or 3% (lower three curves), the period is 1800 seconds/packet, i.e. each sensor sends a data packet every half hour, and payload is set to 9.
Fig. 8 shows the link recovery rate in the case of a heavy load. The LRAKM algorithm is significantly improved at high link failure rates and high loads compared to the other two algorithms.
Fig. 9 is a structural diagram of a power internet of things communication link fault adaptive recovery system provided in an embodiment of the present invention, as shown in fig. 9, including: a calculation module 91, a determination module 92, an evaluation module 93 and a processing module 94; wherein:
the calculating module 91 is configured to calculate a duty ratio of a preset network node, and determine a busy degree of the preset network node based on the duty ratio;
the determining module 92 is configured to determine a preset network node for acquiring redundant data according to the busy level, determine a set of alternative network nodes from the preset network node for acquiring redundant data, and acquire all available communication links from the set of alternative network nodes;
the evaluation module 93 is configured to score all available communication links according to preset network attributes of all available communication links and a sensing service QoS requirement constraint of a sensor to be restored, so as to obtain link available scores;
the processing module 94 is configured to rank all available communication links through the improved KM algorithm based on the link availability scores, and match an optimal communication link according to a ranking result to perform network failure recovery.
The system provided by the embodiment of the present invention is used for executing the corresponding method, the specific implementation manner of the system is consistent with the implementation manner of the method, and the related algorithm flow is the same as the algorithm flow of the corresponding method, which is not described herein again.
The embodiment of the invention carries out self-adaptive accurate matching on the available communication links by perceiving and quantifying the service requirements, considering network load balance and adopting a method of communication task unloading of key nodes, and selects the optimal communication link.
Fig. 10 illustrates a physical structure diagram of an electronic device, and as shown in fig. 10, the electronic device may include: a processor (processor)1010, a communication Interface (Communications Interface)1020, a memory (memory)1030, and a communication bus 1040, wherein the processor 1010, the communication Interface 1020, and the memory 1030 communicate with each other via the communication bus 1040. Processor 1010 may call logic instructions in memory 1030 to perform the following method: calculating the duty ratio of a preset network node, and judging the busy degree of the preset network node based on the duty ratio; determining a preset network node for acquiring redundant data according to the busy degree, determining a spare network node set by the preset network node for acquiring redundant data, and acquiring all available communication links in the spare network node set; according to the preset network attributes of all the available communication links and the QoS (quality of service) demand constraint of the perception service of the sensor to be recovered, scoring all the available communication links to obtain available link scores; and sequencing all the available communication links through an improved KM algorithm based on the link available scores, and matching the optimal communication link according to the sequencing result to recover the network fault.
Furthermore, the logic instructions in the memory 1030 can be implemented in software functional units and stored in a computer readable storage medium when the logic instructions are sold or used as independent products. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
In another aspect, an embodiment of the present invention further provides a non-transitory computer-readable storage medium, on which a computer program is stored, where the computer program is implemented to perform the transmission method provided in the foregoing embodiments when executed by a processor, and for example, the method includes: calculating the duty ratio of a preset network node, and judging the busy degree of the preset network node based on the duty ratio; determining a preset network node for acquiring redundant data according to the busy degree, determining a spare network node set by the preset network node for acquiring redundant data, and acquiring all available communication links in the spare network node set; according to the preset network attributes of all the available communication links and the QoS (quality of service) demand constraint of the perception service of the sensor to be recovered, scoring all the available communication links to obtain available link scores; and sequencing all the available communication links through an improved KM algorithm based on the link available scores, and matching the optimal communication link according to the sequencing result to recover the network fault.
The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. With this understanding in mind, the above-described technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in the embodiments or some parts of the embodiments.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (8)
1. A self-adaptive recovery method for a fault of a communication link of an electric power Internet of things is characterized by comprising the following steps:
calculating the duty ratio of a preset network node, and judging the busy degree of the preset network node based on the duty ratio, wherein the preset network node is a gateway or a repeater;
determining a preset network node for acquiring redundant data according to the busy degree, determining a spare network node set by the preset network node for acquiring redundant data, and acquiring all available communication links in the spare network node set;
according to the preset network attributes of all the available communication links and the QoS (quality of service) demand constraint of the perception service of the sensor to be recovered, scoring all the available communication links to obtain available link scores;
based on the link available scores, sequencing all available communication links through an improved KM algorithm, and matching the optimal communication link according to a sequencing result to recover network failures;
the step of ranking all the available communication links through an improved KM algorithm based on the link availability scores, and performing network failure recovery by matching the optimal communication link according to the ranking result further includes:
adding a plurality of virtual sensor nodes on the preset network node side;
based on a KM algorithm, a vertex labeling method is adopted to search the maximum weight in a weighted complete bipartite graph for problem matching;
acquiring a weight value of a connecting edge of any sensor and any gateway;
calculating the number of all nodes connected with each gateway based on the weight value;
subtracting the number of the actually existing sensors connected with each gateway from the total number of the nodes to obtain the number of the virtual sensor nodes;
the step of ranking all the available communication links through an improved KM algorithm based on the link availability scores and matching the optimal communication link according to the ranking results for network failure recovery specifically includes:
obtaining the improved KM algorithm based on the link available score and the number of the plurality of virtual sensor nodes;
acquiring the weighted complete bipartite graph, wherein the weighted complete bipartite graph comprises a sensor side node set and a gateway side node set;
if the sum of the vertex label of any point in the sensor side node set and the vertex label of any point in the gateway side node set is greater than or equal to the weight value of a connecting edge between two points, judging that the vertex label of any point in the sensor side node set and the vertex label of any point in the gateway side node set are feasible vertex labels;
based on the feasible vertex labels, obtaining equal subgraphs of the feasible vertex labels;
and acquiring a maximum weight perfect matching result in the equal subgraph of the feasible vertex labels as the sequencing result based on the improved KM algorithm.
2. The power internet of things communication link fault adaptive recovery method according to claim 1, wherein the calculating of the duty ratio of the preset network node and the judging of the busy degree of the preset network node based on the duty ratio specifically comprise:
in a preset period, setting each sensor to send a data packet with a preset length LoRa at a random time;
setting a spread spectrum factor, and setting a value range of the spread spectrum factor according to the distance between a sensor and the preset network node;
calculating the transmission time of the preset length LoRa data packet based on the value range;
and obtaining the load rate of the preset network node according to the transmission time of the preset length LoRa data packet.
3. The power internet of things communication link fault adaptive recovery method according to claim 2, wherein the calculating of the transmission time of the preset length LoRa data packet based on the value range specifically includes:
calculating to obtain preamble transmission time based on the set preamble length and the unit time transmission symbol number; the number of the transmission symbols in unit time is obtained based on the value range and the signal bandwidth;
calculating to obtain the transmission time of the data packet based on the byte number of the effective load, the length of the CRC byte, the judgment mark of the header, the coding rate, the value range and the signal bandwidth;
and summing the preamble transmission time and the data packet transmission time to obtain the transmission time of the data packet with the preset length LoRa.
4. The power internet of things communication link fault adaptive recovery method according to claim 2, wherein the obtaining of the load rate of the preset network node from the transmission time of the preset length LoRa data packet specifically includes:
obtaining the non-collision load rate of the LoRa data packet based on the transmission time of the LoRa data packet with the preset length and the overall transmission time of the LoRa data packet;
obtaining the collision load rate of the LoRa data packet based on the starting sending time of the previous data packet, the sending time of the next data packet and the overall transmission time of the LoRa data packet;
and summing the no-collision load rate of the LoRa data packets and the collision load rate of the LoRa data packets to obtain the load rate of the preset network nodes.
5. The power internet of things communication link fault adaptive recovery method according to claim 1, wherein the scoring is performed on all available communication links according to preset network attributes of all available communication links and a perceived service QoS requirement constraint of a sensor to be recovered to obtain link available scores, specifically comprising:
acquiring the load rate of the preset network node, the distance between a plurality of sensors and the transmission time between the preset network node and the plurality of sensors;
respectively setting a first weight parameter, a second weight parameter and a third weight parameter;
and carrying out weighted summation on the first weight parameter, the second weight parameter and the third weight parameter, as well as the load rate of the preset network node, the distances among the plurality of sensors and the transmission time between the preset network node and the plurality of sensors to obtain the available link value.
6. The utility model provides a power thing networking communication link trouble self-adaptation recovery system which characterized in that includes:
the calculation module is used for calculating the duty ratio of a preset network node, and judging the busy degree of the preset network node based on the duty ratio, wherein the preset network node is a gateway or a relay;
the determining module is used for determining a preset network node for acquiring redundant data according to the busy degree, determining a standby network node set by the preset network node for acquiring the redundant data, and acquiring all available communication links in the standby network node set;
the evaluation module is used for scoring all the available communication links according to the preset network attributes of all the available communication links and the perception service QoS requirement constraint of the sensor to be recovered to obtain the available values of the links;
the processing module is used for sequencing all the available communication links through an improved KM algorithm based on the link available scores and matching the optimal communication link according to a sequencing result to recover network failures;
before the ranking all available communication links by using the improved KM algorithm based on the link availability scores and matching the optimal communication link according to the ranking result for network failure recovery, the processing module is further configured to:
adding a plurality of virtual sensor nodes on the preset network node side;
based on a KM algorithm, a vertex labeling method is adopted to search the maximum weight in a weighted complete bipartite graph for problem matching;
acquiring a weight value of a connecting edge of any sensor and any gateway;
calculating the number of all nodes connected with each gateway based on the weight value;
subtracting the number of the actually existing sensors connected with each gateway from the total number of the nodes to obtain the number of the virtual sensor nodes;
the sorting all the available communication links through an improved KM algorithm based on the link available scores and the matching of the optimal communication link according to the sorting result to perform network failure recovery specifically include:
obtaining the improved KM algorithm based on the link available score and the number of the plurality of virtual sensor nodes;
acquiring the weighted complete bipartite graph, wherein the weighted complete bipartite graph comprises a sensor side node set and a gateway side node set;
if the sum of the vertex label of any point in the sensor side node set and the vertex label of any point in the gateway side node set is greater than or equal to the weight value of a connecting edge between two points, judging that the vertex label of any point in the sensor side node set and the vertex label of any point in the gateway side node set are feasible vertex labels;
based on the feasible vertex labels, obtaining equal subgraphs of the feasible vertex labels;
and acquiring a maximum weight perfect matching result in the equal subgraph of the feasible vertex labels as the sequencing result based on the improved KM algorithm.
7. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor when executing the program implements the steps of the power internet of things communication link failure adaptive recovery method of any one of claims 1 to 5.
8. A non-transitory computer readable storage medium having a computer program stored thereon, wherein the computer program when executed by a processor implements the steps of the power internet of things communication link failure adaptive recovery method according to any one of claims 1 to 5.
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