An orbit temperature acquisition, analysis and early warning system

Abstract

The present invention relates to the technical field of railway engineering. Specifically, it relates to an orbital temperature acquisition, analysis and early warning system. The system analyzes the data transmission quality evaluation coefficients of each sampling frequency based on the transmission parameters of each sampling frequency, thereby determining the optimal sampling frequency and screening out each time point. According to the temperature of each sampling point on the track, the temperature compliance degree of each time point on the track is obtained. According to the snow melting parameters of the track, the snow melting influence coefficient is obtained, and then the snow melting effect evaluation index of each time point on the track is analyzed. From this index, each regulation time point is screened out, and then the temperature is regulated at each regulation time point. At the same time, a time point - snow melting effect evaluation index curve is constructed, and the snow melting effect evaluation index of future time points is obtained from it and fed back to the system, which can timely capture the key information of the orbital temperature change, enable the snow melting measures to be implemented when they are most needed, avoid excessive or insufficient snow melting operations, and improve the overall snow melting efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of railway engineering, and more specifically, to an orbital temperature acquisition, analysis and early warning system. Background Art

[0002] With the rapid development of modern rail transit, the safe and stable operation of the rail system has become a crucial issue. Under different environmental conditions, especially under the influence of temperature changes, the physical properties and structural states of the rails will change significantly. For example, in cold weather, the rails may be affected by snow and ice accumulation, which can affect the normal operation of trains. If the drainage is not smooth when the snow melts, it may also lead to the corrosion of rail components.

[0003] Currently, the traditional rail maintenance method is mostly regular inspection. This method is difficult to accurately and timely grasp the rail temperature status and its potential risks in real time. Therefore, it is an urgent need to effectively collect, deeply analyze and timely warn of the rail temperature to ensure the efficient and safe operation of the rail transit system.

[0004] For example, the existing Chinese patent with the application number 201520508572.0 discloses a rail temperature monitoring system. In this solution, the rail temperature is measured by a nail type temperature sensor, and its position is determined by a positioning module to obtain positioning data. The temperature data is amplified, processed by arithmetic operations, and then sent together with the positioning data through a communication module. The trackside terminal is powered by a solar power generation device. The workstation receives and stores the temperature data. If the temperature exceeds the range, a prompt signal is sent, and it is communicatively connected to a remote monitoring center through an interface, which can accurately locate the rail area with abnormal temperature.

[0005] However, this solution has the following deficiencies: This solution only focuses on the measurement, transmission and monitoring of the current temperature, and does not involve the prediction of the future trend of the rail temperature using historical temperature data and relevant environmental factors. It is unable to perform preventive maintenance in advance for possible rail problems, but can only perform repairs after the problems occur, increasing the cost of repairs and the interference to railway operations.

[0006] For example, the existing Chinese patent with the application number 201910319449.7 discloses a fluid heating and snow melting pavement temperature strain monitoring system. In this solution, an electric heating tube in a heating water tank is controlled by a distribution box to heat the fluid, and the circulation pump works. Under the control of a control valve, the heated fluid flows into an S-shaped pipe in the cement concrete pavement of the pavement system through a connecting pipe. The temperature sensor measurement component collects temperature data through a temperature sensor inside the cement concrete pavement and transmits it to a computer through a collector. The acquisition device in the digital speckle pattern interferometry measurement component collects data and then transmits it to an external digital speckle pattern interferometer and then to the computer. The resistance strain gauge measurement component collects data through a resistance strain gauge inside the cement concrete pavement and transmits it to the computer through a collector, thereby realizing the monitoring of the pavement temperature strain.

[0007] However, this solution has the following drawbacks: This solution focuses on the monitoring of two physical quantities, temperature and strain. For a snow-melting road surface, other influencing factors may also need to be considered, such as the water content and density of the snow cover. The snow-melting solution designed only based on temperature and strain may not provide enough heat to quickly and effectively melt wet snow, resulting in too long a residual time of the snow on the road surface and affecting the normal use of the road surface. Summary of the Invention

[0008] In order to overcome the disadvantages in the background technology, the embodiments of the present invention provide an orbital temperature acquisition analysis and early warning system, which can effectively solve the problems involved in the above background technology.

[0009] The object of the present invention can be achieved by the following technical solutions: The present invention provides an orbital temperature acquisition analysis and early warning system, including: a temperature sampling point division module, which is used to select each rail monitoring point and each turnout monitoring point on the track respectively, collectively referred to as each sampling point.

[0010] A data transmission quality analysis module, which is used to obtain the transmission parameters of each sampling frequency through communication transmission detection, and then evaluate the data transmission quality evaluation coefficient of each sampling frequency, determine the optimal sampling frequency, and screen out each time point.

[0011] A snow-melting effect analysis module, which is used to obtain the temperature of each sampling point on the track and the snow-melting parameters of the track, evaluate the temperature compliance degree and snow-melting influence coefficient of each time point on the track, so as to evaluate the snow-melting effect evaluation index of each time point on the track.

[0012] A temperature regulation module, which is used to screen out each regulation time point according to the snow-melting effect evaluation index of each time point on the track, and then perform temperature regulation on each regulation time point.

[0013] A snow-melting effect prediction module, which is used to construct a time point - snow-melting effect evaluation index curve according to the snow-melting effect evaluation index of each time point on the track, obtain the snow-melting effect evaluation index of future time points from it, and feedback it to the system.

[0014] A management database, which is used to store preset data predicted transmission duration, transmission delay duration reference values, transmission parameters of each sampling frequency, temperatures of each rail monitoring point and each turnout monitoring point at each time point on the track, snow-melting parameters of the track, optimal sampling frequency and each time point.

[0015] Preferably, the specific operation method of the temperature sampling point division module is: divide the track into a rail part and a turnout part, and select a number of monitoring points on the rail part and the turnout part of the track respectively according to the setting, and record them as each rail monitoring point and each turnout monitoring point respectively, collectively referred to as each sampling point.

[0016] Preferably, the transmission parameters include the transmission delay duration of temperature data and the signal strength.

[0017] Preferably, the specific detection method for the communication transmission detection is as follows: First step, in a simulated environment, set different acquisition frequencies according to different initially set acquisition interval durations, denoted as each acquisition frequency. At the same time, take a fixed duration as one acquisition cycle, and select each sampling time point in the acquisition cycle according to each acquisition frequency, denoted as each sampling time point of each sampling frequency. Conduct communication transmission detection on the temperature data at each sampling time point of each sampling frequency. By obtaining the temperature reception time point and sampling time point at each sampling time point of each sampling frequency, obtain the transmission delay duration of the temperature data at each sampling frequency.

[0018] Second step, select a number of equally spaced detection points on the communication cable at preset intervals. Through a specific interface, contact the signal strength detection instrument with each detection point of the communication cable at each sampling time point of each sampling frequency to obtain the signal strength corresponding to each detection point at each sampling time point of each sampling frequency, and obtain the signal strength at each sampling frequency through mean calculation.

[0019] Preferably, the specific analysis method of the data transmission quality analysis module is as follows: Based on the transmission delay duration of the temperature data at each sampling frequency and the signal strength at each sampling frequency, evaluate the data transmission quality evaluation coefficient at each sampling frequency. Sort the data transmission quality evaluation coefficients at each sampling frequency in descending order. Denote the sampling frequency corresponding to the first data transmission quality evaluation coefficient as the optimal sampling frequency, and denote each sampling time point of this sampling frequency as each time point.

[0020] Preferably, the specific analysis method for the temperature compliance degree at each time point of the track is as follows: Obtain the temperatures of each rail monitoring point and each turnout monitoring point at each time point of the track, and compare them with the preset snow melting temperature respectively to obtain the temperature compliance degree of the rail part and the turnout part of the track at each time point.

[0021] Based on the temperature compliance degree of the rail part of the track at each time point and the temperature compliance degree of the turnout part of the track at each time point, perform fusion calculation to obtain the temperature compliance degree of the track at each time point.

[0022] Preferably, the specific analysis method of the snow melting influence coefficient is as follows: First step, take a set amount of snow from different positions near the track, mix them to obtain a snow sample, denote its mass as the initial mass of the snow sample, dry it at a set constant temperature until the water in the snow sample completely evaporates, weigh the remaining solid mass of the snow sample to obtain the remaining mass of the snow sample, and obtain the snow water content by comparing it with the initial mass of the snow sample.

[0023] In the second step, snow samples are collected at different positions near the track and filled into a container with a known volume scale to obtain a snow sample with a known volume, denoted as the snow sampling sample. Its volume is denoted as the snow sampling sample volume. The mass of the snow sampling sample is obtained by weighing the snow sampling sample, and the snow density is calculated through the density formula.

[0024] In the third step, several measurement points are selected on the track surface, denoted as each measurement point. A stable heat flux source is applied on one side of the track structure to conduct heat from one side to the other side. At the same time, a heat flux meter is closely attached to each measurement point, and a temperature sensor is installed near the heat flux meter. The heat flux density of each measurement point measured by the heat flux meter and the temperature of each measurement point measured by the temperature sensor are recorded, and the thermal conductivity of the track is calculated through Fourier's law.

[0025] In the fourth step, the snow melting influence coefficient is calculated based on the snow water content, snow density, and thermal conductivity of the track.

[0026] Preferably, the specific operation method for each regulation time point is as follows: Based on the temperature compliance degree and snow melting influence coefficient of each time point of the track, the snow melting effect evaluation index of each time point of the track is evaluated and compared with the preset snow melting effect evaluation index threshold. If the snow melting effect evaluation index of a certain time point of the track is greater than or equal to the preset snow melting effect evaluation index threshold, it means that the snow melting effect at this time point of the track is qualified. If the snow melting effect evaluation index of a certain time point of the track is less than the preset snow melting effect evaluation index threshold, it means that the snow melting effect at this time point of the track is unqualified, and the temperature of the track at this time point needs to be regulated. This time point is denoted as the regulation time point, and thus each regulation time point is screened out.

[0027] Preferably, the specific operation method of the temperature regulation module is as follows: Read the temperatures of each rail monitoring point and each switch monitoring point at each time point of the track, and re-number them as the temperatures of each sampling point at each time point of the track. Screen out the temperatures of each sampling point at each regulation time point of the track. By subtracting them from the preset snow melting temperature respectively, the temperature to be regulated at each sampling point at each regulation time point of the track is obtained. If the temperature to be regulated at a certain sampling point at a certain regulation time point of the track is a positive number, the temperature at this sampling point is correspondingly increased through the snow melting equipment. If the temperature to be regulated at a certain sampling point at a certain regulation time point of the track is a negative number, the temperature at this sampling point is correspondingly decreased.

[0028] After the snow melting equipment regulates the temperatures of each sampling point at each regulation time point, the snow melting effect evaluation index of each time point of the regulated track is analyzed again, denoted as the regulated snow melting effect evaluation index of each time point of the track.

[0029] Preferably, the specific analysis method of the snow melting effect prediction module is as follows: read the regulated snow melting effect evaluation index at each time point of the track, use the time point as the abscissa and the regulated snow melting effect evaluation index as the ordinate to construct a two-dimensional coordinate system, and then mark several points in the constructed two-dimensional coordinate system for the regulated snow melting effect evaluation index at each time point of the track to form a time point-regulated snow melting effect evaluation index curve. Substitute the future time point to be predicted into the time point-snow melting effect evaluation index curve to obtain the snow melting effect evaluation index of the future time point, and feedback it to the system.

[0030] Compared with the prior art, the present invention has the following beneficial effects: First, the present invention analyzes the data transmission quality evaluation coefficients at each sampling frequency based on the transmission parameters at each sampling frequency, thereby determining the optimal sampling frequency and screening out each time point, which can accurately locate the optimal sampling frequency and improve the data transmission efficiency and accuracy.

[0031] Second, the present invention obtains the temperature compliance degree at each time point of the track according to the temperature at each sampling point of the track, obtains the snow melting influence coefficient according to the snow melting parameters of the track, and then analyzes and obtains the snow melting effect evaluation index at each time point of the track. Thus, each regulated time point is screened out to comprehensively evaluate the snow melting condition of the track and provide a basis for regulation.

[0032] Third, the present invention performs temperature regulation on each regulated time point, constructs a time point-snow melting effect evaluation index curve at the same time, obtains the snow melting effect evaluation index of the future time point from it, and feedbacks it to the system, realizing effective temperature regulation and predicting the future snow melting effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for describing the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention, and those of ordinary skill in the art can obtain other drawings without creative efforts based on these drawings.

[0034] Figure 1 It is the system module connection diagram of the present invention.

[0035] Figure 2 is Figure 1 the flowchart of the communication transmission detection in the data transmission quality analysis module in

[0036] Figure 3 is Figure 1 the analysis flowchart of the snow melting influence coefficient in the snow melting effect analysis module in DETAILED DESCRIPTION OF THE INVENTION

[0037] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without making creative efforts shall fall within the protection scope of the present invention.

[0038] Please refer to Figure 1 As shown, an on-track temperature acquisition, analysis and early warning system includes a temperature sampling point division module, a data transmission quality analysis module, a snow melting effect analysis module, a temperature regulation module, a snow melting effect prediction module, and a management database.

[0039] The management database is connected to the temperature sampling point division module, the data transmission quality analysis module, the snow melting effect analysis module, the temperature regulation module, and the snow melting effect prediction module. The snow melting effect analysis module is connected to the temperature sampling point division module, the data transmission quality analysis module, and the temperature regulation module.

[0040] The temperature sampling point division module is used to select each rail monitoring point and each turnout monitoring point on the track respectively, which are collectively referred to as each sampling point.

[0041] The specific operation method of the temperature sampling point division module is as follows: divide the track into a rail part and a turnout part, and select a number of monitoring points on the rail part and the turnout part of the track respectively according to the setting, which are respectively recorded as each rail monitoring point and each turnout monitoring point, and are collectively referred to as each sampling point; there are differences in structure and function between the rail part and the turnout part, and the degrees and ways of being affected by temperature may also be different. By setting sampling points respectively, the temperature information of each part of the track can be obtained more accurately, providing a more accurate data basis for subsequent snow melting effect analysis and temperature regulation.

[0042] It should be noted that the selection method of each rail monitoring point and each turnout monitoring point is as follows: First step, for the rail part of the track, along the length direction of the rail, select a number of monitoring points at a fixed distance, which are recorded as each rail head monitoring point. Vertically, select a number of monitoring points at different heights at the set height difference at the rail web height, which are recorded as each rail web monitoring point. At the same time, according to the selection method of each rail head monitoring point, select a number of monitoring points at the edge position close to the ballast bed side, which are recorded as each rail bottom monitoring point. Number the each rail head monitoring point, each rail web monitoring point, and each rail bottom monitoring point in the set order as each rail monitoring point. The overall state information of the rail can be comprehensively obtained, and problems that may occur in each part of the rail, such as deformation and damage, can be discovered in time.

[0043] In the second step, for the switch part of the track, a number of monitoring points are respectively set on the switch rail at a set spacing, denoted as each switch rail monitoring point. Monitoring points are set one by one at the outer positions of the basic track corresponding to each switch rail monitoring point, denoted as each basic rail monitoring point. At the same time, a number of monitoring points are selected on the movable point rail at different set positions, denoted as each movable point rail monitoring point. Each switch rail monitoring point, each basic rail monitoring point, and each movable point rail monitoring point are numbered as each switch monitoring point in a set order. The switch rail monitoring points can monitor the state of the switch rail. The basic rail monitoring points can cooperate with the switch rail monitoring points to detect the mutual relationship between the switch rail and the basic rail. The movable point rail monitoring points help to understand the state of the movable point rail, which is very important for ensuring the normal operation of the switch and preventing switch failures.

[0044] The transmission parameters include the transmission delay duration and signal strength of the temperature data.

[0045] The data transmission quality analysis module is used to obtain the transmission parameters of each sampling frequency through communication transmission detection, and then evaluate the data transmission quality evaluation coefficient of each sampling frequency, determine the optimal sampling frequency, and screen out each time point.

[0046] Please refer to Figure 2 As shown, the specific detection method of the communication transmission detection is as follows: In the first step, in a simulation environment, different sampling frequencies are set according to different initially set acquisition interval durations, denoted as each sampling frequency. At the same time, with a fixed duration as one acquisition cycle, each sampling time point is selected in the acquisition cycle according to each sampling frequency, denoted as each sampling time point of each sampling frequency. The communication transmission detection of the temperature data at each sampling time point of each sampling frequency is carried out. By obtaining the temperature reception time point and sampling time point of the temperature data at each sampling time point of each sampling frequency, the transmission delay duration of the temperature data of each sampling frequency is obtained; it helps to understand the time delay situation of temperature data transmission at different sampling frequencies and provides an important index for evaluating data transmission quality. By analyzing the transmission delay duration, it can be determined which sampling frequency can minimize the impact of delay on subsequent analysis and decision-making while ensuring the timely transmission of data.

[0047] It should be noted that the specific analysis method for the transmission delay duration of the temperature data at each sampling frequency is as follows: Monitor the temperature data at each sampling time point for each sampling frequency, convert it into an electrical signal for packaging, send the data packet through wireless communication, and receive it through the corresponding communication interface at the receiving end to obtain the specific time point when the temperature data at each sampling time point for each sampling frequency arrives at the receiving end, which is recorded as the temperature reception time point at each sampling time point for each sampling frequency. At the same time, obtain the temperature sampling time point at each sampling time point for each sampling frequency. By taking the difference between the temperature reception time point and the sampling time point at each sampling time point for each sampling frequency, obtain the transmission duration of the temperature data at each sampling time point for each sampling frequency, and compare it with the preset expected data transmission duration to obtain the transmission delay duration of the temperature data at each sampling frequency.

[0048] In the second step, select a number of equally spaced detection points on the communication cable at preset intervals, and contact the signal strength detection instrument with each detection point on the communication cable at each sampling time point for each sampling frequency through a specific interface to obtain the signal strength corresponding to each detection point at each sampling time point for each sampling frequency, and calculate the signal strength for each sampling frequency through mean calculation; Signal strength is another important indicator to measure the quality of data transmission. By detecting and analyzing the signal strength, the stability and reliability of the signal during transmission under different acquisition frequencies can be understood, which helps to judge the transmission performance of the communication cable and the influence of different acquisition frequencies on signal transmission, so as to provide comprehensive data support for determining the optimal sampling frequency.

[0049] The specific analysis method of the data transmission quality analysis module is as follows: Evaluate the data transmission quality evaluation coefficient for each sampling frequency based on the transmission delay duration of the temperature data at each sampling frequency and the signal strength at each sampling frequency. Sort the data transmission quality evaluation coefficients for each sampling frequency in descending order, record the sampling frequency corresponding to the first data transmission quality evaluation coefficient as the optimal sampling frequency, and record each sampling time point of this sampling frequency as each time point; Selecting the optimal sampling frequency can ensure that during the data acquisition and transmission process, temperature data can be obtained and transmitted at the optimal frequency, improving the effectiveness and reliability of the data, and providing high-quality data support for subsequent work such as analyzing the snow melting effect.

[0050] In a preferred embodiment of the present invention, the specific manner of evaluating the data transmission quality evaluation coefficient for each sampling frequency is as follows: Extract the transmission delay duration of the temperature data at each sampling frequency and the signal strength at each sampling frequency, and then perform a weighted summation calculation to obtain the data transmission quality evaluation coefficient for each sampling frequency.

[0051] Exemplarily, the weights corresponding to the transmission delay duration of the temperature data and the signal strength are 0.6 and 0.4.

[0052] The snow melting effect analysis module is used to obtain the temperature of each sampling point on the track and the snow melting parameters of the track, and evaluate the temperature compliance degree and snow melting influence coefficient at each time point of the track, so as to evaluate the snow melting effect evaluation index at each time point of the track.

[0053] The specific analysis method for the temperature compliance degree at each time point of the track is as follows: Obtain the temperatures of each rail monitoring point and each turnout monitoring point at each time point on the track, and compare them with the preset snow melting temperature respectively to obtain the temperature compliance degrees of the rail part and the turnout part of the track at each time point; It can intuitively reflect the proximity of the temperature of the track at different time points to the temperature required for snow melting, provides a direct temperature index for evaluating the snow melting effect. By analyzing the temperature compliance degrees of different parts of the track, the temperature changes of the rails and turnouts during the snow melting process can be understood, which helps to evaluate the snow melting effects of each part specifically, discover temperature abnormal areas in time, and provide a basis for subsequent temperature regulation.

[0054] In a preferred embodiment of the present invention, the specific method for evaluating the temperature compliance degree at each time point of the track is as follows: Extract the temperatures of each sampling point on the track, compare them with the preset snow melting temperature, and then perform mean value calculation to obtain the temperature compliance degree at each time point of the track.

[0055] Based on the temperature compliance degrees at each time point of the rail part of the track and the temperature compliance degrees at each time point of the turnout part of the track, the temperature compliance degree at each time point of the track is obtained through fusion calculation.

[0056] In a preferred embodiment of the present invention, the specific method for fusing and calculating the temperature compliance degree at each time point of the track is as follows: Extract the temperature compliance degrees at each time point of the rail part of the track and the temperature compliance degrees at each time point of the turnout part of the track, and then perform summation calculation according to the weights to obtain the temperature compliance degree at each time point of the track.

[0057] Exemplarily, the weights corresponding to the temperature compliance degree of the rail part of the track and the temperature compliance degree of the turnout part of the track are 0.7 and 0.3.

[0058] Please refer to Figure 3 As shown, the specific analysis method for the snow melting influence coefficient is as follows: First step, take a set amount of snow from different positions near the track, mix them to obtain a snow sample, record its mass as the initial mass of the snow sample, dry it at a set constant temperature until the water in the snow sample completely evaporates, weigh the remaining solid mass of the snow sample to obtain the remaining mass of the snow sample, and obtain the snow water content by comparing it with the initial mass of the snow sample; Understanding the water content of the snow can help analyze the water change situation of the snow during the melting process and its potential impact on the snow melting of the track.

[0059] It should be noted that the specific analysis method for the snow water content is as follows: read the initial mass and the remaining mass of the snow sample respectively, and record them as , and through the formula the snow water content is obtained .

[0060] In the second step, snow samples are collected at different positions near the track and filled into a container with a known volume scale to obtain a snow sample with a known volume, which is recorded as the snow sampling sample. Record its volume as the snow sampling sample volume. Weigh the snow sampling sample to obtain the weighed mass of the snow sampling sample, and calculate the snow density through the density formula; Snow with different densities varies in heat absorption and melting speed. By measuring the snow density, the hindrance effect of snow on track snow melting can be more accurately evaluated, providing more comprehensive data for the analysis of snow melting effect.

[0061] It should be noted that the specific analysis method for the snow density is as follows: record the snow sampling sample volume and the weighed mass of the snow sampling sample as respectively, and substitute them into the formula to obtain the snow density .

[0062] In the third step, select several measurement points on the track surface, record them as each measurement point. Apply a stable heat source on one side of the track structure to conduct heat from one side to the other side. At the same time, closely attach the heat flux meter to each measurement point, and install a temperature sensor near the heat flux meter. Record the heat flux density of each measurement point measured by the heat flux meter and the temperature of each measurement point measured by the temperature sensor, and calculate the thermal conductivity of the track through Fourier's law; The thermal conductivity of the track reflects the heat conduction performance of the track material, which is crucial for understanding the heat transfer mode and efficiency in the track. By measuring the thermal conductivity, the heat transfer situation of the track during snow melting can be better analyzed, providing important physical parameters for evaluating the snow melting effect.

[0063] In the fourth step, based on the snow water content, snow density, and thermal conductivity of the track, a snow melting influence coefficient is calculated through fusion; It can accurately evaluate the interaction of various factors on the snow melting effect during snow melting, providing a comprehensive quantitative index for accurately evaluating the snow melting effect of the track.

[0064] In a preferred embodiment of the present invention, the specific calculation method of the snow melting influence coefficient is as follows: extract the snow water content, snow density, and thermal conductivity of the track, and then perform a weighted summation calculation to obtain the snow melting influence coefficient.

[0065] Exemplarily, the weights corresponding to the snow water content, snow density, and thermal conductivity of the track are 0.4, 0.3, and 0.3.

[0066] A temperature control module is used to screen out each control time point according to the snow melting effect evaluation index at each time point of the track, and then perform temperature control on each control time point.

[0067] The specific operation method for each control time point is as follows: Based on the temperature compliance degree and snow melting influence coefficient at each time point of the track, the snow melting effect evaluation index at each time point of the track is evaluated. Compare it with the preset snow melting effect evaluation index threshold. If the snow melting effect evaluation index at a certain time point of the track is greater than or equal to the preset snow melting effect evaluation index threshold, it means that the snow melting effect at this time point of the track is qualified. If the snow melting effect evaluation index at a certain time point of the track is less than the preset snow melting effect evaluation index threshold, it means that the snow melting effect at this time point of the track is unqualified, and the temperature of the track at this time point needs to be adjusted. Record this time point as the control time point, and thus screen out each control time point; By comparing the snow melting effect evaluation index with the threshold, the time points with unqualified snow melting effects can be accurately identified, so as to targetedly adjust the temperature of the track at these time points, improve the snow melting efficiency, and ensure the safe operation of the track.

[0068] In a preferred embodiment of the present invention, the specific calculation method of the snow melting effect evaluation index at each time point of the track is as follows: Extract the temperature compliance degree and snow melting influence coefficient at each time point of the track, and then perform a weighted summation calculation to obtain the snow melting effect evaluation index.

[0069] Exemplarily, the weights corresponding to the temperature compliance degree and the snow melting influence coefficient are 0.7 and 0.3.

[0070] The specific operation method of the temperature control module is as follows: Read the temperatures of each rail monitoring point and each turnout monitoring point at each time point of the track, re-number them as the temperatures of each sampling point at each time point of the track, screen out the temperatures of each sampling point at each control time point of the track, and obtain the temperature to be adjusted for each sampling point at each control time point of the track by taking the difference between them and the preset snow melting temperature respectively. If the temperature to be adjusted for a certain sampling point at a certain control time point of the track is a positive number, the temperature at this sampling point is correspondingly increased through the snow melting equipment. If the temperature to be adjusted for a certain sampling point at a certain control time point of the track is a negative number, the temperature at this sampling point is correspondingly decreased; By precisely controlling the temperature of each sampling point, the snow melting effect of the track can be effectively improved, ensuring that the snow on the track surface can be melted in time and reducing the impact of snow accumulation on the operation of the track.

[0071] After the snow melting equipment adjusts the temperature of each sampling point at each control time point, the snow melting effect evaluation index at each time point of the adjusted track is analyzed again, which is recorded as the regulated snow melting effect evaluation index at each time point of the track.

[0072] The snow melting effect prediction module is used to construct a time point - snow melting effect evaluation index curve based on the snow melting effect evaluation indexes at each time point of the track, obtain the snow melting effect evaluation index of future time points therefrom, and feedback it to the system.

[0073] The specific analysis method of the snow melting effect prediction module is as follows: Read the regulated snow melting effect evaluation indexes at each time point of the track. Take the time point as the abscissa and the regulated snow melting effect evaluation index as the ordinate to construct a two - dimensional coordinate system. Then, mark several points for the regulated snow melting effect evaluation indexes at each time point of the track in the constructed two - dimensional coordinate system to form a time point - regulated snow melting effect evaluation index curve. Substitute the future time points to be predicted into the time point - snow melting effect evaluation index curve to obtain the snow melting effect evaluation indexes of future time points, and feedback them to the system. This helps to understand the development trend of the snow melting effect on the track in advance. By predicting the snow melting effect evaluation indexes of future time points, preparations can be made in advance to cope with possible poor snow melting effects, ensuring the safety and normal operation of the track.

[0074] The management database is used to store preset data such as the expected data transmission duration, reference values of transmission delay duration, transmission parameters at each sampling frequency, temperatures at each rail monitoring point and each switch monitoring point at each time point of the track, snow melting parameters of the track, the optimal sampling frequency, and each time point.

[0075] According to the present invention, the data transmission quality evaluation coefficients at each sampling frequency are analyzed to determine the optimal sampling frequency, and each time point is selected. The temperature compliance degree at each time point of the track is obtained based on the temperatures at each sampling point of the track, and the snow melting influence coefficient is obtained based on the snow melting parameters of the track. Then, the snow melting effect evaluation indexes at each time point of the track are analyzed. From these indexes, each regulation time point is selected, and then the temperature is regulated at each regulation time point. At the same time, a time point - snow melting effect evaluation index curve is constructed, the snow melting effect evaluation indexes of future time points are obtained therefrom, and feedback is given to the system. This can timely capture the key information of the track temperature change, enable the snow melting measures to be implemented at the most needed time, avoid excessive or insufficient snow melting operations, and improve the overall snow melting efficiency.

[0076] Although the embodiments of the present invention have been shown and described above, it can be understood that the above - mentioned embodiments are exemplary and should not be construed as limiting the present invention. Those of ordinary skill in the art can make changes, modifications, substitutions, and variations to the above - mentioned embodiments within the scope of the present invention, and still be covered by the protection scope of the present invention.

Claims

1. A track temperature collection, analysis and early warning system, characterized in that: The system specifically includes the following modules: The temperature sampling point division module is used to select each rail monitoring point and each switch monitoring point on the track, collectively referred to as each sampling point; The data transmission quality analysis module is used to obtain the transmission parameters of each sampling frequency through communication transmission detection, wherein the transmission parameters include the transmission delay time and signal strength of the temperature data, and obtain the data transmission quality evaluation coefficient of each sampling frequency according to the transmission parameter analysis of each sampling frequency, determine the optimal sampling frequency, and screen out each time point; The snowmelt effect analysis module is used to obtain the temperature of each sampling point of the track and the snowmelt parameters of the track. The snowmelt parameters of the track include the water content of snow, the density of snow and the thermal conductivity of the track. The snowmelt influence coefficient is obtained according to the snowmelt parameters of the track. The temperature compliance degree of the track at each time point is obtained according to the temperature of each sampling point of the track. The temperature compliance degree reflects the degree of proximity between the temperature of the track at different time points and the temperature required for snowmelt. Based on the temperature compliance degree and snowmelt influence coefficient of each time point of the track, the snowmelt effect evaluation index of the track at each time point is obtained. The temperature control module is used to select each control time point according to the snow melting effect evaluation index at each time point of the track, and then perform temperature control at each control time point; The snowmelt effect prediction module is used to construct a time point-snowmelt effect evaluation index curve based on the snowmelt effect evaluation index at each time point of the track, from which the snowmelt effect evaluation index at the future time point is obtained and fed back to the system; The management database is used to store preset data, estimated transmission time, reference value of transmission delay time, transmission parameters of each sampling frequency, temperature of each rail monitoring point at each time point, each switch monitoring point, snow melting parameters of the track, optimal sampling frequency and each time point.

2. A track temperature collection, analysis and early warning system according to claim 1, characterized in that: The specific operation method of the temperature sampling point division module is: The track is divided into a rail part and a switch part. According to the settings, a number of monitoring points are selected from the rail part and the switch part of the track, which are respectively recorded as rail monitoring points and switch monitoring points, collectively referred to as sampling points.

3. A track temperature collection, analysis and early warning system according to claim 1, characterized in that: The specific detection method of the communication transmission detection is: The first step is to set different acquisition frequencies in a simulation environment according to the different acquisition intervals that are initially set, which are recorded as acquisition frequencies. At the same time, a fixed duration is used as an acquisition cycle. According to each acquisition frequency, each sampling time point is selected in the acquisition cycle, which is recorded as each sampling time point of each sampling frequency. The temperature data of each sampling time point of each sampling frequency is tested for communication transmission. The transmission delay of the temperature data of each sampling frequency is obtained by obtaining the temperature receiving time point and sampling time point of each sampling time point of each sampling frequency. In the second step, several equally spaced detection points are selected on the communication cable according to the preset spacing, and the signal strength detection instrument is contacted with each detection point of the communication cable at each sampling frequency and each sampling time point through a specific interface to obtain the signal strength of each detection point corresponding to each sampling frequency and each time point, and the signal strength of each sampling frequency is obtained by average calculation.

4. A track temperature collection, analysis and early warning system according to claim 3, characterized in that: The specific analysis method of the data transmission quality analysis module is: The data transmission quality evaluation coefficient of each sampling frequency is evaluated based on the transmission delay time of the temperature data of each sampling frequency and the signal strength of each sampling frequency. The data transmission quality evaluation coefficient of each sampling frequency is sorted in order from large to small, and the sampling frequency corresponding to the first data transmission quality evaluation coefficient is recorded as the optimal sampling frequency, and the sampling time points of the sampling frequency are recorded as each time point.

5. The track temperature collection, analysis and early warning system according to claim 1 is characterized in that: The specific analysis method of the temperature compliance degree of the track at each time point is: Obtain the temperature of each rail monitoring point and each switch monitoring point at each time point of the track, and compare them with the preset snowmelt temperature to obtain the temperature compliance degree of the rail part and the switch part at each time point; Based on the temperature compliance of the track rail part at each time point and the temperature compliance of the track switch part at each time point, the temperature compliance of the track at each time point is obtained by fusion calculation.

6. A track temperature collection, analysis and early warning system according to claim 1, characterized in that: The specific analysis method of the snowmelt influence coefficient is as follows: In the first step, a set amount of snow is taken from different locations near the track, mixed to obtain a snow sample, and its mass is recorded as the initial mass of the snow sample. The snow sample is dried at a set constant temperature until the water in the snow sample is completely evaporated. The remaining solid mass of the snow sample is weighed to obtain the residual mass of the snow sample, and the water content of the snow is obtained by comparing it with the initial mass of the snow sample. The second step is to collect snow samples at different locations near the track, fill them into a container with a known volume scale, obtain a snow sample of known volume, record it as a snow sampling sample, record its volume as the snow sampling sample volume, weigh the snow sampling sample to obtain the snow sampling sample mass, and calculate the snow density by the density formula; The third step is to select several measurement points on the track surface, which are recorded as measurement points. A stable heat flux source is applied on one side of the track structure to transfer heat from one side to the other side. At the same time, a heat flux meter is tightly attached to each measurement point, and a temperature sensor is installed near the heat flux meter. The heat flux density of each measurement point measured by the heat flux meter and the temperature of each measurement point measured by the temperature sensor are recorded, and the thermal conductivity of the track is calculated by Fourier's law. The fourth step is to calculate the snowmelt influence coefficient based on the fusion of snow moisture content, snow density and thermal conductivity of the track.

7. A track temperature collection, analysis and early warning system according to claim 6, characterized in that: The specific operation method of each control time point is as follows: The snowmelt effect evaluation index of the track at each time point is compared with the preset snowmelt effect evaluation index threshold. If the snowmelt effect evaluation index of the track at a certain time point is greater than or equal to the preset snowmelt effect evaluation index threshold, it means that the snowmelt effect of the track at that time point is qualified. If the snowmelt effect evaluation index of the track at a certain time point is less than the preset snowmelt effect evaluation index threshold, it means that the snowmelt effect of the track at that time point is unqualified, and the temperature of the track at that time point needs to be controlled. This time point is recorded as the control time point, thereby screening out various control time points.

8. A track temperature collection, analysis and early warning system according to claim 7, characterized in that: The specific operation method of the temperature control module is: Read the temperature of each rail monitoring point and each switch monitoring point at each time point of the track, renumber them as the temperature of each sampling point at each time point of the track, select the temperature of each sampling point at each control time point of the track, and obtain the required control temperature of each sampling point at each control time point of the track by subtracting it from the preset snow melting temperature. If the required control temperature of a sampling point at a certain control time point of the track is a positive number, the temperature at the sampling point is correspondingly increased through the snow melting equipment; if the required control temperature of a sampling point at a certain control time point of the track is a negative number, the temperature at the sampling point is correspondingly decreased; After the snow-melting equipment regulates the temperature of each sampling point at each regulation time point, the snow-melting effect evaluation index of the track at each time point after regulation is analyzed again and recorded as the regulation snow-melting effect evaluation index of the track at each time point.

9. A track temperature collection, analysis and early warning system according to claim 8, characterized in that: The specific analysis method of the snow melting effect prediction module is: The snow-melting effect evaluation index of the track at each time point is read, and a two-dimensional coordinate system is constructed with the time point as the horizontal coordinate and the snow-melting effect evaluation index as the vertical coordinate. Then, several points are marked in the constructed two-dimensional coordinate system according to the snow-melting effect evaluation index of the track at each time point to form a time point-snow-melting effect evaluation index curve. The future time point to be predicted is substituted into the time point-snow-melting effect evaluation index curve to obtain the snow-melting effect evaluation index at the future time point, which is then fed back to the system.

Images