Wind speed warning system, warning method, computer storage medium and device

Abstract

The present invention relates to the field of crosswind monitoring technology, and specifically to a wind speed warning system, a warning method, a computer storage medium and a device. The present invention adopts a data acquisition module to collect and obtain data on the crosswind speed and crosswind pressure of a canyon bridge-tunnel connected section of a high-speed railway line; the data transmission module transmits the collected data to a data processing module; the data processing module can be used to make a short-term prediction of the crosswind development of the canyon bridge-tunnel connected section to obtain a short-term wind speed prediction value; the judgment unit is used to judge the short-term wind speed prediction value and the current running speed of the high-speed train to obtain a judgment result, and the command unit conveys corresponding warning information and speed control instructions to the high-speed train according to the judgment result, so as to ensure the safe operation of the high-speed train, and solve the problem that the wind field development cannot be predicted in the prior art.

Description

Technical Field

[0001] The present invention relates to the technical field of crosswind monitoring, and in particular to a wind speed early warning system, an early warning method, a computer storage medium and equipment for a canyon bridge-tunnel connected section of a high-speed railway line. Background Art

[0002] Research shows that under the influence of crosswinds, especially when the infrastructure of high-speed rail lines (such as tunnels formed by cutting mountains and bridges built between canyons) is rapidly switched, the aerodynamic performance of high-speed trains will deteriorate, endangering driving safety. When a single high-speed train passes through a tunnel in a strong crosswind environment, the aerodynamic effect at the tunnel entrance is most seriously affected by the crosswind. The greater the speed of the high-speed train and the crosswind speed, the greater the amplitude of the lateral force and overturning moment of the front of the train, and the worse the running safety of the whole train.

[0003] In order to ensure the safety and efficiency of high-speed train operation, a tunnel entrance crosswind monitoring system is needed to adjust the speed of high-speed trains according to the crosswind speed at the tunnel entrance. However, the existing crosswind monitoring system can generally only reflect the real-time wind speed, but cannot predict the development of the wind field, and cannot provide early warning information and advance speed adjustment instructions for high-speed trains.

[0004] In summary, it is necessary to provide a wind speed warning system, warning method, computer storage medium and equipment for the canyon bridge-tunnel connecting section of the high-speed railway line to solve the problem that the wind field development cannot be predicted in the prior art. Summary of the invention

[0005] The present invention aims to provide a wind speed warning system, warning method, computer storage medium and equipment for a canyon bridge-tunnel connection section of a high-speed railway line. The specific technical scheme is as follows:

[0006] In a first aspect, the present invention provides a wind speed warning system, including a data acquisition module, a data transmission module, a data processing module, a cloud data center, and a warning module;

[0007] The data acquisition module is arranged upstream of the canyon wind farm on one side of the high-speed railway line;

[0008] The data transmission module is connected to the data acquisition module and is connected to the data processing module via wireless communication;

[0009] The data processing module interacts and interfaces with the cloud data center;

[0010] The early warning module includes a connected judgment unit and an instruction unit, the judgment unit is connected to the data processing module; the instruction unit is connected to the high-speed train through wireless communication.

[0011] Optionally, the data acquisition module includes an anemometer for collecting data, a solar charging panel for providing power to the anemometer, and a data transceiver component for transmitting the collected data; the receiving end of the data transceiver component is connected to the anemometer, and the sending end is connected to the data transmission module; the solar charging panel is connected to the anemometer; the collected data includes crosswind speed and crosswind pressure; the frequency of the anemometer collecting data is within 100 Hz.

[0012] Optionally, the data acquisition module also includes a built-in power supply.

[0013] Optionally, the data transmission module is an ESP32 development board.

[0014] In a second aspect, the present invention provides an early warning method of the wind speed early warning system, comprising:

[0015] Step A1, the number of the data acquisition modules is two, one of which is set outside the tunnel and the distance from the bridge-tunnel connection section is s0-1.5s0, recorded as the first acquisition point; the other data acquisition module is set at the bridge-tunnel connection section, recorded as the second acquisition point; wherein s0 is the ratio of the current running speed of the high-speed train to its current deceleration acceleration, and the product of the ratio and the current crosswind speed is obtained;

[0016] Step A2, using the Bernoulli equation and linear regression in the data processing module to jointly solve the data collected by the two data collection modules in step A1 to obtain the predicted wind speed;

[0017] Step A3, in the data processing module, the least squares method is used to fit the predicted wind speed obtained in step A2, and the short-term wind speed prediction expression of the canyon bridge-tunnel connecting section is obtained, and then the short-term wind speed prediction value is obtained; the judgment unit is used to judge the short-term wind speed prediction value and the current speed of the high-speed train to obtain a judgment result, and the instruction unit conveys corresponding warning information and speed control instructions to the high-speed train according to the judgment result.

[0018] Optionally, in step A2, the Bernoulli equation used is equation (A2.1) and equation (A2.2):

[0019]

[0020]

[0021] Among them, p1 is the crosswind pressure at the first collection point; p2 is the crosswind pressure at the second collection point; v1 is the crosswind speed at the first collection point; v2 is the crosswind speed at the second collection point; v3 is the predicted wind speed; h1 is the height of the first collection point relative to the second collection point; h2 is the height of the second collection point; ρ is the air density; g is the gravitational acceleration; a is the regression coefficient of the crosswind pressure variable; b is the crosswind pressure correction constant term; c is the crosswind speed variable regression coefficient; d is the crosswind speed correction constant term;

[0022] The linear regression used is equation (A2.3) and equation (A2.4):

[0023]

[0024]

[0025] Among them, f(p1, p2) is the regression correction function of the crosswind pressure; is the average crosswind pressure collected at the first collection point; is the average crosswind pressure collected at the second collection point; for The square value of p 1,i is the crosswind pressure corresponding to the first acquisition point in the i-th data pair; p 2,i is the crosswind pressure corresponding to the second acquisition point in the i-th data pair; is the regression correction function of the square of the crosswind speed; is the square value of the average crosswind speed collected at the second collection point; is the square of the average predicted wind speed; for The average value of the square of v 2,i is the crosswind speed corresponding to the second acquisition point in the i-th data pair; r is the total number of data pairs collected from the first acquisition point and the second acquisition point, each data pair includes the crosswind speed and the crosswind pressure; i represents the i-th data pair in the r pairs of data pairs;

[0026] The predicted wind speed v3 shown in equation (A2.5) is obtained by solving equations (A2.1)-(A2.4) simultaneously:

[0027]

[0028] Optionally, in step A3, the predicted wind speed obtained in step A2 is fitted using the least squares method to obtain the short-term wind speed prediction expression for the canyon bridge-tunnel connecting section as follows:

[0029]

[0030]

[0031]

[0032] Among them, t0 is the relative initial time; t m is the current moment; t n is the prediction time; t j For t0 and t n A moment in between; T j is the warning time provided to the high-speed train; s is the average flow of the crosswind from the first collection point to the second collection point; The first acquisition point is at t j The crosswind speed collected at all times; For j The predicted wind speed at the moment; Q is the total amount of collected data; N is the coefficient of the fitting equation; For j -T j The predicted wind speed at the time; is the initial fitting coefficient; is the first fitting coefficient; is the Nth fitting coefficient.

[0033] In a third aspect, the present invention provides a computer storage medium having computer program instructions stored thereon, which implement the early warning method when the computer program instructions are executed by a processor.

[0034] In a fourth aspect, the present invention provides a device comprising: at least one processor, at least one memory, and computer program instructions stored in the memory, wherein the early warning method is implemented when the computer program instructions are executed by the processor.

[0035] The application of the technical solution of the present invention has at least the following beneficial effects:

[0036] The present invention provides a wind speed warning system, warning method, computer storage medium and equipment for a canyon bridge-tunnel connected section of a high-speed railway line. The data acquisition module is used to collect and obtain data on the crosswind speed and crosswind pressure of the canyon bridge-tunnel connected section of the high-speed railway line; the data transmission module transmits the collected data to the data processing module; the data processing module can be used to make a short-term prediction of the crosswind development of the canyon bridge-tunnel connected section to obtain a short-term wind speed prediction value; the judgment unit is used to judge the short-term wind speed prediction value and the current travel speed of the high-speed railway train to obtain a judgment result, and the command unit conveys corresponding warning information and speed control instructions to the high-speed railway train according to the judgment result, so as to ensure the safe operation of the high-speed railway train, and solve the problem of being unable to predict the development of the wind field in the prior art. In addition, the wind speed warning system used in the present invention realizes the automatic intelligence of the entire process of data collection, transmission, processing and decision-making, and can be applied to the short-term wind speed prediction of various canyon terrains and bridge-tunnel connected sections.

[0037] In addition to the above-described purposes, features and advantages, the present invention has other purposes, features and advantages. The present invention will be further described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] The drawings constituting a part of this application are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0039] Figure 1 is a schematic structural diagram of a wind speed warning system in Embodiment 1 of the present invention;

[0040] Figure 2 is a schematic diagram of the structure of the data acquisition module in Example 1;

[0041] Figure 3 is a schematic flow chart of the early warning method in Example 1;

[0042] Among them, 1. Anemometer, 2. Solar charging panel. DETAILED DESCRIPTION

[0043] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field belong to the scope of protection of the present invention.

[0044] Embodiment 1:

[0045] See also Figure 1, a wind speed warning system for a canyon bridge-tunnel connected section of a high-speed railway line, comprising a data acquisition module, a data transmission module, a data processing module, a cloud data center and a warning module;

[0046] The data acquisition module is arranged upstream of the canyon wind farm on one side of the high-speed railway line;

[0047] The data transmission module is electrically connected to the data acquisition module and is connected to the data processing module via wireless communication to ensure the timeliness of data transmission; the data processing module can delete expired acquisition data;

[0048] The data processing module interacts and interfaces with the cloud data center;

[0049] The early warning module includes a connected judgment unit and an instruction unit, the judgment unit is connected to the data processing module through a port; the instruction unit is connected to the high-speed train through wireless communication.

[0050] See also Figure 2 The data acquisition module includes an anemometer 1 for collecting data, a solar charging panel 2 for providing electric energy to the anemometer 1, and a data transceiver component (not shown in the figure) for transmitting the collected data; the receiving end of the data transceiver component is connected to the anemometer 1, and the sending end is connected to the data transmission module; the solar charging panel 2 is connected to the anemometer 1 to achieve self-sufficiency in electric energy and achieve energy saving and environmental protection effects; the collected data include crosswind speed and crosswind pressure; the frequency of data collection by the anemometer 1 is 32Hz.

[0051] The data collected by the data acquisition module is also uploaded to the cloud data center. On the one hand, the cloud data center is used to store data; on the other hand, it is used to provide initial parameters for the data processing module; on the other hand, the collected data can also be used by the cloud data center for driving safety design and canyon bridge wind resistance design.

[0052] The data acquisition module also includes a built-in power supply (not shown in the figure) to supply power to the anemometer 1 when there is insufficient sunshine, so as to maintain the normal operation of the anemometer 1.

[0053] The data transmission module is an ESP32 development board.

[0054] See also Figure 3 , an early warning method of the wind speed early warning system of the canyon bridge-tunnel connected section of the high-speed railway line, comprising:

[0055] Step A1, the number of the data acquisition modules is two, one of which is set outside the tunnel and the distance from the bridge-tunnel connection section is s0, recorded as the first acquisition point; the other data acquisition module is set at the bridge-tunnel connection section, recorded as the second acquisition point; wherein s0 is the ratio of the current running speed of the high-speed train to its current deceleration acceleration, and the product of the ratio and the current crosswind speed is obtained; specifically, considering that personnel are prohibited from entering and setting up instruments during the operation of the high-speed railway, it is necessary to select the construction stage of the high-speed railway or the suspension stage of the high-speed railway in operation for data acquisition;

[0056] Step A2, using the Bernoulli equation and linear regression in the data processing module to jointly solve the data collected by the two data collection modules in step A1 to obtain the predicted wind speed;

[0057] Step A3, in the data processing module, the least squares method is used to fit the predicted wind speed obtained in step A2, and the short-term wind speed prediction expression of the canyon bridge-tunnel connected section is obtained, and then the short-term wind speed prediction value is obtained; the judgment unit is used to judge the short-term wind speed prediction value and the current running speed of the high-speed train to obtain a judgment result, and the instruction unit conveys the corresponding warning information and speed control instructions to the high-speed train according to the judgment result, see Table 1 for details (Table 1 is from https: / / www.360kuai.com / pc / 90c7ef9f87849cce7?cota=3&kuai_so=1&tj_url=so_vip&sign=360_57c3bbd1&refer_scene=so_1; the ambient wind speed in Table 1 is the crosswind wind speed of the present invention; the train running speed requirement in Table 1 is the high-speed train running speed requirement of the present invention; the prohibition of EMU trains from entering the wind zone in Table 1 is the prohibition of high-speed trains from entering the crosswind zone of the present invention).

[0058] Table 1 Warning information and speed control instructions

[0059]

[0060]

[0061] In step A2, the Bernoulli equations used are equations (A2.1) and (A2.2):

[0062]

[0063]

[0064] Among them, p1 is the crosswind pressure at the first collection point; p2 is the crosswind pressure at the second collection point; v1 is the crosswind speed at the first collection point; v2 is the crosswind speed at the second collection point; v3 is the predicted wind speed; h1 is the height of the first collection point relative to the second collection point; h2 is the height of the second collection point; ρ is the air density; g is the gravitational acceleration; a is the regression coefficient of the crosswind pressure variable; b is the crosswind pressure correction constant term; c is the crosswind speed variable regression coefficient; d is the crosswind speed correction constant term;

[0065] The linear regression used is equation (A2.3) and equation (A2.4):

[0066]

[0067]

[0068] Among them, f(p1, p2) is the regression correction function of the crosswind pressure; is the average crosswind pressure collected at the first collection point; is the average crosswind pressure collected at the second collection point; for The square value of p 1,i is the crosswind pressure corresponding to the first acquisition point in the i-th data pair; p 2,i is the crosswind pressure corresponding to the second acquisition point in the i-th data pair; is the regression correction function of the square of the crosswind speed; is the square value of the average crosswind speed collected at the second collection point; is the square of the average predicted wind speed; for The average value of the square of v 2,i is the crosswind speed corresponding to the second acquisition point in the i-th data pair; r is the total number of data pairs collected from the first acquisition point and the second acquisition point, each data pair includes the crosswind speed and the crosswind pressure; i represents the i-th data pair in the r pairs of data pairs;

[0069] The predicted wind speed v3 shown in equation (A2.5) is obtained by solving equations (A2.1)-(A2.4) simultaneously:

[0070]

[0071] In step A3, the predicted wind speed obtained in step A2 is fitted by the least square method to obtain the short-term wind speed prediction expression of the canyon bridge-tunnel connecting section as formula (A2.6)-(A2.8):

[0072]

[0073]

[0074]

[0075] Among them, t0 is the relative initial time; t m is the current moment; t n is the prediction time; t j For t0 and t n A moment in between; T j is the warning time provided to the high-speed train; s is the average flow of the crosswind from the first collection point to the second collection point; The first acquisition point is at t j The crosswind speed collected at all times; For j The predicted wind speed at the moment; Q is the total amount of collected data; N is the coefficient of the fitting equation; For j -T j The predicted wind speed at the time; is the initial fitting coefficient; is the first fitting coefficient; is the Nth fitting coefficient.

[0076] The short-term wind speed prediction performed by Example 1 of the present invention is a choice made based on the pulsating characteristics of natural wind in the canyon. It can well meet the short-term wind speed prediction under different crosswind speeds in different time periods, and has good prediction accuracy under the safety scale of high-speed train operation. At the same time, after being put into operation, it can basically realize full process automation, saving manpower and material resources.

[0077] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A warning method for a wind speed warning system, characterized in that: The early warning method is implemented according to the wind speed early warning system; The wind speed warning system includes a data acquisition module, a data transmission module, a data processing module, a cloud data center and a warning module; The data acquisition module is arranged upstream of the canyon wind farm on one side of the high-speed railway line; The data transmission module is connected to the data acquisition module and is connected to the data processing module via wireless communication; The data processing module interacts and interfaces with the cloud data center; The early warning module includes a judgment unit and an instruction unit connected, and the judgment unit is connected to the data processing module; The command unit is connected to the high-speed train via wireless communication; The early warning method comprises: Step A1, the number of the data acquisition modules is two, one of which is set outside the tunnel and the distance from the bridge-tunnel connection section is s0 to 1.5s0, recorded as the first acquisition point; the other data acquisition module is set at the bridge-tunnel connection section, recorded as the second acquisition point; wherein s0 is the ratio of the current running speed of the high-speed train to its current deceleration acceleration, and the product of the ratio and the current crosswind speed is obtained; Step A2, using the Bernoulli equation and linear regression in the data processing module to jointly solve the data collected by the two data collection modules in step A1 to obtain the predicted wind speed; Step A3, in the data processing module, the predicted wind speed obtained in step A2 is fitted by the least square method to obtain a short-term wind speed prediction expression for the canyon bridge-tunnel connected section, and then a short-term wind speed prediction value is obtained; the judgment unit is used to judge the short-term wind speed prediction value and the current running speed of the high-speed train to obtain a judgment result, and the instruction unit conveys corresponding warning information and speed control instructions to the high-speed train according to the judgment result; In step A2, the Bernoulli equations used are equations (A2.1) and (A2.2): Among them, p1 is the crosswind pressure at the first collection point; p2 is the crosswind pressure at the second collection point; v1 is the crosswind speed at the first collection point; v2 is the crosswind speed at the second collection point; v3 is the predicted wind speed; h1 is the height of the first collection point relative to the second collection point; h2 is the height of the second collection point; ρ is the air density; g is the gravitational acceleration; a is the regression coefficient of the crosswind pressure variable; b is the crosswind pressure correction constant term; c is the crosswind speed variable regression coefficient; d is the crosswind speed correction constant term; The linear regression used is equation (A2.3) and equation (A2.4): Among them, f(p1, p2) is the regression correction function of the crosswind pressure; is the average crosswind pressure collected at the first collection point; is the average crosswind pressure collected at the second collection point; for The square value of p 1,i is the crosswind pressure corresponding to the first acquisition point in the i-th data pair; p 2,i is the crosswind pressure corresponding to the second acquisition point in the i-th data pair; is the regression correction function of the square of the crosswind speed; is the square value of the average crosswind speed collected at the second collection point; is the square of the average predicted wind speed; for The average value of the square of v 2,i is the crosswind speed corresponding to the second acquisition point in the i-th data pair; r is the total number of data pairs collected from the first acquisition point and the second acquisition point, each data pair includes the crosswind speed and the crosswind pressure; i represents the i-th data pair in the r pairs of data pairs; The predicted wind speed v3 shown in equation (A2.5) is obtained by solving equations (A2.1)-(A2.4) simultaneously: In step A3, the predicted wind speed obtained in step A2 is fitted by the least square method to obtain the short-term wind speed prediction expression of the canyon bridge-tunnel connecting section as formula (A2.6)-(A2.8): Among them, t0 is the relative initial time; t m is the current moment; t n is the prediction time; t j For t0 and t n A moment in between; T j is the warning time provided to the high-speed train; s is the average flow of the crosswind from the first collection point to the second collection point; is the first acquisition point at t j The crosswind speed collected at all times; For j The predicted wind speed at the moment; Q is the total amount of collected data; N is the coefficient of the fitting equation; For j -T j The predicted wind speed at the time; is the initial fitting coefficient; is the first fitting coefficient; is the Nth fitting coefficient.

2. The early warning method according to claim 1, characterized in that: The data acquisition module includes an anemometer for collecting data, a solar charging panel for providing power to the anemometer, and a data transceiver component for transmitting the collected data; the receiving end of the data transceiver component is connected to the anemometer, and the sending end is connected to the data transmission module; the solar charging panel is connected to the anemometer; the collected data includes crosswind speed and crosswind pressure; the frequency of the anemometer collecting data is within 100 Hz.

3. The early warning method according to claim 2, characterized in that: The data acquisition module also includes a built-in power supply.

4. The early warning method according to claim 1, characterized in that: The data transmission module is an ESP32 development board.

5. A computer storage medium, characterized in that: Computer program instructions are stored thereon, and when the computer program instructions are executed by a processor, the early warning method according to any one of claims 1 to 4 is implemented.

6. A device, characterized in that: include: At least one processor, at least one memory and computer program instructions stored in the memory, when the computer program instructions are executed by the processor, the early warning method according to any one of claims 1 to 4 is implemented.

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