Railway coal transport car induction heating vehicle body control method, device and equipment

Abstract

This application relates to a method, apparatus, and equipment for controlling the body of an induction-heated railway coal transport car. The method includes: acquiring sensing data from multiple fiber Bragg grating sensors within the induction-heated area of ​​the railway track; arranging the multiple fiber Bragg grating sensors along the extension direction of the railway track at the bottom of the track within the induction-heated area; performing data analysis based on the sensing data from the multiple fiber Bragg grating sensors to obtain data analysis results, including at least peak analysis and pulse group identification; determining a heating control strategy for the induction-heated area based on the data analysis results; and controlling the induction-heated area to heat according to the heating control strategy. This method can improve the accuracy of heating control.

Description

Technical Field

[0001] This application relates to the field of railway transportation technology, and in particular to a method, device and equipment for controlling the body of an induction-heated railway coal car. Background Technology

[0002] When open-top railway cars, such as coal wagons, are running in cold regions, the coal inside is prone to freezing, affecting unloading efficiency. Induction heating technology is used to defrost the car bodies online.

[0003] In related technologies, when railway open wagons (such as coal wagons) are heated in the induction heating area, the train position is usually determined by manual observation or traditional electrical sensors (such as photoelectric, ultrasonic, and Hall sensors), and then the heating is controlled according to the determined train position.

[0004] However, the induction heating area has a strong alternating electromagnetic field, and the outdoor environment of railway operation is characterized by high vibration, dust, and large temperature difference, which makes the signals of traditional electrical sensors extremely susceptible to interference, resulting in false triggering, missed triggering, or signal distortion, leading to inaccurate actual heating control. Summary of the Invention

[0005] Therefore, it is necessary to provide a method, device, and equipment for controlling the induction heating vehicle body of a railway coal transport car that can improve the accuracy of heated vehicle body control, in response to the above-mentioned technical problems.

[0006] Firstly, this application provides a method for controlling the body of an induction-heated railway coal transport car, including:

[0007] The sensing data corresponding to each of the multiple fiber Bragg grating sensors in the induction heating area of ​​the railway track are acquired; the multiple fiber Bragg grating sensors are arranged at the bottom of the track in the induction heating area along the extension direction of the railway track;

[0008] Data analysis is performed on the sensing data corresponding to each of the multiple fiber Bragg grating sensors to obtain data analysis results. The data analysis includes at least peak analysis and pulse group identification.

[0009] Based on the data analysis results, a heating control strategy for the induction heating area is determined;

[0010] The heating control strategy is used to control the induction heating area to be heated.

[0011] Secondly, this application also provides a control device for the induction heating vehicle body of a railway coal transport car, comprising:

[0012] The acquisition module is used to acquire the sensing data corresponding to each of the multiple fiber Bragg grating sensors in the induction heating area of ​​the railway track; the multiple fiber Bragg grating sensors are arranged at the bottom of the track in the induction heating area along the extension direction of the railway track.

[0013] The analysis module is used to perform data analysis based on the sensing data corresponding to each of the multiple fiber Bragg grating sensors to obtain data analysis results. The data analysis includes at least peak analysis and pulse group identification.

[0014] The decision module is used to determine the heating control strategy for the induction heating area based on the data analysis results.

[0015] The control module is used to control the induction heating area to be heated based on the heating control strategy.

[0016] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the above-described method.

[0017] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described method.

[0018] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the above-described method.

[0019] The aforementioned method, device, and equipment for controlling the induction-heated car body of railway coal transport vehicles utilize multiple fiber optic grating sensors deployed at the bottom of the track in the induction heating area along the extension direction of the railway track. These sensors collect sensing data, and combined with peak analysis and pulse group identification, the data is analyzed to formulate a heating control strategy, thereby achieving precise control of induction heating. Compared to traditional electrical sensors, fiber optic grating sensing possesses extremely strong resistance to strong electromagnetic interference, effectively avoiding misjudgments of the car body's position and improving the reliability of vehicle position detection. Simultaneously, peak analysis can determine the car body's load, and pulse group identification can identify the car body's type and entry status, providing accurate data support for induction heating. Based on the actual situation of the vehicle, a heating control strategy is determined to achieve precise heating. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a diagram illustrating the application environment of a railway coal transport car induction heating vehicle body control method in one embodiment.

[0022] Figure 2 This is a flowchart illustrating a method for controlling the induction heating vehicle body of a railway coal transport car in one embodiment.

[0023] Figure 3 This is a flowchart illustrating the data analysis process involved in one embodiment;

[0024] Figure 4 This is a schematic diagram of an induction heating system for a coal truck involved in one embodiment;

[0025] Figure 5 This is a schematic diagram of the judgment logic involved in one embodiment;

[0026] Figure 6 This is a structural block diagram of the induction heating vehicle body control device for a railway coal transport car in one embodiment;

[0027] Figure 7 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0028] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0029] It should be noted that the terms "first," "second," etc., used in this application can be used to describe various elements, but these elements are not limited by these terms. These terms are only used to distinguish the first element from the second element. The terms "comprising" and "having," and any variations thereof, used in this application, are intended to cover non-exclusive inclusion. The term "multiple" used in this application refers to two or more. The term "and / or" used in this application refers to one of the embodiments, or any combination of multiple embodiments.

[0030] The induction heating vehicle body control method for railway coal transport cars provided in this application embodiment can be applied to, for example... Figure 1The application environment shown is illustrated. Terminal 101 communicates with server 102 via a network. A data storage system can store the data that server 102 needs to process. The data storage system can be integrated onto server 102, or it can be located in the cloud or on another network server.

[0031] Railway maintenance personnel can initiate a vehicle position detection task for the induction heating area through terminal 101. Server 102 responds to the vehicle position detection task by acquiring the sensing data corresponding to each of the multiple fiber Bragg grating sensors in the induction heating area of ​​the railway track. The multiple fiber Bragg grating sensors are set at the bottom of the track in the induction heating area along the extension direction of the railway track. Data analysis is performed based on the sensing data corresponding to each of the multiple fiber Bragg grating sensors to obtain data analysis results. The data analysis includes at least peak analysis and pulse group identification. Based on the data analysis results, a heating control strategy for the induction heating area is determined. The heating control strategy is used to control the induction heating area for heating.

[0032] Terminal 101 can be, but is not limited to, various personal computers, laptops, smartphones, tablets, drones, low-altitude aircraft, IoT devices, and portable wearable devices. IoT devices can include smart speakers, smart TVs, smart air conditioners, smart in-vehicle devices, and projection equipment. Portable wearable devices can include smartwatches, smart bracelets, and head-mounted displays. Head-mounted displays can be virtual reality (VR) devices, augmented reality (AR) devices, and smart glasses. Server 102 can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services.

[0033] In one exemplary embodiment, such as Figure 2 As shown, a method for controlling the induction heating vehicle body of a railway coal transport car is provided, which is then applied to... Figure 1 Taking the server in the example, the explanation includes the following steps 201 to 204. Wherein:

[0034] Step 201: Obtain the sensing data corresponding to each of the multiple fiber Bragg grating sensors in the induction heating area of ​​the railway track; the multiple fiber Bragg grating sensors are set at the bottom of the track in the induction heating area along the extension direction of the railway track.

[0035] The induction heating zone of the railway track refers to the track area equipped with induction heating equipment that is set up in advance on the coal-carrying track to address the problem of coal freezing in the carriages of coal-carrying trains. When the train enters the induction heating zone, the train carriages entering the zone can be induction heated through the induction heating zone.

[0036] A fiber optic grating sensor is a passive sensing device that uses optical fiber as the transmission carrier and relies on the principle of grating wavelength modulation. In this scheme, a fiber optic grating sensor is used to sense the deformation of the railway track. Specifically, the degree of deformation of the railway track is reflected by the change in the sensing data corresponding to the fiber optic grating sensor, such as the shift of the sensor's center wavelength.

[0037] Understandably, multiple fiber Bragg grating sensors are evenly arranged and fixed at the bottom of the track in the induction heating area along the extension direction of the railway track. This allows them to collect track deformation data corresponding to the induction heating area. When the coal train does not enter the induction heating area, the track only bears its own structural weight, and the sensor wavelength signal is stable with no significant fluctuations. When the train enters the induction heating area, the train's own weight and the coal load in the carriage will directly act on the surface of the rail. The pressure is transmitted to the bottom of the track through the rail. Under the pressure, the rail will produce a small elastic deformation, which simultaneously triggers the deformation of the grating structure inside the fiber Bragg grating sensor installed at the bottom of the rail. This changes the internal optical signal of the sensor, causing the sensor's sensing data to fluctuate regularly.

[0038] Step 202: Perform data analysis based on the sensing data corresponding to each of the multiple fiber Bragg grating sensors to obtain data analysis results. The data analysis includes at least peak analysis and pulse group identification.

[0039] Peak analysis refers to the process of identifying, extracting, and analyzing peak values ​​in the sensing data output by fiber Bragg grating sensors; it can be understood that the magnitude of the peak value reflects the magnitude of the load on the railway track.

[0040] Pulse group identification refers to the process of classifying, matching, and identifying multiple signal pulses that appear consecutively in time. It can be understood that when a train wheel is directly above a fiber Bragg grating sensor and rolls over the track, the sensor's sensing data will change drastically and instantaneously. After the train wheel passes the sensor, the sensor's sensing data will drop back, thus forming a signal pulse. Therefore, the grouping of signal pulses actually reflects the passage of the train wheel or carriage.

[0041] In some embodiments, pulse group identification can not only identify signal pulses from the same fiber Bragg grating sensor, but also further match pulse groups from multiple fiber Bragg grating sensors and perform pulse timing correlation analysis. For example, timing matching can be performed on pulse groups from fiber Bragg grating sensors at different locations. If the matching is successful, the train speed can be calculated based on the distance between the two fiber Bragg grating sensors.

[0042] The data analysis results include at least peak analysis results and pulse group identification results; furthermore, the peak analysis results may include the time of occurrence of the peak, the amplitude of the peak, the number of times the peak occurs, etc.; the pulse group identification results may include the start time of the pulse group, the end time, the number of pulses, the pulse interval, the overall duration, waveform sequence characteristics, etc.

[0043] Step 203: Based on the data analysis results, determine the heating control strategy for the induction heating area.

[0044] The heating control strategy refers to adopting a corresponding heating strategy based on the detected position and type of the train entering the induction heating area. The train position can include the position of entering the area, the position of leaving the area, etc., and the train type can include a locomotive, that is, a car used to pull the carriages, or a freight car, that is, a carriage actually carrying goods.

[0045] Heating control strategies may include whether heating is enabled, the duration of a single heating cycle, and the output power of the heating.

[0046] For example, if the data analysis results indicate that the locomotive is entering the induction heating area, since the locomotive will not be carrying cargo, the heating control strategy is determined to be to wait for heating.

[0047] Step 204: Control the induction heating area to heat based on the heating control strategy.

[0048] In some embodiments, considering that the induction heating area has a certain length, it may cover part of the car body of two carriages at the same time. Therefore, the induction heating area can be provided with multiple heating units, each corresponding to at least one fiber optic grating sensor. Based on this, the heating control strategy can include a control strategy for each heating unit. Thus, the induction heating device can be independently controlled in sections through the control strategy of each heating unit, achieving precise segmented heating and avoiding energy waste caused by continuous heating of the entire area.

[0049] Understandably, the heating control strategy takes into account the relative positional relationship between the train and the induction heating area, as well as the actual state of the train itself, including its load status and vehicle type. By controlling the induction heating area through the heating control strategy, the heating process can be linked in real time with the train's travel status and vehicle type, which can not only ensure the thawing effect of frozen coal in the coal car, but also improve the accuracy of induction heating.

[0050] In the aforementioned method for controlling the induction-heated coal transport vehicle body on railways, multiple fiber optic grating sensors are deployed at the bottom of the track in the induction heating area along the extension direction of the railway track. These sensors collect sensing data, and combined with peak analysis and pulse group identification, data parsing is performed to formulate a heating control strategy, thereby achieving precise control of induction heating. Compared to traditional electrical sensors, fiber optic grating sensing has extremely strong resistance to strong electromagnetic interference, effectively avoiding misjudgments of the vehicle's position and improving the reliability of vehicle position detection. Simultaneously, peak analysis can determine the vehicle's load, and pulse group identification can identify the vehicle's type and entry status, providing accurate data support for induction heating. Based on the actual situation of the vehicle, a heating control strategy is determined to achieve precise heating.

[0051] In one exemplary embodiment, such as Figure 3 As shown, Figure 3 A flowchart illustrating the data analysis process involved in an embodiment of this application is shown, where step 202 includes steps 301 to 303. Wherein:

[0052] Step 301: Peak values ​​are extracted from the sensing data corresponding to each of the multiple fiber Bragg grating sensors to obtain the peak values ​​of the multiple fiber Bragg grating sensors.

[0053] In some embodiments, when extracting the peak value, the time corresponding to the peak value can also be determined, and the fiber optic grating sensor, the peak value, and the time of peak value occurrence can be matched one-to-one.

[0054] In some embodiments, to filter out interference caused by minor fluctuations, a peak threshold can be set, that is, to remove noise and minor environmental disturbances below the peak threshold from the sensed data, thereby retaining only the peak values ​​above the peak threshold.

[0055] In some embodiments, for the sensing data corresponding to each fiber Bragg grating sensor, traversal sampling can be performed within the data range. By comparing the value of a single point with the values ​​of adjacent sampling points before and after it, the value greater than the values ​​of the left and right adjacent sampling points is determined as the peak value.

[0056] Step 302: Perform pulse group identification on the sensing data corresponding to each of the multiple fiber Bragg grating sensors to determine the pulse group mode corresponding to each of the multiple fiber Bragg grating sensors.

[0057] A pulse group refers to a set of pulses formed by aggregating single pulse signals with time intervals that meet a preset pattern within a continuous time window, based on the sensing data of the same fiber optic grating sensor. A single pulse is an independently formed peak signal, and multiple time-related single pulses appearing sequentially according to a certain pattern constitute a pulse group.

[0058] The pulse group mode is a type identifier pre-defined based on the pulse characteristics of the pulse group. The pulse characteristics of the pulse group may include at least one of the following: the number of pulses contained in the pulse group, the time interval between adjacent pulses, the pulse amplitude distribution pattern, and the start and end times of the pulse group.

[0059] In some embodiments, for the sensing data of each fiber Bragg grating sensor, a sliding time window can be used to segment the full data. Then, within a single time window, valid pulses with completed peak extraction are selected and ordered according to the pulse occurrence time to generate the pulse group corresponding to the window. The feature parameters of each group of pulses are extracted and matched with a preset classification rule to determine the pulse group mode corresponding to the current sensor. The above process is repeated by traversing the sensing data of all remaining fiber Bragg grating sensors to obtain the pulse group mode corresponding to each of the multiple fiber Bragg grating sensors.

[0060] Step 303: Based on the peak values ​​and pulse group modes of multiple fiber Bragg grating sensors, data analysis results are obtained.

[0061] In some embodiments, peak and pulse group modes can be combined to obtain data analysis results.

[0062] In other embodiments, multiple peak intervals can be preset, and the data analysis results can be obtained by combining the peak interval to which the peak belongs and the pulse group mode.

[0063] In this embodiment, peak values ​​are extracted and pulse groups are identified from the sensing data of each fiber Bragg grating sensor. The peak parameters and pulse group patterns of all sensors are combined to generate a comprehensive data analysis result. This allows for the identification of vehicle status, including load status, vehicle operating status, and vehicle type, based on both load amplitude and time-series characteristics. This provides reliable data support for the generation of subsequent heating control strategies.

[0064] In one exemplary embodiment, a heating control strategy for the induction heating area is determined based on data analysis results, including:

[0065] The train load is determined based on the peak value identified through peak analysis in the data analysis results.

[0066] The train's load capacity can be a specific load value, such as 2 tons; or it can be the load status, such as empty or heavily loaded.

[0067] In some embodiments, multiple thresholds can be preset, and the train load can be determined based on the relative magnitude relationship between the peak value and the threshold.

[0068] For example, if the peak value is greater than the first threshold, it is determined that a train has passed. Further, if the peak value is greater than the first threshold but less than the second threshold, it is determined that the train is "empty". If the peak value is greater than the third threshold, it is determined that the train is "heavy". The first threshold, the second threshold and the third threshold increase in sequence.

[0069] In other embodiments, the train load can also be obtained by fitting based on the peak value.

[0070] For example, the fiber Bragg grating sensor is pre-calibrated by collecting peak values ​​under different loads. Then, the peak values ​​under different loads are fitted to obtain the fitting calibration curve of the fiber Bragg grating sensor. In this way, after determining the peak value, the corresponding train load can be determined by fitting the calibration curve.

[0071] Based on the pulse group pattern identified through pulse group recognition in the data analysis results, the train type is determined.

[0072] In some embodiments, a correspondence between pulse group modes and train types can be preset, and the train type can be determined by querying the correspondence.

[0073] In other embodiments, multiple pulse group templates and the correspondence between multiple pulse group templates and train types may be pre-set to determine the pulse group template that matches the pulse group pattern, and the corresponding train type may be determined according to the matched pulse group template; wherein, the pulse group template defines the pulse group characteristics of the pulse group pattern.

[0074] Heating control strategies are determined based on train load and train type.

[0075] In some embodiments, the heating control strategy can be determined by rule matching. Specifically, a binding rule base between train type, train load and heating control strategy is established in advance. The measured train type, train load and the binding rule base are compared item by item to obtain a heating control strategy that is suitable for the current working condition.

[0076] In the above embodiments, the train load is calculated using fiber optic grating peak values ​​and the train type is identified using pulse group patterns. This allows for the formulation of heating control strategies based on the train load and train type, improving the accuracy of the heating control strategies and thus ensuring the heating effect.

[0077] In one exemplary embodiment, determining the train type based on the pulse group pattern contained in the data analysis results includes:

[0078] Based on the pulse group patterns contained in the data analysis results, template matching is performed on multiple preset pulse group templates to determine the target pulse group template.

[0079] Among them, the pulse group template is a set of standard pulse features established in advance based on the measured pulse characteristics corresponding to various vehicle models; the pulse group feature parameters, such as the number of pulses and the pulse timing interval, can be defined through the pulse group template.

[0080] Different pulse group modes can correspond to the same pulse group template. By matching the actual pulse group parameters (such as pulse timing interval) in the pulse group mode with the parameters in the pulse group template, if the match is successful, the pulse group template is determined as the target pulse group template.

[0081] Based on the mapping relationship between the preset pulse group template and the train type, the train type corresponding to the target pulse group template is determined.

[0082] For example, if the target pulse group template is the first pulse group template, then the first train type corresponding to the first pulse group template is determined as the train type.

[0083] In the above embodiments, the vehicle model is identified by matching the pulse group mode with the preset template. The vehicle model is quickly determined based on the preset mapping relationship between the template and the vehicle model, thereby improving the accuracy of vehicle model identification and ensuring the reliability of subsequent heating control strategy formulation.

[0084] In one exemplary embodiment, a heating control strategy is determined based on the train load and train type, including:

[0085] When the train type is indicated as a locomotive, the heating control strategy is set to wait for heating.

[0086] The locomotive is used to haul the cargo cars. Obviously, since the locomotive does not carry cargo, it does not need to be heated. However, the locomotive is connected to the cargo cars that are loaded with cargo, which need to be heated. Therefore, the heating control strategy is to wait for heating. This will prevent the locomotive from being heated ineffectively and will also put the induction heating equipment into standby mode in advance, thus preparing for the heating of the subsequent cargo cars.

[0087] When the train type is indicated as freight and the train load is indicated as heavy load, the thermal position control strategy is determined to be full-power heating.

[0088] It is understandable that heavy-duty trucks have a large cargo load, and the cargo is more compressed and piled up, leading to a more severe freezing phenomenon. Therefore, using full-power heating can improve heating efficiency and achieve sufficient heating for heavy-duty trucks.

[0089] When the train type is indicated as freight car and the train load is indicated as empty, the heating control strategy is determined to be energy-saving heating.

[0090] It is understandable that empty trucks have a smaller cargo load and the freezing phenomenon between cargo is relatively minor. Therefore, adopting energy-saving heating can effectively prevent freezing and reduce heating energy consumption.

[0091] In the above embodiments, differentiated heating strategies are implemented according to the vehicle type and load. Different heating strategies are adopted for three different train positions: locomotive, heavy-duty freight car, and empty freight car, taking into account both heating reliability and energy-saving effect of the equipment.

[0092] In one exemplary embodiment, the mounting positions corresponding to the plurality of fiber Bragg grating sensors include at least the start and end positions of the induction heating area; controlling the heating of the induction heating area based on a heating control strategy includes:

[0093] Based on the relative positional relationship between multiple fiber Bragg grating sensors and the trigger time of the sensing data corresponding to multiple fiber Bragg grating sensors, the train speed, the entry time of the induction heating area, and the passage time are determined.

[0094] Understandably, when the fiber optic grating sensor at the starting point of the induction heating area first detects a sensing data pulse, it means that the train has begun to enter the induction heating area, and the corresponding time is the entry time. When the fiber optic grating sensor at the ending point of the induction heating area detects a data pulse, and the sensing data of the fiber optic grating sensor at the starting point has not detected a data pulse for a period of time, it means that the train has left the induction heating area, and the corresponding time is the passing time.

[0095] Meanwhile, when the fiber Bragg grating sensor at the starting position and the fiber Bragg grating sensor at the ending position detect the same pulse one after the other, the train speed can be estimated by the time interval between the two pulses and the relative distance between the two fiber Bragg grating sensors.

[0096] The timing for controlling the induction heating zone is determined based on the entry and exit times of the zone.

[0097] Specifically, the start time for control will be determined by the entry time, and the end time for control will be determined by the exit time.

[0098] It should be noted that the control timing is not the time when heating begins, but rather the time when the heating control strategy is first implemented.

[0099] Heating of the induction heating area is controlled based on train speed, control timing, and heating control strategy.

[0100] In some embodiments, it may be necessary to first determine whether to start executing the heating control strategy based on the control timing. If the basic heating power in the heating control strategy is not zero, the basic heating power is corrected according to the train speed to obtain the corrected heating power. The corrected heating power is then used to replace the basic heating power in the heating control strategy, and the induction heating area is controlled to be heated according to the updated power parameters.

[0101] In the above embodiments, the train speed and the time when the train enters and leaves the heating zone are calculated based on the spacing between the fiber optic grating measurement points at the beginning and end and the signal trigger time difference, thereby determining the control timing of the heating control strategy and ensuring the heating execution effect of the heating control strategy.

[0102] For ease of understanding, the following description will be provided in conjunction with specific embodiments. It should be noted that these embodiments are merely illustrative examples and do not constitute a limitation on this solution.

[0103] Please see Figure 4 , Figure 4 The diagram shows a schematic of an induction heating system for a coal truck according to an embodiment of this application. It includes multiple fiber Bragg grating sensors, including a first fiber Bragg grating sensor (FBG) 1, a second fiber Bragg grating sensor (FBG2), a third fiber Bragg grating sensor (FBG3), and a fourth fiber Bragg grating sensor (FBG4) installed sequentially on the track of the induction heating area along the vehicle's direction of travel. FBG1 and FBG2 are located on both sides of the starting point of the induction heating area, while FBG3 and FBG4 are located on both sides of the ending point of the induction heating area.

[0104] Each fiber Bragg grating sensor is connected to a fiber optic fusion splice box via an independent fiber optic lead. The fiber optic fusion splice box is located beside the rail and is used to aggregate the fiber optic lines output by multiple fiber Bragg grating sensors. The optical signals are then aggregated and output to the ODF (Optical Distribution Frame) via one or more optical cables.

[0105] The ODF rack is installed indoors and serves as a distribution and scheduling node for fiber optic lines, transferring optical signals to the optoelectronic conversion unit via fiber optic patch cords.

[0106] The photoelectric conversion unit is also located indoors and is used to demodulate the received optical signal and convert it into an electrical signal. The detection data, which includes strain / wavelength change information, is then sent to the vehicle body and position detection unit via an industrial Ethernet or serial communication interface.

[0107] The vehicle body and position detection unit are located indoors and integrate signal processing algorithms. Based on the response timing of each FBG sensor, the system analyzes information such as the vehicle's wheelset position, driving direction, speed, and number of axles. It also retrieves axle load data based on wavelength offset to distinguish between locomotives, empty cars, and coal loading cars.

[0108] The power control center is used to electrically connect with the vehicle body and the position detection unit, receive the vehicle type and position determination signals output by them, and use them to control the start and stop of the induction heating device and adjust the heating power.

[0109] Specifically, all FBGs are installed at the bottom of the rail. Wheel passage time and axle load information are obtained through strain measurement. Since the preceding and following positions of each FBG are known, the vehicle position and speed can be accurately identified using the pulse timing of FBG1~FBG4 and the sensed axle load information. Furthermore, by combining the axle load and axle count information detected by the FBGs, railway locomotives can be classified as fully loaded or empty. It should be noted that FBG1 and FBG2 are redundant designs for the starting position, allowing data analysis based on the data from any one FBG. Similarly, FBG3 and FBG4 are redundant designs for the ending position.

[0110] For example, taking the Dongfeng 4B locomotive as an example and the C80 freight car as an example: The locomotive (Dongfeng 4B) has a 3-axle bogie (Co-Co), that is, a single bogie has 3 axles, and two bogies have a total of 6 axles, with a total weight of usually >80 tons (axle load 21 tons+); the fully loaded freight car (C80) has a 2-axle bogie, that is, a single bogie has 2 axles, and two bogies have a total of 4 axles, with a total weight of 100 tons (axle load 25 tons); the empty freight car (C80) has a 2-axle bogie, with a total weight of 20 tons (axle load 5 tons).

[0111] When the wheel passes over the track, the track deformation causes the four FBGs to strain, which in turn generates wavelength shift, i.e., FBG signal. The FBG signal enters the vehicle body and position detection unit through the fiber optic splice box, ODF frame, photoelectric conversion unit, and digital signal.

[0112] In some embodiments, to improve detection accuracy, the induction heating system may also be equipped with a height sensor for synchronously acquiring the vehicle body contour and a speed sensor for synchronously acquiring the vehicle speed.

[0113] Subsequently, by using the strain peak values ​​of FBG1~FBG4 and calibrating the curves, the single axle load and total weight of the current train can be determined. Through pulse timing analysis of FBG1~FBG4, the wheelbase, total number of axles, train speed, and pulse group mode of the train can be determined. In this embodiment, if the pulse group mode is 3 axles, it can be determined as a locomotive; if the pulse group mode is 2 axles, it can be determined as a freight car.

[0114] In addition, the clearance under the vehicle can be determined by using the height profile collected by the height sensor, and then the clearance under the vehicle can be used to further verify the confidence of the train's load. Specifically, in the unloaded state, the vehicle body weight is small, the suspension deformation is small, and the clearance under the vehicle is relatively large. As the load increases, the vehicle suspension is compressed and sinks, and the clearance under the vehicle decreases synchronously. A calibration correspondence between the clearance height and the load state is established in advance. The measured clearance is used to determine the load state, and cross-compare it with the load state obtained by fitting the aforementioned FBG grating to determine whether the load calculation result is reliable.

[0115] The data judgment logic based on FBG is as follows: Figure 5 As shown, the axle load is determined based on the peak value of the FBG signal, and the pulse group pattern is identified based on the pulse pattern of the FBG. Then, the pulse group pattern is combined with the axle load, and different combinations correspond to different control logic.

[0116] Specifically, if the total number of axles is 6 and the bogies have 3 axles, the train is identified as a Dongfeng 4B locomotive; if the total number of axles is 4 and the axle load is greater than 20 tons, the train is identified as a C80 loaded car; if the total number of axles is 4 and the axle load is less than 8 tons, the train is identified as a C80 empty car. The total number of axles is determined by the FBG pulse group mode. If the pulse group mode is two sets of three consecutive pulses, the total number of axles is 6; if the pulse group mode is two sets of two consecutive pulses, the total number of axles is 4.

[0117] Furthermore, the judgment logic for the complete process of the train entering, passing through, and leaving the heating area is shown in the table below:

[0118]

[0119] In some embodiments, the inlet and outlet groups of the induction heating area can each use more than two FBG sensors to further increase redundancy. More FBG groups can also be arranged along the track length to achieve more continuous vehicle position tracking. Meanwhile, in scenarios with extremely high safety requirements and the need to prevent false alarms, the "OR" logic can be changed to "AND" logic (i.e., requiring both FBGs in the same group to be triggered) or more complex composite judgment logic such as "OR followed by AND" or "majority voting." Furthermore, the FBG sensor can be replaced with other optical-based passive sensors, such as Fabry-Perot interferometer fiber optic sensors or distributed fiber optic sensing systems. The latter utilizes an entire fiber optic cable as a sensor, analyzing backscattered light signals to locate vibration or temperature events. Wavelength demodulation can be achieved using demodulators based on different technical approaches, such as tunable filter methods, CCD (Charge-Coupled Device) spectrometer methods, or interferometric scanning methods.

[0120] It should be understood that although the steps in the flowcharts of the above embodiments are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the above embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages in other steps. It is understood that the steps in different embodiments can be freely combined as needed, and all non-contradictory solutions formed by such combinations are within the scope of protection of this application.

[0121] Based on the same inventive concept, this application also provides a railway coal car induction heating vehicle body control device for implementing the above-mentioned railway coal car induction heating vehicle body control method. The solution provided by this device is similar to the solution described in the above method. Therefore, the specific limitations of one or more railway coal car induction heating vehicle body control device embodiments provided below can be found in the limitations of the railway coal car induction heating vehicle body control method described above, and will not be repeated here.

[0122] In one exemplary embodiment, such as Figure 6 The diagram shows a schematic of a railway coal transport car induction heating vehicle body control device 500, which includes:

[0123] The acquisition module 501 is used to acquire the sensing data corresponding to each of the multiple fiber Bragg grating sensors in the induction heating area of ​​the railway track; the multiple fiber Bragg grating sensors are arranged at the bottom of the track in the induction heating area along the extension direction of the railway track.

[0124] Analysis module 502 is used to perform data analysis based on the sensing data corresponding to each of multiple fiber Bragg grating sensors to obtain data analysis results. The data analysis includes at least peak analysis and pulse group identification.

[0125] The decision module 503 is used to determine the heating control strategy for the induction heating area based on the data analysis results.

[0126] The control module 504 is used to control the induction heating area to heat based on the heating control strategy.

[0127] In some embodiments, the analysis module 502 is used to extract peak values ​​from the sensing data corresponding to each of the multiple fiber Bragg grating sensors to obtain the peak values ​​of the multiple fiber Bragg grating sensors; to identify pulse groups from the sensing data corresponding to each of the multiple fiber Bragg grating sensors to determine the pulse group patterns corresponding to each of the multiple fiber Bragg grating sensors; and to obtain data analysis results based on the peak values ​​and pulse group patterns of the multiple fiber Bragg grating sensors.

[0128] In some embodiments, the decision module 503 is used to determine the train load based on the peak value determined by peak analysis in the data analysis results; determine the train type based on the pulse group pattern determined by pulse group identification in the data analysis results; and determine the heating control strategy based on the train load and the train type.

[0129] In some embodiments, the decision module 503 is used to perform template matching in multiple preset pulse group templates based on the pulse group pattern contained in the data analysis results to determine the target pulse group template; and to determine the train type corresponding to the target pulse group template based on the mapping relationship between the preset pulse group template and the train type.

[0130] In some embodiments, the decision module 503 is configured to determine the heating control strategy as waiting for heating when the train type indication is a locomotive; determine the heating control strategy as full-power heating when the train type indication is a freight car and the train load indication is heavy load; and determine the heating control strategy as energy-saving heating when the train type indication is a freight car and the train load indication is empty.

[0131] In some embodiments, the installation positions corresponding to the multiple fiber Bragg grating sensors include at least the start and end positions of the induction heating area; the control module 504 is used to determine the train speed, the entry time and the passage time of the induction heating area based on the relative positional relationship between the multiple fiber Bragg grating sensors and the trigger time of the sensing data corresponding to the multiple fiber Bragg grating sensors; to determine the control timing of the induction heating area based on the entry and passage times of the induction heating area; and to control the induction heating area to heat based on the train speed, the control timing and the heating control strategy.

[0132] The various modules in the aforementioned induction heating vehicle body control device for railway coal transport cars can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in the processor of a computer device in hardware form or independent of it, or stored in the memory of a computer device in software form, so that the processor can call and execute the corresponding operations of each module.

[0133] In one exemplary embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 7 As shown, the computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The database stores data related to the induction heating control of the vehicle body. The I / O interfaces are used for information exchange between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a method for controlling the induction heating of a railway coal transport car.

[0134] Those skilled in the art will understand that Figure 7 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0135] In one exemplary embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the method described above.

[0136] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described method.

[0137] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps of the method described above.

[0138] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0139] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0140] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0141] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for controlling the body of an induction-heated railway coal car, characterized in that, The method includes: The sensing data corresponding to each of the multiple fiber Bragg grating sensors in the induction heating area of ​​the railway track are acquired; the multiple fiber Bragg grating sensors are arranged at the bottom of the track in the induction heating area along the extension direction of the railway track; Data analysis is performed on the sensing data corresponding to each of the multiple fiber Bragg grating sensors to obtain data analysis results. The data analysis includes at least peak analysis and pulse group identification. Based on the data analysis results, a heating control strategy for the induction heating area is determined; The heating control strategy is used to control the induction heating area to be heated.

2. The method according to claim 1, characterized in that, The data analysis based on the sensing data corresponding to each of the multiple fiber Bragg grating sensors, to obtain the data analysis results, includes: Peak values ​​of the multiple fiber Bragg grating sensors are obtained by extracting the sensing data corresponding to each of the multiple fiber Bragg grating sensors. Pulse group identification is performed on the sensing data corresponding to each of the multiple fiber Bragg grating sensors to determine the pulse group mode corresponding to each of the multiple fiber Bragg grating sensors. Data analysis results were obtained based on the peak values ​​and pulse group modes of multiple fiber Bragg grating sensors.

3. The method according to claim 1, characterized in that, The step of determining the heating control strategy for the induction heating area based on the data analysis results includes: Based on the peak value determined by the peak value analysis in the data analysis results, the train load is determined; Based on the pulse group pattern identified through the pulse group identification in the data analysis results, the train type is determined; A heating control strategy is determined based on the train's load and train type.

4. The method according to claim 3, characterized in that, The determination of train type based on the pulse group patterns contained in the data analysis results includes: Based on the pulse group patterns contained in the data analysis results, template matching is performed in multiple preset pulse group templates to determine the target pulse group template; Based on the mapping relationship between the preset pulse group template and the train type, the train type corresponding to the target pulse group template is determined.

5. The method according to claim 3, characterized in that, The determination of the heating control strategy based on the train load and the train type includes: When the train type is indicated as a locomotive, the heating control strategy is determined to be waiting for heating; When the train type indication is freight car and the train load indication is heavy load, the heating control strategy is determined to be full power heating. When the train type is indicated as a freight car and the train load is indicated as empty, the heating control strategy is determined to be energy-saving heating.

6. The method according to any one of claims 1-5, characterized in that, The installation positions corresponding to the plurality of fiber Bragg grating sensors include at least the start position and the end position of the induction heating area; The heating of the induction heating area based on the heating control strategy includes: Based on the relative positional relationship between the plurality of fiber Bragg grating sensors and the trigger time of the sensing data corresponding to the plurality of fiber Bragg grating sensors, the train speed, the entry time and the passage time of the induction heating area are determined; The control timing of the induction heating area is determined based on the entry and exit times of the induction heating area. The induction heating area is heated based on the train speed, the control timing, and the heating control strategy.

7. A control device for the induction heating vehicle body of a railway coal transport car, characterized in that, The device includes: The acquisition module is used to acquire the sensing data corresponding to each of the multiple fiber Bragg grating sensors in the induction heating area of ​​the railway track; the multiple fiber Bragg grating sensors are arranged at the bottom of the track in the induction heating area along the extension direction of the railway track. The analysis module is used to perform data analysis based on the sensing data corresponding to each of the multiple fiber Bragg grating sensors to obtain data analysis results. The data analysis includes at least peak analysis and pulse group identification. The decision module is used to determine the heating control strategy for the induction heating area based on the data analysis results. The control module is used to control the induction heating area to be heated based on the heating control strategy.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

Images