阅读说明：本技术 铁路防灾监测无线传输系统路由及数据压缩自适应优化方法 (Railway disaster prevention monitoring wireless transmission system routing and data compression self-adaptive optimization method ) 是由 贾利民 马小平 王朝静 秦勇 赵静 于 2021-09-10 设计创作，主要内容包括：本发明涉及铁路防灾监测无线通信技术领域,特别是涉及一种铁路防灾监测无线传输系统路由及数据压缩自适应优化方法。该方法根据铁路防灾监测线性无线传输系统节点能量受限及数据通信需求差异化等特性,设计一种通信路由及数据压缩率自适应优化方法,充分发挥传感器节点的边缘计算能力对监测数据特征进行预判,并根据数据特征进行路由及数据压缩率的自适应优化设计,在多监测节点、复杂监测环境情况下保证了异常信息传输的实时性和正常信息传输的稳定性,确保为铁路系统的安全运行及决策提供丰富精准致灾要素监测数据和防灾应急决策支持。(The invention relates to the technical field of railway disaster prevention monitoring wireless communication, in particular to a railway disaster prevention monitoring wireless transmission system routing and data compression self-adaptive optimization method. According to the method, a communication route and data compression ratio self-adaptive optimization method is designed according to the characteristics of limited node energy, differentiated data communication requirements and the like of a railway disaster prevention monitoring linear wireless transmission system, edge computing capacity of sensor nodes is fully exerted to pre-judge monitoring data characteristics, and self-adaptive optimization design of the route and the data compression ratio is carried out according to the data characteristics, so that the instantaneity of abnormal information transmission and the stability of normal information transmission are ensured under the condition of multiple monitoring nodes and complex monitoring environments, and abundant and accurate disaster-causing element monitoring data and disaster prevention emergency decision support are ensured to be provided for safe operation and decision of a railway system.)

1. A railway disaster prevention monitoring wireless transmission system routing and data compression self-adaptive optimization method is characterized by specifically comprising the following steps:

step S101: initializing a wireless routing matrix F and a data compression rate matrix R of a node according to the architecture of the railway disaster prevention monitoring wireless transmission system;

step S102: acquiring data acquired by each disaster prevention monitoring sensor node at present, and performing edge end preprocessing and characteristic analysis on the data;

step S103: preliminarily judging whether the disaster prevention monitoring data content has abnormal conditions or not; if the abnormal condition exists, the step S104 is executed; if no abnormal condition exists, the step S105 is executed;

step S104: when an abnormal condition exists, entering an abnormal data real-time priority transmission mode: the abnormal node data adopts a mode of low compression and less sub-packaging so as to ensure the real-time property of abnormal data transmission; the conventional node data is compressed by adopting self-adaptive sub-packets;

step S105: and when no abnormal condition exists, all the conventional nodes start the conventional data stable transmission mode and adopt the self-adaptive packet compression.

2. The railway disaster prevention monitoring wireless transmission system routing and data compression adaptive optimization method according to claim 1, wherein in step S101:

the wireless routing matrix F is:

n represents the number of sensor nodes in the railway disaster prevention monitoring wireless transmission system, B represents a sink node of disaster prevention monitoring data or represents a base station node along the railway; f. of_i,jRepresents the amount of data transmitted from base station node i to base station node j, i being 0,1,2, …, N, j being 1,2, …, B; assuming that data is transmitted to a base station in a one-way mode, wherein the wireless routing matrix F is an upper triangular matrix;

the data compression matrix R is:

wherein r is_i,jThe data compression rate adopted on a data link transmitted from a base station node i to a base station node j is represented, data is supposed to be transmitted in a single direction towards the base station, and the data compression matrix R is an upper triangular matrix; wherein the compression ratio r is defined as the amount d of data after compression_comAnd the amount of data d before compression_oriThe ratio of (a) to (b), namely:

3. the adaptive optimization method for route and data compression of wireless transmission system for railway disaster prevention and monitoring as claimed in claim 1, wherein in step S103, said abnormal condition comprises storm/rain/snow, earthquake or foreign body invasion.

4. The method for adaptive optimization of routing and data compression of a railway disaster prevention monitoring wireless transmission system according to claim 2, wherein step S104 specifically comprises:

s104-1, determining the data transmission mode of the abnormal node:

(1) calculating the energy consumption and time delay of the non-sub-package non-compression multi-hop routing transmission of abnormal node monitoring data;

(2) calculating a multi-hop transmission mode with the lowest delay grade;

(3) selecting a transmission mode with the same delay grade and the most balanced energy consumption;

(4) calculating whether the energy consumption of the transmission mode selected in the step (3) is greater than the residual energy, and if so, outputting an optimal route for abnormal data transmission; if not, calculating a transmission mode with a higher delay level, and then entering the step (3) until an optimal route for transmitting abnormal data is output;

s104-2, the conventional node adopts a self-adaptive packet compression mode for transmission;

the optimization objective function is:

wherein, varE_re(t +1) is the variance of the residual energy at the moment t +1 of all nodes;for node i to have energy remaining at time t +1,the average value of the residual energy of all nodes at the moment t +1 is obtained;

wherein, the node i has residual energy at the moment of t +1For node i remaining energy at time tAnd consuming energyThe difference of (a) is:

5. the railway disaster prevention monitoring wireless transmission system routing and data compression adaptive optimization method according to claim 4, wherein in step S104-1:

in the step (1), the time delay T of monitoring data non-sub-packaging and non-compression multi-hop routing transmission by the abnormal node is generated in three processes of data compression, transmission and decompression, and the calculation method of the time delay T is as follows:

T＝T_c+T_t+T_d

wherein, T_cCompressing the time delay for the data; t is_tIs the data transmission delay; t is_dDecompressing the data for a delay;

in the step (1), the energy consumption of the abnormal node monitoring data non-sub-package non-compression multi-hop routing transmission is composed of three parts of data sending, compression and receiving, and the node energy consumption calculation method comprises the following steps:

E_T＝E_s+E_c+E_r

wherein E is_sEnergy consumption for data transmission; e_cEnergy consumption for data compression; e_rEnergy consumption for data reception;

wherein the transmission energy consumption of the i nodeComprises the following steps:

wherein E is_eleThe energy consumption parameter of the electronic device is constant; xi_fsIs a free space energy dissipation coefficient, which is a constant; xi_mpThe coefficient is a multipath fading energy dissipation coefficient and is a constant; d₀Is the critical distance between nodes; d_i,jIs the distance between node i and node j;

compression energy consumption of i-nodeComprises the following steps:

wherein E is_Com(r_i,j) Indicates when the data compression rate is r_i,jTime data compression energy consumption coefficient;

receiving energy consumption of i nodeComprises the following steps:

6. the method for adaptive optimization of routing and data compression of a railway disaster prevention monitoring wireless transmission system according to claim 1, wherein step S105 specifically comprises: when no abnormal condition exists, all the conventional nodes start a conventional data stable transmission mode and adopt self-adaptive packet compression;

the optimization objective function is:

wherein, varE_re(t +1) is the variance of the residual energy of all nodes at the time t + 1;for node i to have energy remaining at time t +1,the average value of the residual energy of all nodes at the moment t +1 is obtained;

wherein, the node i has residual energy at the moment of t +1For node i remaining energy at time tAnd consuming energyThe difference of (a) is:

Technical Field

The invention relates to the technical field of railway disaster prevention monitoring wireless communication, in particular to a railway disaster prevention monitoring wireless transmission system routing and data compression self-adaptive optimization 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.

However, energy resource limitation is the biggest challenge facing local wireless transmission systems, and energy consumption efficiency improvement based on communication routing protocol optimization is one of the most effective measures for solving the problem. Local wireless transmission systems of the railway disaster prevention monitoring system are linearly distributed, namely a method for optimally utilizing energy resources of the transmission system by the linear sensor network system at present mainly comprises a multi-hop-based load balancing optimization algorithm, and the balance of communication loads and the balance of energy consumption among nodes are kept in a multi-hop packet forwarding mode, so that the life cycle of the system is prolonged. On one hand, the algorithm does not sufficiently consider the imbalance of the communication requirements of the disaster prevention monitoring data in the system, namely, under the condition of abnormal conditions, part of the disaster prevention monitoring data needs to be transmitted preferentially so as to ensure the safety service capability of the monitoring system; on the other hand, the edge computing capability of the nodes is not considered enough, namely, the disaster prevention monitoring data can fully utilize the edge computing capability of the nodes and carry out preliminary compression on the data so as to reduce the data transmission quantity and the energy consumption in the transmission process and further maximize the life cycle of the system.

Therefore, one technical problem that needs to be solved by those skilled in the art is: on one hand, the method is used for identifying abnormal data and designing a corresponding data transmission mode by utilizing the edge computing capability of the railway disaster prevention monitoring system; on the other hand, a data compression algorithm is adopted in a self-adaptive mode, transmission energy consumption is reduced, and therefore the reliable service capability of the system is guaranteed to the maximum extent while the safe service capability of the system is guaranteed.

Disclosure of Invention

Aiming at the technical problems, the invention provides a railway disaster prevention monitoring wireless transmission system routing and data compression self-adaptive optimization method, which makes full use of the edge computing capability of railway disaster prevention monitoring nodes, and carries out differentiated communication protocol design on different monitoring data so as to greatly improve the life cycle of a monitoring system on the basis of ensuring the real-time preferential transmission of safety monitoring information.

The invention is realized by the following technical scheme:

a railway disaster prevention monitoring wireless transmission system routing and data compression self-adaptive optimization method specifically comprises the following steps:

step S101: initializing a wireless routing matrix F and a data compression rate matrix R of a node according to the architecture of the railway disaster prevention monitoring wireless transmission system;

step S102: acquiring data acquired by each disaster prevention monitoring sensor node at present, and performing edge end preprocessing and characteristic analysis on the data;

step S103: preliminarily judging whether the disaster prevention monitoring data content has abnormal conditions or not; if the abnormal condition exists, the step S104 is executed; if no abnormal condition exists, the step S105 is executed;

step S104: when an abnormal condition exists, entering an abnormal data real-time priority transmission mode: the abnormal node data adopts a mode of low compression and less sub-packaging so as to ensure the real-time property of abnormal data transmission; the conventional node data is compressed by adopting self-adaptive sub-packets, so that the energy consumption imbalance among nodes caused by the prior transmission of abnormal data is effectively inhibited, and the high-safety service capability of the system is ensured;

step S105: when no abnormal condition exists, all the conventional nodes start the conventional data stable transmission mode, and all the conventional nodes adopt self-adaptive packet compression, so that the overall life cycle of the system is effectively prolonged, and the high-reliability service capability of the monitoring system is ensured.

Further, in step S101:

the wireless routing matrix F is:

n represents the number of sensor nodes in the railway disaster prevention monitoring wireless transmission system, B represents a sink node of disaster prevention monitoring data or represents a base station node along the railway; f. of_i,jRepresents the amount of data transmitted from base station node i to base station node j, i being 0,1,2, …, N, j being 1,2, …, B; assuming that data is transmitted to a base station in a one-way mode, wherein the wireless routing matrix F is an upper triangular matrix;

the data compression matrix R is:

wherein r is_i,jThe data compression rate adopted on a data link transmitted from a base station node i to a base station node j is represented, data is supposed to be transmitted in a single direction towards the base station, and the data compression matrix R is an upper triangular matrix; wherein the compression ratio r is defined as the amount d of data after compression_comAnd the amount of data d before compression_oriThe ratio of (a) to (b), namely:

further, in step S103, the abnormal condition includes storm/rain/snow, earthquake or foreign body invasion.

Further, step S104 specifically includes:

s104-1, determining the data transmission mode of the abnormal node:

(1) calculating the energy consumption and time delay of the non-sub-package non-compression multi-hop routing transmission of abnormal node monitoring data;

(2) calculating a multi-hop transmission mode with the lowest delay grade;

(3) selecting a transmission mode with the same delay grade and the most balanced energy consumption;

(4) calculating whether the energy consumption of the transmission mode selected in the step (3) is greater than the residual energy, and if so, outputting an optimal route for abnormal data transmission; if not, calculating a transmission mode with a higher delay level, and then entering the step (3) until an optimal route for transmitting abnormal data is output;

step S104-1 can ensure that under the condition that the communication capability (the communication capability comprises a single-hop transmission distance and the relation between the residual energy and the transmission energy consumption) allows, the data processing and transmission time delay is reduced to the maximum extent; when the communication capability is not allowed, the total transmission energy consumption is reduced to the maximum extent under the condition of the same time delay.

S104-2, the conventional node adopts a self-adaptive packet compression mode for transmission; the method aims to inhibit the energy consumption imbalance among nodes caused by abnormal node data transmission;

the optimization objective function is:

wherein, varE_re(t +1) is the variance of the residual energy at the moment t +1 of all nodes;for node i to have energy remaining at time t +1,the average value of the residual energy of all nodes at the moment t +1 is obtained;

wherein, the node i has residual energy at the moment of t +1For node i remaining energy at time tAnd consuming energyThe difference of (a) is:

further, in step S104-1:

in the step (1), the time delay T of monitoring data non-sub-packaging and non-compression multi-hop routing transmission by the abnormal node is generated in three processes of data compression, transmission and decompression, and the calculation method of the time delay T is as follows:

T＝T_c+T_t+T_d

wherein, T_cCompressing the time delay for the data; t is_tIs the data transmission delay; t is_dDecompressing the data for a delay;

data compression delay T_cThe compression algorithm and the compression ratio are jointly determined, and when the same compression algorithm is adopted, the lower the compression ratio is, the larger the corresponding compression time delay is; data transmission delay T_tThe number of data packets and the number of hops are jointly determined, and the higher the number of packets and the number of hops is, the larger the corresponding transmission delay is; data decompression delay T_dThe decompression algorithm and the compression ratio are jointly determined, and when the same decompression algorithm is adopted, the lower the compression ratio is, the larger the corresponding decompression time delay is;

in the step (1), the energy consumption of the abnormal node monitoring data non-sub-package non-compression multi-hop routing transmission is composed of three parts of data transmission, compression and reception, and the node energy consumption calculation method is as follows (because the data is decompressed uniformly at the base station, the patent assumes that the energy of the base station is not constrained, and therefore the decompression energy consumption is not considered):

E_T＝E_s+E_c+E_r

wherein E is_sEnergy consumption for data transmission; e_cEnergy consumption for data compression; e_rEnergy consumption for data reception; energy consumption for data transmission E_sThe larger the data volume is, the longer the distance is, the larger the corresponding transmission energy consumption is; energy consumption for data compression E_cThe compression algorithm, the compression ratio and the compressed data volume are jointly determined, and when the same compression algorithm is adopted, the lower the compression ratio is, the larger the data volume is, and the larger the corresponding compression energy consumption is; energy consumption for data reception E_rThe larger the received data amount is, the larger the corresponding receiving energy consumption is.

Wherein the transmission energy consumption of the i nodeComprises the following steps:

wherein E is_eleThe energy consumption parameter of the electronic device is constant; xi_fsIs a free space energy dissipation coefficient, which is a constant; xi_mpThe coefficient is a multipath fading energy dissipation coefficient and is a constant; d₀Is the critical distance between nodes; d_i,jIs the distance between node i and node j;

when the distance between the source node and the destination node is smaller than the critical distance, the energy consumption of sending adopts a free space energy consumption model; and when the distance between the source node and the destination node is greater than or equal to the critical distance, the transmitting energy consumption adopts a multipath fading energy consumption model.

Compression energy consumption of i-nodeComprises the following steps:

wherein E is_Com(r_i,j) Indicates when the data compression rate is r_i,jTime data compression energy consumption coefficient;

receiving energy consumption of i nodeComprises the following steps:

further, step S105 specifically includes: when no abnormal condition exists, all the conventional nodes start the conventional data stable transmission mode, and all the conventional nodes adopt self-adaptive packet compression, so that the overall life cycle of the system is effectively prolonged, and the high-reliability service capability of the monitoring system is ensured.

The optimization objective function is:

wherein, varE_re(t +1) is the variance of the residual energy of all nodes at the time t + 1;for node i to have energy remaining at time t +1,the average value of the residual energy of all nodes at the moment t +1 is obtained;

wherein, the node i has residual energy at the moment of t +1For node i remaining energy at time tAnd consuming energyThe difference of (a) is:

compared with the prior art, the invention has the following advantages:

the invention provides a railway disaster prevention monitoring wireless transmission system routing and data compression self-adaptive optimization method, aiming at the condition that the railway disaster prevention monitoring local wireless transmission system has insufficient energy resources, according to the characteristics of limited node energy, differentiated data communication requirements and the like of the railway disaster prevention monitoring linear wireless transmission system, the characteristics of the acquired data are analyzed and judged by fully utilizing the edge computing capability of the sensor node, and the self-adaptive optimization design of routing and data compression ratio is carried out according to the data characteristics, and a mode of 'abnormal data real-time preferential transmission' and a mode of 'conventional data stable transmission' are respectively adopted in a self-adaptive mode according to the monitored data characteristics, the real-time property of abnormal information transmission and the stability of normal information transmission are ensured under the condition of multiple monitoring nodes and complex monitoring environment, and rich and accurate disaster-causing element monitoring data and disaster-preventing emergency decision support are ensured to be provided for the safe operation and decision of a railway system; the safety service capability of the railway disaster prevention monitoring system is ensured, and the reliable service capability of the system is greatly improved.

Drawings

Fig. 1 is a flow chart of a railway disaster prevention monitoring wireless transmission system routing and data compression adaptive optimization method in an embodiment of the present invention;

fig. 2 is a general structural diagram of a railway disaster prevention monitoring wireless transmission system routing and data compression adaptive optimization method in an embodiment of the present invention;

fig. 3 is a flowchart of an implementation of a method for adaptive optimization of routing and data compression in a railway disaster prevention monitoring wireless transmission system according to an embodiment of the present invention.

Detailed Description

In order to make the aforementioned objects, features and advantages of the present invention comprehensible, the present invention is described in further detail with reference to the accompanying drawings and the detailed description thereof. It should be emphasized that the following description is merely exemplary in nature and is not intended to limit the scope of the invention or its application.

Referring to fig. 1, a flow chart of a method for adaptive optimization of route and data compression of a railway disaster prevention monitoring wireless transmission system is shown, wherein the method comprises the following steps:

step S101: initializing a wireless routing matrix F and a data compression rate matrix R of a node according to the architecture of the railway disaster prevention monitoring wireless transmission system;

step S102: acquiring data acquired by each disaster prevention monitoring sensor node at present, and performing edge end preprocessing and characteristic analysis on the data;

step S103: preliminarily judging whether the disaster prevention monitoring data content has abnormal conditions or not; if the abnormal condition exists, the step S104 is executed; if no abnormal condition exists, the step S105 is executed;

step S104: when an abnormal condition exists, entering an abnormal data real-time priority transmission mode:

the abnormal node data adopts a low-compression and less-packet mode to ensure the real-time property of abnormal data transmission; the conventional node data is compressed by adopting self-adaptive sub-packets; the method effectively inhibits the energy consumption imbalance among nodes caused by the prior transmission of abnormal data and ensures the high-safety service capability of the system;

step S105: when no abnormal condition exists, all the conventional nodes start the conventional data stable transmission mode, and all the conventional nodes adopt self-adaptive packet compression, so that the overall life cycle of the system is effectively prolonged, and the high-reliability service capability of the monitoring system is ensured.

Fig. 3 is a flowchart of an implementation of a method for adaptive optimization of routing and data compression in a railway disaster prevention monitoring wireless transmission system according to an embodiment of the present invention. The method comprises the following specific steps:

step 1: initializing a wireless routing matrix F and a data compression rate matrix R of a node according to the architecture of the railway disaster prevention monitoring wireless transmission system;

the wireless routing matrix F is:

n represents the number of sensor nodes in the railway disaster prevention monitoring wireless transmission system, B represents a sink node of disaster prevention monitoring data or represents a base station node along the railway; f. of_i,jRepresents the amount of data transmitted from base station node i to base station node j, i being 0,1,2, …, N, j being 1,2, …, B; assuming that data is transmitted to a base station in a one-way mode, wherein the wireless routing matrix F is an upper triangular matrix;

the data compression matrix R is:

wherein r is_i,jThe data compression rate adopted on a data link transmitted from a base station node i to a base station node j is represented, data is supposed to be transmitted in a single direction towards the base station, and the data compression matrix R is an upper triangular matrix; wherein the compression ratio r is defined as the amount d of data after compression_comAnd the amount of data d before compression_oriThe ratio of (a) to (b), namely:

step 2: acquiring data acquired by each disaster prevention monitoring sensor node at present, and performing edge end preprocessing and characteristic analysis on the data;

step 3: preliminarily judging whether the disaster prevention monitoring data content has abnormal conditions or not; if the abnormal condition exists, the step S104 is executed; if no abnormal condition exists, the step S105 is executed;

specifically, the abnormal conditions comprise storm/rain/snow, earthquake, and foreign body invasion;

step S104: when an abnormal condition exists, entering an abnormal data real-time priority transmission mode; the abnormal node data adopts a low-compression and less-packet mode to ensure the real-time property of abnormal data transmission; the conventional node data is compressed by adopting self-adaptive sub-packets; the method effectively inhibits the energy consumption imbalance among nodes caused by the prior transmission of abnormal data and ensures the high-safety service capability of the system;

step S104 specifically includes:

s104-1, determining the data transmission mode of the abnormal node:

(1) calculating the energy consumption and time delay of the non-sub-package non-compression multi-hop routing transmission of abnormal node monitoring data;

(2) calculating a multi-hop transmission mode with the lowest delay grade;

(3) the transmission mode with the same delay grade and the most balanced energy consumption is optimized;

(4) calculating whether the energy consumption of the transmission mode selected in the step (3) is greater than the residual energy, and if so, outputting an optimal route for abnormal data transmission; if not, calculating a transmission mode with a higher delay level, and then entering the step (3) until an optimal route for abnormal data transmission is output.

Step S104-1 can ensure that under the condition that the communication capability (the communication capability comprises a single-hop transmission distance and the relation between the residual energy and the transmission energy consumption) allows, the data processing and transmission time delay is reduced to the maximum extent; when the communication capability is not allowed, under the condition of the same time delay, the total transmission energy consumption is reduced to the maximum extent:

step S104-1 in step (1), the time delay T is generated in three processes of data compression, transmission and decompression, and the calculation method of the time delay T is as follows:

T＝T_c+T_t+T_d

wherein, T_cCompressing the time delay for the data; t is_tIs the data transmission delay; t is_dDecompressing the data for a delay;

data compression delay T_cThe compression algorithm and the compression ratio are jointly determined, and when the same compression algorithm is adopted, the lower the compression ratio is, the larger the corresponding compression time delay is; data transmission delay T_tThe number of data packets and the number of hops are jointly determined, and the higher the number of packets and the number of hops is, the larger the corresponding transmission delay is; data ofDecompression delay T_dThe decompression algorithm and the compression ratio are jointly determined, and when the same decompression algorithm is adopted, the lower the compression ratio is, the larger the corresponding decompression time delay is;

in step S104-1, in step (1), the node energy consumption is composed of three parts, namely data transmission, compression and reception, and since data is decompressed uniformly at the base station, the patent assumes that the energy of the base station is not constrained, and therefore decompression energy consumption is not considered: the node energy consumption calculation method comprises the following steps: e_T＝E_s+E_c+E_r

Wherein E is_sEnergy consumption for data transmission; e_cEnergy consumption for data compression; e_rEnergy consumption for data reception; energy consumption for data transmission E_sThe larger the data volume is, the longer the distance is, the larger the corresponding transmission energy consumption is; energy consumption for data compression E_cThe compression algorithm, the compression ratio and the compressed data volume are jointly determined, and when the same compression algorithm is adopted, the lower the compression ratio is, the larger the data volume is, and the larger the corresponding compression energy consumption is; energy consumption for data reception E_rThe larger the received data amount is, the larger the corresponding receiving energy consumption is.

Wherein the transmission energy consumption of the i nodeComprises the following steps:

wherein E is_eleThe energy consumption parameter of the electronic device is constant; xi_fsIs a free space energy dissipation coefficient, which is a constant; xi_mpThe coefficient is a multipath fading energy dissipation coefficient and is a constant; d₀Is the critical distance between nodes; d_i,jIs the distance between node i and node j;

when the distance between the source node and the destination node is smaller than the critical distance, the energy consumption of sending adopts a free space energy consumption model; and when the distance between the source node and the destination node is greater than or equal to the critical distance, the transmitting energy consumption adopts a multipath fading energy consumption model.

Compression energy consumption of i-nodeComprises the following steps:

wherein E is_Com(r_i,j) Indicates when the data compression rate is r_i,jTime data compression energy consumption coefficient;

receiving energy consumption of i nodeComprises the following steps:

s104-2, the conventional node adopts a self-adaptive packet compression mode for transmission; the method aims to inhibit the energy consumption imbalance among nodes caused by abnormal node data transmission;

the optimization objective function is:

wherein, varE_re(t +1) is the variance of the residual energy at the moment of t +1 of all nodes, and the smaller the variance is, the better the energy consumption balance among all nodes is;for node i to have energy remaining at time t +1,the mean value of the residual energy at the moment t +1 of all the nodes is obtained;

wherein, the node i has residual energy at the moment of t +1For node i remaining energy at time tAnd consuming energyThe difference of (a) is:

step S105 specifically includes: when no abnormal condition exists, all the conventional nodes start the conventional data stable transmission mode, and all the conventional nodes adopt self-adaptive packet compression, so that the overall life cycle of the system is effectively prolonged, and the high-reliability service capability of the monitoring system is ensured.

The optimization objective function is:

wherein, varE_re(t +1) is the variance of the residual energy at the moment of t +1 of all nodes, and the smaller the variance is, the better the energy consumption balance among all nodes is;for node i to have energy remaining at time t +1,the average value of the residual energy of all nodes at the moment t +1 is obtained;

wherein, the node i has residual energy at the moment of t +1For node i remaining energy at time tAnd consuming energyThe difference of (a) is:

the embodiment aims at the influence of communication protocol design of a railway disaster prevention monitoring wireless transmission system on the safety service capability and the stable service capability of the system, and designs a reasonable and effective routing and data compression rate adaptive optimization method, which fully exerts the edge computing capability of a sensor node to pre-judge the characteristics of monitored data, and carries out routing and data compression rate adaptive optimization design according to the data characteristics, thereby ensuring the real-time property of abnormal information transmission and the stability of normal information transmission under the conditions of multiple monitoring nodes and complex monitoring environments, and ensuring that abundant and accurate disaster-causing element monitoring data and disaster prevention emergency decision support are provided for the safety operation and decision of the railway system.

Fig. 2 is a general structure diagram of a method for adaptive optimization of routing and data compression in a railway disaster prevention monitoring wireless transmission system in an embodiment of the present invention, and the general idea is as follows:

the railway disaster prevention monitoring sensor 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 of the corresponding sensor.

Before data is sent, the 5 types of sensors are responsible for preprocessing data by the computing unit, analyze data characteristics and judge whether abnormal data appears. If abnormal data occurs, the sensor node starts an 'abnormal data priority transmission mode' to optimize a transmission route, then the rest normal sensor nodes start a 'conventional data stable transmission mode', and self-adaptive packet compression is adopted to optimize the transmission route and data compression; if no abnormal data appears, all the sensor nodes start a conventional data stable transmission mode to optimize transmission routing and data compression; and finally, designing a communication protocol according to the optimal data transmission route and the data compression ratio output by the optimization model, and carrying out data transmission by the system according to the communication protocol.

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.