CN113242274B - Information grading return method for railway disaster prevention monitoring system - Google Patents

Abstract

The invention relates to the technical field of railway disaster prevention monitoring wireless communication, in particular to a railway disaster prevention monitoring system information grading return method. According to the method, the computing power of the disaster prevention monitoring node is fully utilized to carry out edge side preprocessing and feature classification on the monitoring data, and a state-driven information returning strategy is adopted for safety-related abnormal information, so that the real-time performance of safety-related information returning is guaranteed; and for the data information in the normal state, a variable-period information returning strategy is adopted according to the state of the disaster prevention monitoring node, so that the periodic integrity of the conventional information returning is ensured. By combining the advantages of the state-driven and variable-period information feedback strategies, on the premise of guaranteeing safe and relevant information real-time transmission, the energy and bandwidth resource waste caused by redundant information feedback is effectively reduced, and the life cycle and the service capability of the monitoring system are improved.

Description

The method of information classification back transmission of railway disaster prevention monitoring system

technical field

The invention relates to the technical field of wireless communication for railway disaster prevention monitoring, in particular to a method for hierarchically returning information of a railway disaster prevention monitoring system.

Background technique

With the substantial increase in operating speed and the rapid growth of operating mileage, railways have become one of the main modes of travel for passengers and express freight in China, and an important pillar of my country's national economic and social development. However, my country's railway lines cover a wide range and the operating environment is complex and changeable, which brings great challenges to the safety and reliability of railway system operation. The technologies of real-time acquisition, transmission, and calculation of disaster-causing factor information play an increasingly important role in railway disaster prevention and control, providing rich big data support and effective technologies for risk assessment, early warning, and control of railway operation systems. support. At present, the disaster prevention monitoring systems along the railway line mostly use wired communication or mobile communication such as 4G to transmit monitoring information. However, in areas with complex terrain and environment, the deployment of power and network resources along the railway lines is difficult, the cost is high, and the quality of mobile network signals is poor, making it difficult to ensure the effective transmission of disaster prevention monitoring information. With the development of wireless transmission technology, the construction of a local transmission system based on wireless communication in areas with complex terrain and environment will effectively solve this problem. Nodes or base stations send information to the data center for analysis and processing.

At present, most of the disaster prevention monitoring data along the railway adopts a fixed sampling frequency for data perception and transmission, which can maintain the integrity of the monitoring data return to the greatest extent. However, the sampling frequency of this transmission method plays a decisive role in the monitoring effect. On the one hand, a large sampling frequency will increase the amount of redundant data transmission and the corresponding waste of energy resources; Security risks.

Therefore, a technical problem that needs to be solved by those skilled in the art is: how to use the edge computing capability of the railway disaster prevention monitoring system to preprocess and identify the monitoring data state, so as to return the data according to the level of information security.

SUMMARY OF THE INVENTION

In view of the above technical problems, the present invention provides a method for hierarchically transmitting information of a railway disaster prevention monitoring system, which makes full use of the edge computing capability of railway disaster prevention monitoring nodes, adopts a hierarchical return method for data of different security levels, and maintains the maximum extent possible. The real-time nature of the return of safety-related information, and the maximization of the overall life cycle of the monitoring system.

The present invention is achieved through the following technical solutions:

A method for hierarchically returning information of a railway disaster prevention monitoring system, the method specifically comprises the following steps:

Step S101: assign a corresponding label [1, 2, 3, ..., N] to each monitoring node in the railway disaster prevention monitoring system, and initialize the state of each node; the initial state of all nodes in the first round is normal; From the second round, the nodes are classified and labeled according to their state and data state;

Step S102: Utilize the perception capability of each monitoring node of the railway disaster prevention monitoring system to obtain the disaster-causing element data of the monitoring area in real time; wherein, the disaster-causing elements include wind, rain, snow, earthquake and foreign body intrusion;

Step S103: Using the computing capability of each monitoring node of the railway disaster prevention monitoring system, perform edge-end preprocessing on the obtained disaster-causing element data to realize feature extraction for data analysis;

Step S104: according to the node status identification result in step S101, determine whether the status of each node is normal; if the node identification status is normal, go to step S105; if the node identification status is abnormal, go to step S109;

Step S105: For nodes whose node identification status is normal, according to the data features extracted in step S103, it is judged whether the monitoring data of the node is normal according to the risk level of the disaster-causing element preset by the system; if the monitoring data is normal, that is, the risk level of the monitoring data is 0, Go to step S106; if the monitoring data is abnormal, that is, the risk level of the monitoring data is not 0, go to step S107;

Step S106: the monitoring data risk level is 0, and the corresponding node enters the preset regular periodic transmission mode, that is, the data is returned according to the regular sampling frequency f preset by the system;

Step S107: if the monitoring data risk level is not 0, mark the corresponding node as an isolation node, and set the isolation period to T=0; then proceed to step S108;

Step S108: For the isolated nodes whose monitoring data risk level is not 0, a preset abnormal data real-time transmission mode is adopted, and the data to be monitored is transmitted in a manner with minimum delay when energy and bandwidth resources allow;

Step S109: For a node whose node identification status is abnormal, according to the data feature extracted in step S103, it is judged whether the monitoring data of the node is normal according to the risk level of the disaster-causing element preset by the system;

If the monitoring data is abnormal, that is, the risk level of the monitoring data is not 0, go to step S107 and step S108 to perform data transmission; if the monitoring data is normal, that is, the risk level of the monitoring data is 0, go to step S110;

Step S110: Change the isolation period to T=T+1, and then proceed to step S111;

Step S111: Determine whether the isolation period of the isolation node has expired, that is, whether T has reached Tmax, where the maximum value of the isolation period Tmax is preset by the system and is a constant; if the isolation period is not reached, go to step S112, if the isolation period is reached, go to step S113 ;

Step S112: If the isolation period is not reached, that is, T<=Tmax, the corresponding node enters the isolation period transmission mode, that is, the node performs data transmission at a sampling frequency f ^/ that is higher than the preset conventional sampling frequency f;

Step S113: when the isolation period is reached, that is, T>Tmax, the corresponding node enters the preset regular periodic transmission mode, that is, data return is performed according to the regular sampling frequency f preset by the system;

Step S114: after all nodes have determined the transmission mode, the system establishes a multi-node coordinated transmission multi-objective optimization model according to the transmission mode and transmission requirements of each node, and optimizes the transmission route of each node;

Step S115: The monitoring data is transmitted according to the optimized communication protocol, and after the transmission, the process goes to step S101.

Further, in step S114, the specific method for establishing a multi-node coordinated transmission multi-objective optimization model includes:

(1) Establish a node delay minimization model:

F ₁ (i)=minL(i), i=1,2,...,N

In the formula, F ₁ (i) represents the node delay minimization optimization objective function; i represents the ith node, and N is the total number of nodes in the railway disaster prevention monitoring system;

(2) Establish a node energy minimization model:

F ₂ (i)=minE(i), i=1,2,...,N

F ₂ (i) represents the optimization objective function of node energy consumption minimization;

The energy consumption of each node is divided into three stages: data reception, data processing and data transmission. The total energy consumption E(i) of a single node is defined as:

E(i)=E _r (i)×f _r (i)+E _d (i)×f _d (i)+E _t (i)×f _t (i)

In the formula, f _r (i) is the data receiving frequency of node i; f _d (i) is the data processing frequency of node i; f _t (i) is the data sending frequency of node i; E _r (i) represents node i E _d (i) represents the unit data processing energy consumption of node i, and E _t (i) represents the unit data transmission energy consumption of node i;

(3) Establish a model of energy consumption balance between nodes:

表示所有节点能耗的平均值；F ₃ (i) represents the energy consumption equalization optimization objective function among nodes;

Represents the average energy consumption of all nodes;

(4) Establish a multi-node cooperative transmission multi-objective optimization model:

Among them, F ₁ ^* (i), F ₂ ^* (i), F ₃ ^* (i) are the normalization of F ₁ (i), F ₂ (i), and F ₃ (i) after normalization processing, respectively form;

λ _i , γ _i , η _i are 0-1 variables, used for different types of node selection;

Further, in step S101:

The nodes are classified according to the node state and data state. The classified nodes include:

Class A node: the node is normal and the data is normal;

Class B node: the node is normal, the data is abnormal;

Type C node: node exception, data exception;

D-type nodes: abnormal node, normal data, node isolation period;

Class E node: node abnormality, normal data, node isolation period expires;

Mark the data sending and receiving capabilities of nodes according to the node classification. The marking types include:

For data of class A nodes: low frequency receiving and sending;

For data of class B nodes: send in real time;

For data of class C nodes: send in real time;

For data of class D nodes: high frequency transmission;

For data of class E nodes: low frequency transmission and reception.

Beneficial technical effects of the present invention:

The method provided by the invention designs an information grading return method combining state drive and variable period according to the real-time risk assessment requirements of the railway disaster prevention monitoring system and the limited energy and bandwidth resources of the linear wireless transmission system. First, make full use of the computing power of disaster prevention monitoring nodes to perform edge-side preprocessing and feature classification on monitoring data, and adopt a state-driven information return strategy for safety-related abnormal information, which ensures the real-time nature of safety-related information return; secondly , For the data information in the normal state, according to the state of the disaster prevention monitoring node, the variable period information return strategy is adopted to ensure the periodic integrity of the regular information return. Combining the advantages of state-driven and variable-period information return strategies, on the premise of ensuring real-time transmission of security-related information, it effectively reduces the waste of energy and bandwidth resources caused by redundant information return, and improves the life cycle of the monitoring system. service capacity. In addition, for nodes that have recently generated abnormal data, the frequency of data return is increased for a period of time, which is used for situation identification, prediction and prevention and control of disaster-causing factors; finally, for nodes that are in a normal state, a lower frequency of data return is used. , to minimize unnecessary energy consumption and effectively ensure the stability of disaster prevention status monitoring.

Description of drawings

Fig. 1 is a flow chart of a method for hierarchically returning information of a railway disaster prevention monitoring system according to an embodiment of the present invention;

Fig. 2 is the overall structure diagram of a railway disaster prevention monitoring system information classification back transmission system according to an embodiment of the present invention;

FIG. 3 is a flow chart of the realization of a method for hierarchically returning information of a railway disaster prevention monitoring system according to an embodiment of the present invention.

Detailed ways

In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention, but not to limit the present invention.

On the contrary, the present invention covers any alternatives, modifications, equivalents and arrangements within the spirit and scope of the present invention as defined by the appended claims. Further, in order to give the public a better understanding of the present invention, some specific details are described in detail in the following detailed description of the present invention. The present invention can be fully understood by those skilled in the art without the description of these detailed parts.

Referring to FIG. 1 , a flowchart of a method for hierarchically returning information of a railway disaster prevention monitoring system is shown, and the specific method includes:

Step S101 : assign a corresponding label [1, 2, 3, ..., N] to each monitoring node in the railway disaster prevention monitoring system, and initialize the state of each node; the initial state of the first round is normal; At the beginning of the round, the nodes are classified and labeled according to the node state and data state;

Step S102: Using the perception capability of each monitoring node of the railway disaster prevention monitoring system to obtain the disaster-causing element data of the monitoring area in real time;

Step S103: Using the computing capability of each monitoring node of the railway disaster prevention monitoring system, perform edge-end preprocessing on the obtained disaster-causing element data to realize feature extraction for data analysis;

Step S104: according to the node status identification result in step S101, determine whether the status of each node is normal; if the node identification status is normal, go to step S105; if the node identification status is abnormal, go to step S109;

Step S105: For a node whose node identification status is normal, according to the data characteristics extracted in step S103, it is judged whether the monitoring data of the node is normal according to the risk level of the disaster-causing element preset by the system; if the monitoring data is normal, that is, the risk level of the monitoring data is 0 , go to step S106; if the monitoring data is abnormal, that is, the risk level of the monitoring data is not 0, go to step S107;

Step S106: the monitoring data risk level is 0, and the corresponding node enters the preset regular periodic transmission mode, that is, the data is returned according to the regular sampling frequency f preset by the system;

Step S107: if the monitoring data risk level is not 0, mark the corresponding node as an isolation node, and set the isolation period to T=0; then proceed to step S108;

Step S108: For the isolated nodes whose monitoring data risk level is not 0, a preset abnormal data real-time transmission mode is adopted, and the data to be monitored is transmitted in a manner with minimum delay when energy and bandwidth resources allow;

Step S109: For a node whose node identification status is abnormal, according to the data feature extracted in step S103, it is judged whether the monitoring data of the node is normal according to the risk level of the disaster-causing element preset by the system;

If the monitoring data is abnormal, that is, the risk level of the monitoring data is not 0, go to step S107 and step S108 to perform data transmission; if the monitoring data is normal, that is, the risk level of the monitoring data is 0, go to step S110;

Step S110: Change the isolation period to T=T+1, and then proceed to step S111;

Step S111: Determine whether the isolation period of the isolation node has expired, that is, whether T has reached Tmax, where the maximum value of the isolation period Tmax is preset by the system and is a constant; if the isolation period is not reached, go to step S112, if the isolation period is reached, go to step S113 ;

Step S112: If the isolation period is not reached, that is, T<=Tmax, the corresponding node enters the isolation period transmission mode, that is, the node performs data transmission at a sampling frequency f ^/ that is higher than the preset conventional sampling frequency f to ensure that the node The integrity of data transmission during the isolation period is conducive to the assessment and prediction of the state of disaster-causing elements in the region;

Step S113: when the isolation period is reached, that is, T>Tmax, the corresponding node enters the preset regular periodic transmission mode, that is, data return is performed according to the regular sampling frequency f preset by the system;

Step S114: after all nodes have determined the transmission mode, the system establishes a multi-node coordinated transmission multi-objective optimization model according to the transmission mode and transmission requirements of each node, and optimizes the transmission route of each node;

Specifically, the data entering the abnormal data real-time transmission mode node shall be transmitted in the mode with the highest priority and the smallest delay; the data entering the isolation period transmission mode node shall be transmitted in a high-frequency transmission mode to ensure the integrity of data monitoring; The data of the transmission mode node adopts the low frequency transmission mode to ensure the efficient use of energy and bandwidth resources.

Step S115: The monitoring data is transmitted according to the optimized communication protocol, and after the transmission, the process goes to step S101.

In this embodiment, in step S114, the establishment of a multi-node coordinated transmission multi-objective optimization model is specifically:

Generally speaking, the wireless sensor nodes of the railway disaster prevention monitoring system are all single-antenna, that is, each node can only process the transmission (receive/transmit) of a single information at the same time point. In the linear sensor network, each node plays the roles of information sender, processor and receiver at the same time. In order to avoid data transmission channel conflict, the channel is divided into multiple time slots, each of which is responsible for different types of data processing tasks. The transmission delay of data at node i is defined as:

L(i)=Lr(i)+Lw(i)+Lt(i)

Among them, Lr(i) is the data receiving delay, Lw(i) is the data transmission waiting delay, which is determined by the data processing time and transmission priority, and Lt(i) is the data sending time;

The transmission delay of node data from source to sink is defined as:

Among them, H is the total number of hops; since the data transmission and reception between nodes are continuous, the sending and receiving time of adjacent nodes can be combined;

The energy consumption of each node is divided into three stages: data reception, data processing and data transmission. The total energy consumption of a single node is defined as:

E(i)=E _r (i)×f _r (i)+E _d (i)×f _d (i)+E _t (i)×f _t (i)

where f _r (i) is the data receiving frequency of node i, which is determined by the pre-sequence nodes that have a direct data transmission relationship with the node; f _d (i) is the data processing frequency of node i, which is determined by the data sensing frequency. All data needs to be preprocessed in real time; f _t (i) is the data sending frequency of node i, which is determined by the data sending mode of the node.

First, this patent establishes three single-objective models, namely, a delay minimization model, an energy consumption minimization model and an energy consumption equalization model. Among them, the node delay minimization model is:

F ₁ (i)=minL(i), i=1,2,...,N

The energy minimization model is:

F ₂ (i)=minE(i), i=1,2,...,N

The energy consumption equalization model is:

The multi-objective optimization model of multi-node cooperative transmission is:

Among them, F ₁ ^* (i), F ₂ ^* (i), F ₃ ^* (i) are the normalized forms of F ₁ (i), F ₂ (i), F ₃ (i), respectively, λ _i , γ _i , η _i are 0-1 variables, used for different types of node selection:

The above model can effectively ensure that the transmission delay of abnormal nodes (node identification status and node data are abnormal) is minimized, and the real-time information transmission is guaranteed; isolated nodes (node identification status is abnormal, node data is normal) transmission delay and energy consumption are minimized at the same time. The energy consumption of normal nodes (node identification status and node data are normal) is balanced to improve the system life cycle.

Referring to Fig. 2, the overall structure entity diagram of the present invention is shown, and the general idea is:

Railway disaster prevention monitoring sensors are mainly composed of 5 types of wind speed sensor, rain sensor, snow sensor, earthquake sensor and foreign object sensor. Each type of sensor is composed of four parts: sensing unit, computing unit, communication unit and energy unit. The energy consumption of each unit are supplied by the energy unit.

Before the above five types of sensors send data, firstly, judge the identification status of the node, which are the normal state and the isolated state; secondly, analyze the data of the node in the isolated state to determine whether there is abnormal data; thirdly, The transmission mode is selected according to the node identification status and data status. The normal node adopts the periodic transmission mode of low sampling frequency, the node that isolates the normal data of the node adopts the periodic transmission mode of high sampling frequency, and the node that isolates the abnormal data of the node adopts the real-time transmission mode. mode; fourth, all nodes jointly participate in the construction and optimization of the multi-node cooperative transmission multi-objective optimization model; fifth, the communication protocol is designed according to the optimization results, and the system performs data transmission according to the communication protocol.

Referring to Fig. 3, the method realization flow chart of the example of the present invention is shown, and the concrete steps are:

Step 1. Classify the nodes according to the node state and data state. The classified nodes include:

Class A node: the node is normal and the data is normal;

Class B node: the node is normal, the data is abnormal;

Type C node: node exception, data exception;

D-type nodes: abnormal node, normal data, node isolation period;

Class E node: node abnormality, normal data, node isolation period expires;

Step 2: Mark the data sending and receiving capabilities of the node according to the node classification, and the marking types include:

For data of class A nodes: low frequency receiving and sending;

For data of class B nodes: send in real time;

For data of class C nodes: send in real time;

For data of class D nodes: high frequency transmission;

For data of class E nodes: low frequency transmission and reception;

Step 3: Initialize the multi-hop link matrix between nodes according to the data sending and receiving capabilities of the nodes, and the transmission links between nodes are represented by f _i,j :

Step 4: Establish a node delay minimization model,

F ₁ (i)=minL(i), i=1,2,...,N

Generally speaking, the wireless sensor nodes of the railway disaster prevention monitoring system are all single-antenna, that is, each node can only process the transmission (receive/transmit) of a single information at the same time point. In the linear sensor network, each node plays the roles of information sender, processor and receiver at the same time. In order to avoid data transmission channel conflict, the channel is divided into multiple time slots, each of which is responsible for different types of data processing tasks. The transmission delay of data at node i is defined as:

L(i)=Lr(i)+Lw(i)+Lt(i)

Among them, Lr(i) is the data receiving delay, Lw(i) is the data transmission waiting delay, which is determined by the data processing time and transmission priority, and Lt(i) is the data sending time;

The transmission delay of node data from source to sink is defined as:

Among them, H is the total number of hops; since the data transmission and reception between nodes are continuous, the sending and receiving time of adjacent nodes can be combined;

Step 5: Establish a node energy minimization model,

F ₂ (i)=minE(i), i=1,2,...,N

The energy consumption of each node is divided into three stages: data reception, data processing and data transmission. The total energy consumption of a single node is defined as:

E(i)=E _r (i)×f _r (i)+E _d (i)×f _d (i)+E _t (i)×f _t (i)

where f _r (i) is the data receiving frequency of node i, which is determined by the pre-sequence nodes that have a direct data transmission relationship with the node; f _d (i) is the data processing frequency of node i, which is determined by the data sensing frequency. All data needs to be preprocessed in real time; f _t (i) is the data sending frequency of node i, which is determined by the data sending mode of the node.

Step 6: Establish an energy consumption equalization model between nodes,

Step 7: Establish a multi-node collaborative interoperability relationship model, and determine the optimization goals for different types of data:

The optimization goals of different types of nodes are different, namely: A (energy consumption equalization F3), B (delay minimization F1), C (delay minimization F1), D (delay minimization F1, lowest energy consumption) F2), E (energy consumption equalization F3);

Step 8: Establish a multi-objective optimization model,

Among them, F ₁ ^* (i), F ₂ ^* (i), F ₃ ^* (i) are the normalized forms of F ₁ (i), F ₂ (i), F ₃ (i), respectively, λ _i , γ _i , η _i are 0-1 variables, used for different types of node selection:

Step 9: Solve the multi-objective optimization model, obtain the optimal multi-hop link for data transmission of each node of the railway disaster prevention monitoring system, and design the transmission protocol, and the system performs data transmission according to the transmission protocol.

In this embodiment, a method for hierarchically returning information of a railway disaster prevention monitoring system is designed. According to the real-time risk assessment requirements of the railway disaster prevention monitoring system and the limited energy and bandwidth resources of the linear wireless transmission system, this method designs a state-driven and variable period information grading backhaul method. First, make full use of the computing power of disaster prevention monitoring nodes to perform edge-side preprocessing and feature classification on monitoring data, and adopt a state-driven information return strategy for safety-related abnormal information, which ensures the real-time nature of safety-related information return; secondly , For the data information in the normal state, according to the state of the disaster prevention monitoring node, the variable period information return strategy is adopted to ensure the periodic integrity of the regular information return. Combining the advantages of state-driven and variable-period information return strategies, on the premise of ensuring real-time transmission of security-related information, it effectively reduces the waste of energy and bandwidth resources caused by redundant information return, and improves the life cycle of the monitoring system. service capacity.

Those of ordinary skill in the art can understand that all or part of the steps/units/modules of the above-mentioned embodiments can be implemented through program instructions related to hardware, and the aforementioned programs can be stored in a computer-readable storage medium, and when the program is executed, Execution includes the steps corresponding to the units in the foregoing embodiments; and the aforementioned storage medium includes: ROM, RAM, magnetic disk, or optical disk and other media that can store program codes.

The specific embodiments described above further describe the purpose, technical solutions and beneficial effects of the present invention in further detail. It should be understood that the above descriptions are only specific embodiments of the present invention, and are not intended to limit the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims (3)

1. A railway disaster prevention monitoring system information grading return method is characterized by specifically comprising the following steps:

step S101: allocating corresponding labels [1,2,3, …, N ] to each monitoring node in the railway disaster prevention monitoring system, and initializing the state of each node; the initial states of all the nodes in the first round are normal; from the second round, classifying and labeling the nodes according to the node states and the data states;

step S102: acquiring disaster-causing element data of a monitoring area in real time by using the sensing capability of each monitoring node of the railway disaster prevention monitoring system;

step S103: performing edge end preprocessing on the acquired disaster-causing element data by utilizing the computing power of each monitoring node of the railway disaster prevention monitoring system to realize the feature extraction of data analysis;

step S104: respectively judging whether the state of each node is normal or not according to the node state identification result of the step S101; if the node identification state is normal, the step S105 is entered; if the node identification state is abnormal, the step S109 is entered;

step S105: for the node with the normal node identification state, judging whether the node monitoring data is normal or not according to the data characteristics extracted in the step S103 and comparing with the disaster-causing element risk level preset by the system; if the monitoring data is normal, namely the risk level of the monitoring data is 0, entering the step S106; if the monitoring data is abnormal, namely the risk level of the monitoring data is not 0, the step S107 is entered;

step S106: monitoring data with a risk level of 0, and enabling the corresponding node to enter a preset conventional periodic transmission mode, namely according to a conventional sampling frequency preset by a systemfData returning is carried out;

step S107: if the risk level of the monitoring data is not 0, marking the corresponding node as an isolation node, and setting the isolation period to be T = 0; then, the process goes to step S108; the isolation period is a time period for transmitting data by adopting an isolation period transmission mode when the node identification state is abnormal but the monitoring data is normal;

step S108: for the isolated nodes with the monitored data risk level not being 0, a preset abnormal data real-time transmission mode is adopted, namely the monitored data is transmitted in a mode with minimum time delay under the condition of allowing energy and bandwidth resources;

step S109: for a node with an abnormal node identification state, judging whether the node monitoring data is normal or not according to the data characteristics extracted in the step S103 and comparing with the disaster-causing element risk level preset by the system;

if the monitoring data is abnormal, namely the risk level of the monitoring data is not 0, the step S107 and the step S108 are carried out for data transmission; if the monitoring data is normal, namely the risk level of the monitoring data is 0, the step S110 is entered;

step S110: the isolated period is changed to T = T +1, and then the process proceeds to step S111;

step S111: judging whether the isolation node is at the expiration or not, namely whether T reaches Tmax or not, wherein the maximum value Tmax of the isolation period is preset by a system and is a constant; if the quarantine duration is not reached, the process proceeds to step S112, and if the quarantine duration is reached, the process proceeds to step S113;

step S112: without reaching the quarantine period, i.e. T<= Tmax, the corresponding node enters the isolated period transmission mode, i.e. the node is at a sampling frequency higher than the preset normal sampling frequencyfHigher sampling frequencyf ^/ Carrying out data transmission;

step S113: reach the quarantine phase, i.e. T>Tmax, the corresponding node enters a preset conventional periodic transmission mode, namely, according to the conventional sampling frequency preset by the systemfData returning is carried out;

step S114: after all nodes determine the transmission mode, the system establishes a multi-node cooperative transmission multi-target optimization model according to the transmission mode and the transmission requirement of each node, and optimizes the transmission route of each node;

step S115: and transmitting the monitoring data according to the optimized communication protocol, and entering the step S101 after the transmission is finished.

2. The method for hierarchical returning of information of railway disaster prevention monitoring system according to claim 1,

in step S114, the specific method for establishing the multi-node cooperative transmission multi-objective optimization model includes:

(1) establishing a node time delay minimization model:

in the formula,F ₁ (i)representing a node time delay minimization optimization objective function;L(i)representing the transmission delay of data at node i; i represents the ith node, and N is the total number of nodes in the railway disaster prevention monitoring system;

(2) establishing a node energy consumption minimization model:

F ₂ (i)representing a node energy consumption minimization optimization objective function;

the energy consumption of each node is divided into three stages of data receiving, data processing and data sending, and the total energy consumption of each node isE(i)Is defined as:

in the formula，

Is a node

The data reception frequency of (1);

is a node

The data processing frequency of (1);

is a node

The data transmission frequency of (1);E _r (i)represents the unit data reception power consumption of the node i,E _d (i)represents the unit data processing energy consumption of the node i,E _t (i)Representing the unit data transmission energy consumption of the node i;

(3) establishing an energy consumption equalization model between nodes:

F ₃ (i)representing an energy consumption equalization optimization objective function among nodes;

represents the average value of the energy consumption of all nodes;

(4) establishing a multi-node cooperative transmission multi-objective optimization model:

wherein,

，

，

are respectively as

，

，

Normalizing the normalized form after the normalization processing;

is a variable from 0 to 1 and is used for selecting different types of nodes;

。

3. the method for hierarchical returning of information of railway disaster prevention monitoring system according to claim 1,

in step S101:

classifying the nodes according to the node states and the data states, wherein the classified nodes comprise:

a type node: the node is normal and the data is normal;

and B type node: the node is normal and the data is abnormal;

c type node: node exception, data exception;

d type node: node exception, data normal, node isolation period;

e type node: the node is abnormal, the data is normal, and the node isolation is expired;

marking the data transceiving capacity of the node according to the node classification, wherein the marking type comprises the following steps:

for data of class a nodes: low-frequency receiving and transmitting;

for class B node data: sending in real time;

for data of class C nodes: sending in real time;

for data of class D nodes: high-frequency transmission;

for class E node data: and (5) low-frequency transceiving.

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