CN112672300B - Power consumption data vine type transmission optimization method for medium and low voltage switch cabinet - Google Patents
Power consumption data vine type transmission optimization method for medium and low voltage switch cabinet Download PDFInfo
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Abstract
The invention discloses an electricity consumption data vine type transmission optimization method for a medium and low voltage switch cabinet, which comprises the following steps: s1, establishing a vine network model and a network energy consumption model, wherein the vine network model comprises a network architecture for simulating a vine structure, medium and low voltage switch cabinets distributed on each vine node and a plurality of wireless sensors; s2, establishing clusters for each wireless sensor election cluster head in the network architecture; s3, establishing a shortest transmission path taking the average transmission times of the link data as a weight, and finding one or more candidate shortest transmission paths; and S4, screening out the optimal data transmission path by introducing a path cost function based on the shortest transmission path obtained in the step S3. According to the invention, the energy value of each middle-low voltage switch cabinet node is calculated, the transmission path of the non-cluster-head node is updated, the data loss probability is reduced, the transmission efficiency of all nodes is improved, and the technical defects of low efficiency, data loss and the like of the traditional transmission mode are overcome.
Description
Technical Field
The invention relates to the technical field of wireless data transmission, in particular to a vine-type transmission optimization method for power consumption data of a medium-low voltage switch cabinet.
Background
With the development of social economy, the smart grid develops rapidly, and meanwhile, challenges are provided for technologies such as collection, transmission and analysis of power utilization data, wherein data transmission is a key ring for utilizing data. However, there are few studies on data transmission of low-voltage switch cabinets in office buildings in residential buildings, and it is a very worthy of study how to quickly and accurately transmit data of the low-voltage switch cabinets and ensure low energy consumption in the transmission process.
The vines are special plants, have strong adhesiveness and good interactivity on attached objects (nodes), and are beneficial to the whole attached objects to form a node vine network. The application provides a vine type transmission optimization method suitable for power consumption data transmission of low-voltage switch cabinets in communities or office buildings based on the attachment characteristic of vines to nodes.
Description of specific application background: for a complete community, n office buildings are not supposed to be provided, each office building is provided with a switch cabinet, and each floor of the office building is supposed to have a company, so that m companies are provided. The intelligent electric meter in the switch cabinet can collect the power utilization information of each company, the power utilization information comprises f power indexes, specifically P, Q, i, u, … and S, and the power utilization data information at the moment is a time sequence on the assumption that data are transmitted once every hour, and the length is not assumed to be j. The position of each switch cabinet is set as a data transmission node, so that n nodes exist, and the transmitted data is data with dimensions of m multiplied by f multiplied by j.
The data transmission nodes are large in number, the network operation time is long, the transmitted data volume is large, and a large data sensing network is formed. How to quickly and accurately transmit the electricity utilization information of each company in the office building to the base station with low energy consumption is a very difficult problem. An effective transmission optimization algorithm needs to be designed for the problem.
The traditional data transmission method only considers the shortest path of data transmission, but does not combine with the actual transmission efficiency of a wireless link or the data receiving rate, so that the quality of wireless link selection cannot be accurately reflected, and most of sensing networks do not take the energy of a data transmission node into account, so that the network interrupts transmission due to no energy, or consumes a large amount of energy due to the poor quality of a data transmission path. Aiming at the problems, the invention provides an electricity consumption data vine type transmission optimization method for a medium and low voltage switch cabinet, namely, a transmission network is set to be a vine, points fixed by the vine are network nodes, and data of different attachments (buildings) are communicated in a root system connection mode.
Disclosure of Invention
The invention provides a vine type transmission optimization method for power consumption data of a medium and low voltage switch cabinet, aiming at overcoming the defects of the technology.
Summary of the invention:
firstly, a vine network model is established, then the concept of wireless link data packet receiving rate is introduced to represent an authorized graph of data transmission, a Dijkstra algorithm is adopted to find out a plurality of candidate shortest distance paths, on the basis, the residual energy of nodes on the candidate paths, the average residual energy of the paths and the number of nodes on the paths are comprehensively considered, and an optimal transmission path is selected, so that the purposes of reducing the working time of a wireless network, reducing the energy loss of the whole system and the like are achieved.
The technical scheme adopted by the invention for overcoming the technical problems is as follows:
a vine-type transmission optimization method for power consumption data of a medium and low voltage switch cabinet comprises the following steps:
s1, establishing a vine network model and a network energy consumption model, wherein the vine network model comprises a network architecture for simulating a vine structure, medium and low voltage switch cabinets distributed on each vine node and a plurality of wireless sensors;
s2, establishing clusters for each wireless sensor election cluster head in the network architecture;
s3, establishing a shortest transmission path taking the average transmission times of the link data as a weight, and finding one or more candidate shortest transmission paths;
and S4, screening out the optimal data transmission path by introducing a path cost function based on the shortest transmission path obtained in the step S3.
Further, in step S1, the establishing the vine network model specifically includes the following steps:
M-M grid division is carried out on a cell where a medium-low voltage switch cabinet is installed or an area in the range of an office building, the medium-low voltage switch cabinet is arranged in each grid to serve as a node for data acquisition and transmission, and the position of each node is fixed and has a unique vine ID; and each middle-low voltage switch cabinet node can change the transmitting power according to the distance of the receiving node.
Further, in step S1, the network energy consumption model adopts a first-order radio communication energy consumption model.
Further, the energy consumption in the first-order radio communication energy consumption model is specifically as follows:
when the wireless sensor node transmits the k bit data for a distance d in the network, the energy consumption E is reduced Tx Comprises the following steps:
energy E lost when wireless sensor node receives k bit data Rx Comprises the following steps:
E Rx (k)＝kl
energy E required by wireless sensor node for processing k bit data da-fus Comprises the following steps:
E da-fus ＝kE da
wherein l is total energy consumption of unit bit data sent or received by the wireless sensor node, and if the data energy loss rate transmitted by the non-cluster head child node to the upper layer base station node is l 1 By analogy, when n transmission layers exist, the total data loss ratio is as follows:
l＝l 1 +l 2 +…+l n
E da d is the Euclidean distance between two wireless sensor nodes and d is the energy consumption of unit bit data 0 Is the transmission distance threshold, epsilon fs And ε ms Amplifier power amplification is under a free space channel model and a multipath fading model, respectively.
Further, in step S2, the specifically establishing a cluster for each wireless sensor election cluster head in the network architecture includes:
s21, in the network, selecting a plurality of cluster heads by each wireless sensor node according to an REC algorithm;
s22, broadcasting information of successfully electing cluster heads to the periphery by each cluster head, sending a cluster entering application to the nearest cluster head by each non-cluster head node according to the coordinate position in the network, and receiving the cluster entering application by the corresponding cluster head so as to complete the clustering of the network;
s23, the base station establishes a routing table C of a cluster head according to the network clustering condition finished in the step S22;
s24, the base station sends the routing table C to all cluster heads in the cluster.
Further, in step S3, the establishing the shortest transmission path with the average number of transmission times of the link data as a weight specifically includes the following steps:
s31, introducing the average data transmission times as the wireless link empowerment;
and S32, finding the shortest transmission path through a shortest path algorithm.
Further, in step S31, the step of introducing the average number of data transmissions to give the right to the wireless link specifically includes the following steps:
s311, using p (e) i ) Indicating a radio link e i The formula is defined as follows:
in the above formula, the first and second carbon atoms are,
is one bit of the bitmap and represents a neighbor node v i The reception state of the h-th probe packet; if the data is successfully received, then
Otherwise
W represents the grid side length weight of the vine network model;
s312, defining average data transmission times mu (e) on the wireless link on the basis of the wireless link data packet receiving rate i )：
Further, in step S32, finding one or more shortest transmission paths by Dijkstra shortest path algorithm specifically includes the following steps:
s321, setting a routing table S to represent a node set of which the edge weight of the non-cluster head node is the shortest edge weight;
s322, a non-cluster-head node M in any cluster transmits the collected information to a cluster head Ci of the cluster in a TDMA time slot and marks the Ci;
s323, the cluster head Ci performs data transmission according to the following steps, so that the shortest transmission path from all cluster head nodes to non-cluster head nodes is obtained:
s3231, traversing all the adjacent cluster heads by the cluster head Ci according to the routing table C obtained in the step S2, and obtaining edge weights of the adjacent cluster heads;
s3232, comparing the "edge weights between the non-cluster-head node M and all the adjacent cluster heads of the cluster head Ci" with the "sum of the edge weights between the non-cluster-head node M and the cluster head Ci and the edge weights between the cluster head Ci and the corresponding adjacent cluster head": if the comparison result of the two routing tables is the same, establishing a plurality of sub-routing tables with the same initial state in the routing table S, marking the corresponding adjacent cluster heads of the cluster heads Ci, adding the cluster heads Ci into the corresponding sub-routing tables, and updating the sub-routing tables; otherwise, marking the adjacent cluster head nodes according to the Dijkstra algorithm and updating the routing table S;
s3233, and traversing paths of adjacent nodes of the cluster head Ci according to the method of the steps S3231-S3232 until sub-routing tables of the routing table S contain all nodes of the middle and low voltage switch cabinets.
Further, in step S4, a path cost function is introduced to screen out an optimal data transmission path, the magnitude of the cost function value of the storage path of each sub-routing table is compared, and a path with the smallest cost function value is selected as the optimal path.
Further, in step S4, the screening out the optimal data transmission path by introducing the path cost function specifically includes the following steps:
setting the residual energy coefficient of a certain non-cluster head node as R, and defining a path cost function of a sub-routing table a as follows, wherein the sub-routing table a is a sub-routing table in a routing table S, and the non-cluster head node is taken as a source point:
in the above formula, E Avg-Re (a) Storing the average remaining energy of the path for sub-routing table a, Re (a, b) storing the remaining energy of node b on the path for sub-routing table a, Hop (a) storing the Hop count of the path for sub-routing table a, Hop max Storing the maximum hop count of the path for all the sub-routing tables, wherein alpha, beta and gamma are weight factors with the value larger than 0, alpha + beta + gamma is 1, n is the number of nodes of the path stored in the sub-routing table a, and R (a and b) is the residual energy coefficient of the node b on the path stored in the sub-routing table a;
for any node Q, the residual energy coefficient r (Q) is defined as follows:
in the above formula, Re (Q) is the node residual energy, E 0 Is the node initial energy.
The invention has the beneficial effects that:
1. the invention provides a concept of average transmission times of wireless link data for endowing the wireless link with a weight, overcomes the defect of using Euclidean distance as the weight, and can more clearly reflect the actual transmission condition of the data under the condition of complex and various environments of a cell or an office building.
2. The invention introduces the residual energy coefficient of the node and defines the path cost function, introduces the concept of energy into the wireless data transmission network, can ensure low energy consumption of the wireless data transmission network under the condition of high transmission efficiency, and has important practical significance at the moment of energy shortage.
3. According to the invention, the energy value of each middle-low voltage switch cabinet node is calculated, the transmission path of the non-cluster-head node is updated, the data loss probability is reduced, the transmission efficiency of all nodes is improved, and the technical defects of low efficiency, data loss and the like of the traditional transmission mode are overcome.
Drawings
Fig. 1 is a schematic diagram of cluster head information aggregation according to an embodiment of the present invention.
Fig. 2 is a schematic diagram of a clustered wireless sensor node according to an embodiment of the present invention.
Fig. 3 is a schematic diagram of an optimal route selection according to an embodiment of the present invention.
Detailed Description
In order to facilitate a better understanding of the invention for those skilled in the art, the invention will be described in further detail with reference to the accompanying drawings and specific examples, which are given by way of illustration only and do not limit the scope of the invention.
The invention provides an electricity consumption data vine type transmission optimization method of a medium and low voltage switch cabinet based on research on a plant vine structure.
The method for optimizing the vine-type transmission of the electricity consumption data of the medium and low voltage switch cabinet comprises the following steps:
s1, establishing a vine network model and a network energy consumption model, wherein the vine network model comprises a network architecture for simulating a vine structure, medium and low voltage switch cabinets distributed on each vine node and a plurality of wireless sensors;
s2, establishing clusters for each wireless sensor election cluster head in the network architecture;
s3, establishing a shortest transmission path taking the average transmission times of the link data as a weight, and finding one or more candidate shortest transmission paths;
and S4, screening out the optimal data transmission path by introducing a path cost function based on the shortest transmission path obtained in the step S3.
As shown in fig. 1, the upper layer is represented as a base station, and the lower layer is represented as a node layer where the vine network is located, and the two layers are connected through cluster head communication.
In step S1 of this embodiment, establishing a vine network model specifically includes, first, performing M × M grid division on an area within a range of a cell where a low and medium voltage switch cabinet is installed, setting the low and medium voltage switch cabinet in each grid as a node for data acquisition and transmission, and setting a position of each node to be fixed and to have a unique vine ID; setting the types of all medium and low voltage switch cabinets in the vine network to be the same as the initial configuration, and setting the energy of the base station to be infinite and the position to be fixed; setting the specific position of each medium and low voltage switch cabinet node in the vine network area is known; and the nodes of the low-voltage switch cabinet in each transmission can change the transmission power according to the distance of the receiving nodes.
In step S1 of this embodiment, the network energy consumption model adopts a first-order radio communication energy consumption model, and energy consumption in the first-order radio communication energy consumption model is specifically as follows:
when the wireless sensor node in the network transmits the k bit data for a distance d, the energy consumption E of the wireless sensor node is Tx Comprises the following steps:
energy E lost when wireless sensor node receives k bit data Rx Comprises the following steps:
E Rx (k)＝kl
energy E required by wireless sensor node for processing k bit data da-fus Comprises the following steps:
E da-fus ＝kE da
wherein l is total energy consumption of unit bit data sent or received by the wireless sensor node, and if the data energy loss rate transmitted by the non-cluster head child node to the upper layer base station node is l 1 By analogy, when n transmission layers exist, the total data loss ratio is as follows:
l＝l 1 +l 2 +…+l n
E da d is the Euclidean distance between two wireless sensor nodes and d is the energy consumption of unit bit data 0 Is the transmission distance threshold, epsilon fs And ε ms Amplifier power amplification is under a free space channel model and a multipath fading model, respectively.
In step S2 in this embodiment, the creating a cluster for each wireless sensor election cluster head in the network architecture includes the following steps:
s21, in the network, each wireless sensor node elects a plurality of cluster heads according to the REC algorithm, after the REC algorithm is partitioned and layered according to the actual application scene, each member sends an information data packet to the base station node, the distance between each node and the base station node is calculated by the following formula, and the point with the minimum distance is selected as the cluster head of the area.
In the above formula, i is a member node, j is a base station node, X i 、Y i As member node coordinates, X j 、Y j Are base station node coordinates.
The number of the cluster heads is determined by an actual application scene, a dynamic cluster head mode is adopted, the remaining energy factors and the distance influence factors are compared, and the cluster heads are selected in a partition mode, so that the problems of unreasonable cluster head number, redundant data communication and the like can be effectively solved, the energy balance consumption of the vine network is facilitated, and the service life of the network is prolonged.
S22, each cluster head broadcasts information of successfully electing the cluster head to the periphery, each non-cluster head node sends a cluster entering application to the nearest cluster head according to the coordinate position in the network, and the corresponding cluster head receives the cluster entering application to complete the clustering of the network, so that the stepped uniform transmission of data can be ensured when the cluster head is communicated with the base station node, and the communication reliability is reduced and the energy consumption is improved instead of point-to-point single long-distance transmission. Fig. 2 shows clustered wireless sensor nodes, where black dots pointed by arrows represent cluster head nodes, and other gray dots leading out arrows represent non-cluster head nodes.
And S23, the base station establishes a routing table C (L, N, E) of a cluster head according to the network clustering condition finished in the step S22, wherein L represents the cluster head position, N represents the node number, and E represents the residual energy of the corresponding cluster head.
S24, the base station sends the routing table C (L, N, E) to all cluster heads in the cluster.
In step S3 of this embodiment, the method for establishing the shortest transmission path with the average number of transmission times of link data as a weight includes the following steps:
s31, leading in the average data transmission times as the empowerment of the wireless link;
and S32, finding the shortest transmission path through a shortest path algorithm.
Specifically, in step S31, the step of introducing the average number of data transmissions to give the right to the wireless link specifically includes the following steps:
s311, using p (e) i ) Indicating a radio link e i The formula is defined as follows:
in the above formula, the first and second carbon atoms are,
is one bit of the bitmap and represents a neighbor node v i The reception state of the h-th probe packet; if the data is successfully received, then
Otherwise
W represents the grid side length weight of the vine network model;
s312, defining average data transmission times mu (e) on the wireless link on the basis of the wireless link data packet receiving rate i )：
Specifically, in step S32, finding one or more shortest transmission paths by Dijkstra shortest path algorithm specifically includes the following steps:
s321, setting a routing table S to indicate that the edge weight of the non-cluster-head node M is a node set of the shortest edge weight;
s322, a non-cluster-head node M in any cluster transmits the collected information to a cluster head Ci of the cluster in a TDMA time slot and marks the Ci;
s323, the cluster head Ci performs data transmission according to the following steps, so that the shortest transmission path from all cluster head nodes to non-cluster head nodes is obtained:
s3231, traversing all the adjacent cluster heads by the cluster head Ci according to the routing table C obtained in the step S2, and obtaining edge weights of the adjacent cluster heads;
s3232, comparing the "edge weights between the non-cluster-head node M and all the adjacent cluster heads of the cluster head Ci" with the "sum of the edge weights between the non-cluster-head node M and the cluster head Ci and the edge weights between the cluster head Ci and the corresponding adjacent cluster head": if the comparison result of the two routing tables is the same, establishing a plurality of sub-routing tables with the same initial state in the routing table S, marking the corresponding adjacent cluster heads of the cluster heads Ci, adding the cluster heads Ci into the corresponding sub-routing tables, and updating the sub-routing tables; otherwise, marking the adjacent cluster head nodes according to the Dijkstra algorithm and updating the routing table S;
s3233, the adjacent nodes of the cluster head Ci traverse the path according to the method of the steps S3231-S3232 until the sub-routing table of the routing table S contains all the nodes of the middle and low voltage switch cabinets.
In this embodiment, based on the shortest transmission path obtained in step S3, a path cost function is introduced to screen out an optimal data transmission path, which specifically includes the following steps:
setting the residual energy coefficient of a certain non-cluster head node as R, and defining a path cost function of a sub-routing table a as follows, wherein the sub-routing table a is a sub-routing table in a routing table S, and the non-cluster head node is taken as a source point:
in the above formula, E Avg-Re (a) Storing the average remaining energy of the path for sub-routing table a, Re (a, b) storing the remaining energy of node b on the path for sub-routing table a, Hop (a) storing the Hop count of the path for sub-routing table a, Hop max Storing the maximum hop count of the path for all the sub-routing tables, wherein alpha, beta and gamma are weight factors with the value larger than 0, alpha + beta + gamma is 1, n is the number of nodes of the path stored in the sub-routing table a, and R (a and b) is the residual energy coefficient of the node b on the path stored in the sub-routing table a;
for any node Q, the residual energy coefficient r (Q) is defined as follows:
in the above formula, Re (Q) is the node residual energy, E 0 Is the node initial energy.
And comparing the magnitude of the cost function value of the storage path of each sub-routing table, and selecting the path with the minimum cost function value as the optimal path. Specifically, as shown in fig. 3, the energy of the neighboring cluster head of the top-left child node (i.e., the non-cluster head node) reaches the upper limit, and therefore, another cluster head node with the remaining energy is selected nearby.
The foregoing merely illustrates the principles and preferred embodiments of the invention and many variations and modifications may be made by those skilled in the art in light of the foregoing description, which are within the scope of the invention.
Claims (7)
1. A vine-type transmission optimization method for power consumption data of a medium and low voltage switch cabinet is characterized by comprising the following steps:
s1, establishing a vine network model and a network energy consumption model, wherein the vine network model comprises a network architecture for simulating a vine structure, medium and low voltage switch cabinets distributed on each vine node and a plurality of wireless sensors;
s2, establishing clusters for each wireless sensor election cluster head in the network architecture;
s3, establishing a shortest transmission path taking the average transmission times of the link data as a weight, and finding one or more candidate shortest transmission paths; the establishing of the shortest transmission path with the average transmission times of the link data as a weight value comprises the following steps: s31, introducing average data transmission times as wireless link empowerment, S32, searching the shortest transmission path through the shortest path algorithm;
in step S31, the step of introducing the average number of data transmissions to give the right to the wireless link specifically includes the following steps:
s311, using p (e) i ) Indicating a radio link e i The formula is defined as follows:
in the above formula, the first and second carbon atoms are,
is one bit of the bitmap and represents a neighbor node v i The reception state of the h-th probe packet; if the data is successfully received, then
Otherwise
W represents a grid side length weight of the vine network model;
s312, defining average data transmission times mu (e) on the wireless link on the basis of the wireless link data packet receiving rate i )：
In step S32, one or more shortest transmission paths are found through Dijkstra shortest path algorithm, which specifically includes the following steps:
s321, setting a routing table S to represent a node set of which the edge weight of the non-cluster head node is the shortest edge weight;
s322, a non-cluster-head node M in any cluster transmits the collected information to a cluster head Ci of the cluster in a TDMA time slot and marks the Ci;
s323, the cluster head Ci performs data transmission according to the following steps, so that the shortest transmission path from all cluster head nodes to non-cluster head nodes is obtained:
s3231, traversing all the adjacent cluster heads by the cluster head Ci according to the routing table C obtained in the step S2, and obtaining edge weights of the adjacent cluster heads;
s3232, comparing the edge weights between the non-cluster-head node M and all the neighboring cluster heads of the cluster head Ci with the sum of the edge weights between the non-cluster-head node M and the cluster head Ci and the edge weights between the cluster head Ci and the corresponding neighboring cluster head: if the comparison result of the two routing tables is the same, establishing a plurality of sub-routing tables with the same initial state in the routing table S, marking the corresponding adjacent cluster heads of the cluster heads Ci, adding the cluster heads Ci into the corresponding sub-routing tables, and updating the sub-routing tables; otherwise, marking the adjacent cluster head nodes according to the Dijkstra algorithm and updating the routing table S;
s3233, traversing paths of adjacent nodes of the cluster head Ci according to the method of the steps S3231-S3232 until sub-routing tables of the routing table S contain all nodes of the middle and low voltage switch cabinets;
and S4, screening out the optimal data transmission path by introducing a path cost function based on the shortest transmission path obtained in the step S3.
2. The method for optimizing the vine-type transmission of the electricity consumption data of the medium and low voltage switch cabinet according to claim 1, wherein the step S1 of establishing the vine network model specifically includes the following steps:
M-M grid division is carried out on a cell where a medium-low voltage switch cabinet is installed or an area in the range of an office building, the medium-low voltage switch cabinet is arranged in each grid to serve as a node for data acquisition and transmission, and the position of each node is fixed and has a unique vine ID; and each middle-low voltage switch cabinet node can change the transmitting power according to the distance of the receiving node.
3. The method for optimizing the data transmission of medium and low voltage switchgear cabinet according to claim 1, wherein in step S1, the network energy consumption model is a first-order radio communication energy consumption model.
4. The method for optimizing the tendril-type transmission of the electricity consumption data of the medium and low voltage switch cabinet according to claim 3, wherein the energy consumption in the first-order radio communication energy consumption model is as follows:
when the wireless sensor node transmits the k bit data for a distance d in the network, the energy consumption E is reduced Tx Comprises the following steps:
energy E lost when wireless sensor node receives k bit data Rx Comprises the following steps:
E Rx (k)＝kl
energy E required by wireless sensor node for processing k bit data da-fus Comprises the following steps:
E da-fus ＝kE da
wherein l is total energy consumption of unit bit data sent or received by the wireless sensor node, and if the data energy loss rate transmitted by the non-cluster head child node to the upper layer base station node is l 1 By analogy, when n transmission layers exist, the total data loss ratio is as follows:
l＝l 1 +l 2 +…+l n
E da d is the Euclidean distance between two wireless sensor nodes and d is the energy consumption of unit bit data 0 Is the transmission distance threshold, epsilon fs And ε ms Amplifier power amplification is under a free space channel model and a multipath fading model, respectively.
5. The method for optimizing the vine type transmission of the electricity consumption data of the medium and low voltage switch cabinets according to claim 1, wherein the step S2 of establishing a cluster for each wireless sensor electing a cluster head in the network architecture includes the following steps:
s21, in the network, selecting a plurality of cluster heads by each wireless sensor node according to an REC algorithm;
s22, broadcasting information of successfully electing cluster heads to the periphery by each cluster head, sending a cluster entering application to the nearest cluster head by each non-cluster head node according to the coordinate position in the network, and receiving the cluster entering application by the corresponding cluster head so as to complete the clustering of the network;
s23, the base station establishes a routing table C of a cluster head according to the network clustering condition finished in the step S22;
s24, the base station sends the routing table C to all cluster heads in the cluster.
6. The method for vine-type transmission optimization of electricity consumption data of medium and low voltage switch cabinets according to claim 1, wherein in step S4, a path cost function is introduced to screen out an optimal data transmission path, the magnitude of the cost function value of the storage path of each sub-routing table is compared, and the path with the minimum cost function value is selected as the optimal path.
7. The method for optimizing the tendril-type transmission of the electricity consumption data of the medium and low voltage switch cabinet according to claim 6, wherein the step S4 of introducing the path cost function to screen out the optimal data transmission path specifically comprises the following steps:
setting the residual energy coefficient of a certain non-cluster head node as R, and defining a path cost function of a sub-routing table a as follows, wherein the sub-routing table a is a sub-routing table in a routing table S, and the non-cluster head node is taken as a source point:
in the above formula, E Avg-Re (a) Storing the average remaining energy of the path for sub-routing table a, Re (a, b) storing the remaining energy of node b on the path for sub-routing table a, Hop (a) storing the Hop count of the path for sub-routing table a, Hop max Storing the maximum hop count of the path for all the sub-routing tables, wherein alpha, beta and gamma are weight factors with the value larger than 0, alpha + beta + gamma is 1, n is the number of nodes of the path stored in the sub-routing table a, and R (a and b) is the residual energy coefficient of the node b on the path stored in the sub-routing table a;
for any node Q, the residual energy coefficient r (Q) is defined as follows:
in the above formula, Re (Q) is the node residual energy, E 0 Is the node initial energy.
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