CN113242274A - Information grading return method for railway disaster prevention monitoring system - Google Patents
Information grading return method for railway disaster prevention monitoring system Download PDFInfo
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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
Technical Field
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.
Background
With the great increase of the running speed and the rapid increase of the operating mileage, railways become one of the main modes of travelers and rapid freight transportation in China, and are important pillars for the development of national economy and society in China. However, the railway line coverage in China is wide, and the operation environment is complex and changeable, which brings great challenges to the safety and reliability of the operation of the railway system. The technologies of real-time acquisition, transmission, calculation and the like of disaster-causing element information play more and more important roles in railway disaster prevention and control, and abundant big data support and effective technical support are provided for risk assessment, early warning and control of a railway operation system. At present, a disaster prevention monitoring system along a railway mostly adopts a wired communication mode or a mobile communication mode such as 4G and the like to transmit monitoring information. However, in a region with a complex terrain environment, the deployment difficulty of power and network resources along a railway is high, the cost is high, the mobile network signal quality is poor, and the effective transmission of disaster prevention monitoring information is difficult to guarantee. With the development of wireless transmission technology, a local transmission system based on wireless communication is constructed in a region with a complex terrain environment to effectively solve the problem, monitoring information is transmitted, collected and forwarded in the local wireless transmission system, and then the information is sent to a data center for analysis and processing through a sink node or a base station in an open area.
At present, disaster prevention monitoring data along a railway mostly adopt fixed sampling frequency to sense and transmit the data, so that the integrity of monitoring data return can be kept to the maximum extent, and the sampling frequency of the transmission mode has a decisive effect on the monitoring effect. On the one hand, the large sampling frequency can increase the transmission quantity of redundant data and the corresponding energy resource waste; on the other hand, the small sampling frequency can cause the real-time performance of certain safety-related data transmission to be reduced, even the phenomenon of missing transmission occurs, and potential safety hazards are caused.
Therefore, one technical problem that needs to be solved by those skilled in the art is: how to utilize the edge computing power of the railway disaster prevention monitoring system to preprocess and identify the monitoring data state, thereby carrying out grading return on the data according to the information security level.
Disclosure of Invention
Aiming at the technical problems, the invention provides a railway disaster prevention monitoring system information grading return method, which fully utilizes the edge computing capability of railway disaster prevention monitoring nodes, adopts a grading return method for data in different safety level states, and keeps the return real-time performance of safety related information to the maximum extent and maximizes the whole life cycle of a monitoring system.
The invention is realized by the following technical scheme:
a railway disaster prevention monitoring system information grading return method specifically comprises 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; wherein the disaster-causing elements comprise wind, rain, snow, earthquake and foreign invasion;
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 the data risk level to be 0, and enabling the corresponding node to enter a preset conventional periodic transmission mode, namely returning data according to a conventional sampling frequency f preset by a system;
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;
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 into the isolated period transmission mode, i.e. the node has a higher sampling frequency f than the preset regular sampling frequency f/Carrying out data transmission;
step S113: when the isolation period is reached, namely T is greater than Tmax, the corresponding node enters a preset conventional periodic transmission mode, namely data are transmitted back according to a conventional sampling frequency f preset by a system;
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.
Further, in step S114, the establishing a multi-node cooperative transmission multi-objective optimization model specifically includes:
(1) establishing a node time delay minimization model:
F1(i)＝minL(i),i＝1,2,…,N
in the formula, F1(i) Representing a node time 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) establishing a node energy consumption minimization model:
F2(i)＝minE(i),i＝1,2,…,N
F2(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 E (i) of a single node is defined as follows:
E(i)＝Er(i)×fr(i)+Ed(i)×fd(i)+Et(i)×ft(i)
in the formula (f)r(i) A data reception frequency for node i; f. ofd(i) The data processing frequency for node i; f. oft(i) A data transmission frequency for node i; er(i) Representing energy consumption per data reception of node i, Ed(i) Represents the energy consumption of the unit data processing of the node i, Et(i) Representing the unit data transmission energy consumption of the node i;
(3) establishing an energy consumption equalization model between nodes:
F3(i) representing an energy consumption equalization optimization objective function among nodes;
representing the average of the energy consumption of all nodes；
(4) Establishing a multi-node cooperative transmission multi-objective optimization model:
wherein, F1 *(i)，F2 *(i)，F3 *(i) Are respectively F1(i)，F2(i)，F3(i) Normalizing the normalized form after the normalization processing;
λi，γi，ηiis a variable from 0 to 1 and is used for selecting different types of nodes;
further, 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.
The invention has the beneficial technical effects that:
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 requirement of the railway disaster prevention monitoring system and the limited characteristics of energy and bandwidth resources of the linear wireless transmission system. Firstly, 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 secondly, 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. Moreover, for the nodes which generate abnormal data recently, the data return frequency is increased within a period of time, and the nodes are used for disaster-causing element situation identification, prediction and prevention and control; and finally, for the nodes in the normal state, a lower data return frequency is adopted, so that unnecessary energy consumption is reduced to the maximum extent, and the stability of monitoring of the disaster prevention state is effectively guaranteed.
Drawings
Fig. 1 is a flowchart of a method for hierarchical returning of information of a railway disaster prevention monitoring system according to an embodiment of the present invention;
fig. 2 is a general structural diagram of an information grading and returning system of a railway disaster prevention monitoring system according to an embodiment of the present invention;
fig. 3 is a flowchart of an implementation of the information classification returning method for the railway disaster prevention monitoring system according to the embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
On the contrary, the invention is intended to cover alternatives, modifications, equivalents and alternatives which may be included within the spirit and scope of the invention as defined by the appended claims. Furthermore, in the following detailed description of the present invention, certain specific details are set forth in order to provide a better understanding of the present invention. It will be apparent to one skilled in the art that the present invention may be practiced without these specific details.
Referring to fig. 1, a flow chart of a method for hierarchically returning information of a railway disaster prevention monitoring system is shown, where the method specifically includes:
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 state of the first round is 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 the data risk level to be 0, and enabling the corresponding node to enter a preset conventional periodic transmission mode, namely returning data according to a conventional sampling frequency f preset by a system;
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;
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 into the isolated period transmission mode, i.e. the node has a higher sampling frequency f than the preset regular sampling frequency f/Data transmission is carried out to ensure the integrity of data transmission of the node during the isolation period, and the evaluation and prediction of the disaster-causing element state of the area are facilitated;
step S113: when the isolation period is reached, namely T is greater than Tmax, the corresponding node enters a preset conventional periodic transmission mode, namely data are transmitted back according to a conventional sampling frequency f preset by a system;
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;
specifically, a transmission mode with highest priority and minimum time delay is carried out on data entering an abnormal data real-time transmission mode node; the data entering the node of the transmission mode in the isolation period is transmitted in a high-frequency mode, so that the integrity of data monitoring is ensured; and a low-frequency transmission mode is adopted for data entering a node in a conventional transmission mode, so that the high-efficiency utilization of energy and bandwidth resources is ensured.
Step S115: and transmitting the monitoring data according to the optimized communication protocol, and entering the step S101 after the transmission is finished.
In this embodiment, in step S114, the establishing a multi-node cooperative transmission multi-objective optimization model specifically includes:
generally, the wireless sensor nodes of the railway disaster prevention monitoring system are all single-antenna, that is, each node can only process transmission (receiving/transmitting) of single information at the same time point. In the linear sensor network, each node simultaneously plays 3 roles of an information sender, a processor and a receiver, in order to avoid data transmission channel collision, a channel is divided into a plurality of time slots, and each time slot 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)
wherein, lr (i) is a data receiving time delay, lw (i) is a data transmission waiting time delay, which is determined by the data processing time and the transmission priority, and lt (i) is a data sending time;
the transmission delay of the node data from the source point to the sink node is defined as:
wherein H is the total hop count; because the data transmission and the data reception between the nodes are continuous, the receiving and transmitting time of the adjacent nodes can be combined;
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 a single node is defined as follows:
E(i)＝Er(i)×fr(i)+Ed(i)×fd(i)+Et(i)×ft(i)
wherein f isr(i) The data receiving frequency of the node i is determined by a preorder node which has a direct data transmission relation with the node; f. ofd(i) The data processing frequency of the node i is determined by the data sensing frequency, and the sensed data needs to be preprocessed in real time; f. oft(i) The data transmission frequency of the node i is determined by the data transmission mode of the node.
Firstly, 3 single-target models are established, namely a time delay minimization model, an energy consumption minimization model and an energy consumption equalization model. The node time delay minimization model is as follows:
F1(i)＝minL(i),i＝1,2,…,N
the energy consumption minimization model is as follows:
F2(i)＝minE(i),i＝1,2,…,N
the energy consumption equalization model is as follows:
the multi-node cooperative transmission multi-target optimization model comprises the following steps:
wherein, F1 *(i)，F2 *(i)，F3 *(i) Are respectively F1(i)，F2(i)，F3(i) Normalized form of (a) ("lambda")i，γi，ηiIs a variable from 0 to 1, and is used for different types of node selection:
the model can effectively ensure that the transmission delay of abnormal nodes (both the node identification state and the node data are abnormal) is minimized, and the real-time property of information transmission is ensured; the transmission delay and energy consumption of isolated nodes (node identification state is abnormal, node data is normal) are minimized at the same time, and the transmission real-time performance and the monitoring continuity are guaranteed; and (4) balancing the energy consumption of normal nodes (the node identification state and the node data are normal) so as to improve the life cycle of the system.
Referring to fig. 2, there is shown an entity diagram of the overall structure of the present invention, the general idea is:
the railway disaster prevention monitoring sensor mainly comprises a wind speed sensor, a rainfall sensor, a snow sensor, an earthquake sensor and a foreign matter sensor 5, each sensor comprises a sensing unit, a calculating unit, a communication unit and an energy unit 4, and the energy consumption of each unit is supplied by the energy unit.
Before data is sent, the 5-type sensor firstly judges the identification states of the nodes, namely a normal state and an isolation state; secondly, analyzing the data of the nodes in the isolation state, and judging whether abnormal data appear; thirdly, selecting a transmission mode according to the node identification state and the data state, wherein a normal node adopts a periodic transmission mode with low sampling frequency, a node for isolating normal data of the node adopts a periodic transmission mode with high sampling frequency, and a node for isolating abnormal data of the node adopts a real-time transmission mode; fourthly, all the nodes participate in the construction and optimization of the multi-node cooperative transmission multi-target optimization model together; fifthly, a communication protocol is designed according to the optimized result, and the system transmits data according to the communication protocol.
Referring to fig. 3, a flowchart of a method implementation of the embodiment of the present invention is shown, and the specific steps are:
step1, 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;
step2, marking the data transceiving capacity of the nodes according to the node classification, wherein the marking types comprise:
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: low-frequency transceiving;
step3, initializing a multi-hop link matrix between nodes according to the data transceiving capacity of the nodes, wherein the transmission link between the nodes is fi,jRepresents:
step4, establishing a node time delay minimization model,
F1(i)＝minL(i),i＝1,2,…,N
generally, the wireless sensor nodes of the railway disaster prevention monitoring system are all single-antenna, that is, each node can only process transmission (receiving/transmitting) of single information at the same time point. In the linear sensor network, each node simultaneously plays 3 roles of an information sender, a processor and a receiver, in order to avoid data transmission channel collision, a channel is divided into a plurality of time slots, and each time slot 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)
wherein, lr (i) is a data receiving time delay, lw (i) is a data transmission waiting time delay, which is determined by the data processing time and the transmission priority, and lt (i) is a data sending time;
the transmission delay of the node data from the source point to the sink node is defined as:
wherein H is the total hop count; because the data transmission and the data reception between the nodes are continuous, the receiving and transmitting time of the adjacent nodes can be combined;
step5, establishing a node energy consumption minimization model,
F2(i)＝minE(i),i＝1,2,…,N
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 a single node is defined as follows:
E(i)＝Er(i)×fr(i)+Ed(i)×fd(i)+Et(i)×ft(i)
wherein f isr(i) The data receiving frequency of the node i is determined by a preorder node which has a direct data transmission relation with the node; f. ofd(i) The data processing frequency of the node i is determined by the data sensing frequency, and the sensed data needs to be preprocessed in real time; f. oft(i) The data transmission frequency of the node i is determined by the data transmission mode of the node.
Step6, establishing an energy consumption equalization model among nodes,
step7, establishing a multi-node cooperative interoperation relationship model, and determining optimization targets of different types of data:
the optimization targets of different types of nodes are different, and are respectively as follows: a (energy consumption equalization F3), B (delay minimization F1), C (delay minimization F1), D (delay minimization F1, energy consumption minimization F2) and E (energy consumption equalization F3);
step8, establishing a multi-objective optimization model,
wherein, F1 *(i)，F2 *(i)，F3 *(i) Are respectively F1(i)，F2(i)，F3(i) Normalized form of (a) ("lambda")i，γi，ηiIs a variable from 0 to 1, and is used for different types of node selection:
and Step9, solving the multi-target optimization model to obtain an optimal multi-hop link for data transmission of each node of the railway disaster prevention monitoring system, and designing a transmission protocol, wherein the system transmits data according to the transmission protocol.
The embodiment designs an information grading return method of a railway disaster prevention monitoring system. The method designs an information grading feedback method combining state driving and variable periods according to the risk real-time evaluation requirement of a railway disaster prevention monitoring system and the energy and bandwidth resource limitation characteristics of a linear wireless transmission system. Firstly, 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 secondly, 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.
It will be understood by those skilled in the art that all or part of the steps/units/modules for implementing the embodiments may be implemented by hardware associated with program instructions, and the program may be stored in a computer-readable storage medium, and when executed, the program performs the steps corresponding to the units in the embodiments; and the aforementioned storage medium includes: various media that can store program codes, such as ROM, RAM, magnetic or optical disks.
The above-mentioned embodiments are intended to illustrate the objects, technical solutions and advantages of the present invention in further detail, and it should be understood that the above-mentioned embodiments are only exemplary embodiments of the present invention, and are not intended to limit the present invention, and any modifications, equivalents, improvements and the like made within the spirit and principle of the present invention should be included in 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 the data risk level to be 0, and enabling the corresponding node to enter a preset conventional periodic transmission mode, namely returning data according to a conventional sampling frequency f preset by a system;
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;
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 into the isolated period transmission mode, i.e. the node has a higher sampling frequency f than the preset regular sampling frequency f/Carrying out data transmission;
step S113: when the isolation period is reached, namely T is greater than Tmax, the corresponding node enters a preset conventional periodic transmission mode, namely data are transmitted back according to a conventional sampling frequency f preset by a system;
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:
F1(i)＝min L(i),i＝1,2,…,N
in the formula, F1(i) Representing a node time 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) establishing a node energy consumption minimization model:
F2(i)＝min E(i),i＝1,2,…,N
F2(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 E (i) of a single node is defined as follows:
E(i)＝Er(i)×fr(i)+Ed(i)×fd(i)+Et(i)×ft(i)
in the formula (f)r(i) A data reception frequency for node i; f. ofd(i) The data processing frequency for node i; f. oft(i) A data transmission frequency for node i; er(i) Representing energy consumption per data reception of node i, Ed(i) Represents the energy consumption of the unit data processing of the node i, Et(i) Representing the unit data transmission energy consumption of the node i;
(3) establishing an energy consumption equalization model between nodes:
F3(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, F1 *(i)，F2 *(i)，F3 *(i) Are respectively F1(i)，F2(i)，F3(i) Normalizing the normalized form after the normalization processing;
λi，γi，ηiis a variable from 0 to 1 and is used for selecting different types of nodes;
3. the method for hierarchical returning of information of a railway disaster prevention monitoring system according to claim 1, wherein 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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