Protection and early warning method for mobile charging intelligent rail road

Abstract

The invention provides a protection and early warning method for a mobile charging intelligent rail road, and belongs to the technical field of early warning protection. The method comprises the steps of 1, monitoring the running bumping condition of a target vehicle on a target intelligent rail road in real time through a vehicle sensor, and obtaining original bumping data; 2, preprocessing the original jolting data to obtain first jolting data, and starting an early warning system; step 3, based on an early warning system, performing jolting grade division on the first jolting data, and adjusting operation parameters of a DC-DC converter in the target intelligent rail road; 4, planning a configuration strategy of the capacitance of the target intelligent rail road based on energy prediction software, and meanwhile, performing early warning according to the jolting level; the influence of power interruption on a vehicle system is effectively reduced, and energy supply and equipment stability in the driving process are guaranteed.

Description

Technical Field

[0001] The present invention relates to the field of early warning protection technology, and in particular to a protection and early warning method for a mobile charging smart rail highway. Background Art

[0002] At present, if a vehicle encounters severe bumps during driving, it will cause the positive and negative poles of the carbon slide to move instantly, and the electrical connection between the pantograph and the contact network will be unstable. The contact point of the carbon layer of the slide on the contact network will fall off or move due to vibration, resulting in voltage fluctuations or instantaneous disconnection. When the voltage drops by more than 200V and lasts for more than 10-20 milliseconds (the threshold that the battery management system and DC-DC can perceive), it indicates that there is obvious reactance interference. In this case, the DC-DC will judge the emergency situation based on its built-in protection mechanism. It monitors not only the voltage value, but also the change of current, the change of network impedance, and the impact of fast transient current. If these parameters exceed the designed safety protection range, the DC-DC will suspend output to prevent excessive current from causing damage to electrical equipment, especially to prevent arcing, which will damage equipment and even cause fire. When the voltage stabilizes, if the restart conditions are met, the DC-DC will restart and adjust the power output according to system requirements to restore normal power supply.

[0003] Therefore, the present invention proposes a protection and early warning method for a mobile charging smart rail highway. Summary of the invention

[0004] The present invention provides a protection and early warning method for a mobile charging smart rail highway, which is used to realize intelligent control and early warning by real-time monitoring of the bumpy conditions of target vehicles on the smart rail highway. First, the vehicle sensor obtains the original bumpy data, determines whether the early warning threshold is reached after preprocessing, and starts the early warning system. Next, the bumpy data is graded according to the early warning system, and the operating parameters of the DC-DC converter in the target smart rail highway are adjusted in combination with the control algorithm. Through the energy prediction software, the DC-DC converter parameters are optimized, and the capacitor configuration strategy is planned according to the bumpy level, and an early warning is provided at the same time. Finally, the smart rail highway is processed in sections, and sections with too high bumpy levels are marked as "caution sections", and the DC-DC converter is controlled to stop power output when the vehicle passes by, thereby improving driving safety and system stability. This process realizes intelligent dynamic adjustment and early warning functions, effectively improving the safety and energy efficiency management of the transportation system.

[0005] On the one hand, the present invention provides a protection and early warning method for a mobile charging smart rail highway, comprising: Step 1: Use vehicle sensors to monitor the running bumps of the target vehicle on the target smart rail highway in real time to obtain the original bump data; Step 2: Preprocessing the original turbulence data to obtain first turbulence data, and if the first turbulence data reaches a preset warning threshold, starting the warning system; Step 3: Based on the early warning system, the first bump data is classified into bump levels, and the operating parameters of the DC-DC converter in the target smart rail highway are adjusted in combination with the control algorithm; Step 4: Based on the energy prediction software, the capacitor configuration strategy of the target smart rail highway is planned in combination with the different operating parameters of the DC-DC converter, and an early warning is issued according to the bump level; Step 5: Divide the target smart rail highway into sections, and mark the sections with bump levels greater than the preset safety level as caution sections. When the target vehicle runs to the caution section, the DC-DC converter is controlled to stop power output.

[0006] On the other hand, the vehicle sensors are used to monitor the running bumps of the target vehicle on the target smart rail highway in real time, and the original bump data is obtained, including: Get a detailed route based on the preset plan of the target smart rail highway; Select the target vehicle and select the corresponding vehicle sensor type according to the bump parameter type; Assign a unique first number to the installation position of the target vehicle, assign a unique second number to each vehicle sensor, and assign the vehicle sensor to the target vehicle according to a one-to-one matching relationship between the installation position and the vehicle sensor; Based on the detailed route, the target vehicle is started and driven from the starting point to the end point of the target smart rail highway; The bump parameter type sensor of the target vehicle is monitored in real time to obtain the original bump data.

[0007] On the other hand, the original turbulence data is preprocessed to obtain first turbulence data, including: If the original turbulence data of any turbulence parameter type is of video type, then the original video is processed frame by frame to obtain an original video sequence; Obtain any original image in the original video sequence, eliminate interference jitter factors from the original image according to a stability algorithm to obtain a standard image, and use an edge detection algorithm to extract edge points in the standard image to obtain an edge image; The original video sequence is processed to generate an edge image sequence, any edge image in the edge image sequence is selected as a central image, an edge image to the right of a time node of the central image is selected as a comparison image, the central image and the comparison image are projected into a standard coordinate system, and the motion state of the target vehicle is evaluated; Get any edge pixel point of the central image and compare and analyze it with the corresponding edge pixel point in the comparison image, and satisfy the following formula, and then get the motion vector of the i-th pixel point: : ; Where n means there are n edge pixels in the center image. Indicates the convolution of the edge pixel of the i-th center image in the x direction. It means to perform convolution in the x direction on the edge pixel of the i-th image. Indicates the convolution of the edge pixel of the i-th center image in the y direction. Indicates the convolution of the edge pixel of the i-th contrast image in the y direction. Indicates the motion vector direction of the i-th pixel in the center image, Represents the motion vector modulus of the i-th pixel in the center image, Indicates the time interval from the center image to the contrast image; By calculating the motion vector of each pixel in any image of the edge image sequence, the pixels are clustered into different areas, including moving areas and static areas, using a clustering algorithm; Performing time series normalization processing on the original bump data of other bump parameter types according to the time nodes of the edge image sequence to obtain normalized bump data; The first turbulence data includes an edge image sequence and motion vectors corresponding to the edge images and standardized turbulence data of other turbulence parameter types.

[0008] On the other hand, if the first turbulence data reaches a preset warning threshold, the warning system is activated, including: Obtaining standardized turbulence data of any turbulence parameter type, and if the standardized turbulence data reaches a preset warning threshold, obtaining an abnormal time interval corresponding to the occurrence of abnormal turbulence; Acquire the corresponding edge image group in the edge image sequence according to the abnormal time interval, analyze the modulus and direction of the motion vector field of all edge images, identify the motion area and the static area, and determine the bump intensity of any motion area in the edge image group based on the motion intensity and direction of the motion area; If the intensity of turbulence reaches the preset warning threshold, the warning system will be activated.

[0009] On the other hand, based on the early warning system, the first turbulence data is classified into turbulence levels, including: Based on the early warning system, the first turbulence data within the abnormal time interval is classified into turbulence levels, specifically: ;in, Indicates the level of turbulence. represents the turbulence intensity of the jth motion region, Indicates the mean value of the turbulence intensity within the abnormal time interval, represents the preset turbulence intensity threshold, P( ) represents the intensity function, Indicates the maximum turbulence intensity of all motion areas within the abnormal time interval, ( ) represents the logarithmic function, represents the error condition function.

[0010] On the other hand, the control algorithm is combined to adjust the operating parameters of the DC-DC converter in the target smart rail highway, including: According to the bump level, the target voltage of the DC-DC converter in the target smart rail highway is determined in combination with the bump level-target voltage mapping table; The target voltage and the output voltage of the current DC-DC converter are input into the PID control model, the error of each proportional control iteration is obtained, and the model parameters of the PID control model are adjusted according to the size of the error to obtain the final PID control model; The regulated operating parameters of the DC-DC converter are output based on the final PID control model.

[0011] On the other hand, based on the energy prediction software and combined with the different operating parameters of the DC-DC converter, the capacitor configuration strategy of the target smart rail highway is planned, including: According to the regulating operation parameters of the DC-DC converter, a corresponding regulating operation instruction is generated and executed on the DC-DC converter; If the operating parameters of the DC-DC converter are greater than or equal to the preset standard threshold range, the bidirectional chopper module in the DC-DC converter will boost the input voltage to the rated voltage and supply power to the capacitor of the target smart rail highway for energy storage. The storage energy of the capacitor is calculated based on the prediction model combined with the different operating parameters of the DC-DC converter. If the grid voltage fluctuation in the DC-DC converter operating parameters is abnormal or arcing occurs, the capacitor will be reduced in voltage based on the bidirectional chopper module in the DC-DC converter. According to the target voltage of the capacitor reduction, the speed and direction of the capacitor discharge are controlled by adjusting the DC-DC converter duty cycle.

[0012] According to the regulating operation parameters of the DC-DC converter, a corresponding regulating operation instruction is generated and executed on the DC-DC converter; If the grid voltage in the DC-DC converter operating parameters is less than the preset standard threshold, the bidirectional chopper module in the DC-DC converter will boost the input voltage to the rated voltage and supply power to the capacitor of the target smart rail highway for energy storage. The storage energy of the capacitor is calculated based on the prediction model combined with the different operating parameters of the DC-DC converter. If the grid voltage in the DC-DC converter operating parameters is greater than or equal to the preset standard threshold, the capacitor is reduced in voltage based on the bidirectional chopper module in the DC-DC converter. According to the target voltage of the capacitor reduction, the speed and direction of the capacitor discharge are controlled by adjusting the DC-DC converter duty cycle.

[0013] On the other hand, the target intelligent rail highway is divided into sections, including: According to all the bump levels involved in the operation of different target vehicles, they are respectively added to the target smart rail highway; The target intelligent rail highway is processed in sections according to the additional results.

[0014] Compared with the prior art, the present invention has the following beneficial effects: The present invention provides a protection and early warning method for a mobile charging smart rail highway, which is used to realize intelligent control and early warning by real-time monitoring of the bumpy conditions of target vehicles on the smart rail highway. First, the vehicle sensor obtains the original bumpy data, determines whether the early warning threshold is reached after preprocessing, and starts the early warning system. Next, the bumpy data is graded according to the early warning system, and the operating parameters of the DC-DC converter in the target smart rail highway are adjusted in combination with the control algorithm. Through the energy prediction software, the DC-DC converter parameters are optimized, and the capacitor configuration strategy is planned according to the bumpy level, and an early warning is provided at the same time. Finally, the smart rail highway is processed in sections, and sections with too high bumpy levels are marked as "caution sections", and the DC-DC converter is controlled to stop power output when the vehicle passes by, thereby improving driving safety and system stability. This process realizes intelligent dynamic adjustment and early warning functions, effectively improving the safety and energy efficiency management of the transportation system. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0016] Figure 1 It is a flow chart of a protection and early warning method for a mobile charging smart rail highway provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0017] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are 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 without creative work are within the scope of protection of the present invention.

[0018] Embodiment 1: like Figure 1 As shown, a protection and early warning method for a mobile charging smart rail highway provided by an embodiment of the present invention includes: Step 1: Use vehicle sensors to monitor the running bumps of the target vehicle on the target smart rail highway in real time to obtain the original bump data; Step 2: Preprocessing the original turbulence data to obtain first turbulence data, and if the first turbulence data reaches a preset warning threshold, starting the warning system; Step 3: Based on the early warning system, the first bump data is classified into bump levels, and the operating parameters of the DC-DC converter in the target smart rail highway are adjusted in combination with the control algorithm; Step 4: Based on the energy prediction software, the capacitor configuration strategy of the target smart rail highway is planned in combination with the different operating parameters of the DC-DC converter, and an early warning is issued according to the bump level; Step 5: Divide the target smart rail highway into sections, and mark the sections with bump levels greater than the preset safety level as caution sections. When the target vehicle runs to the caution section, the DC-DC converter is controlled to stop power output.

[0019] In this embodiment, the vehicle sensor is a device used to monitor the operating status of the target vehicle in real time, and detect information such as the vehicle's speed, acceleration, inclination, vibration, etc.

[0020] In this embodiment, the target vehicle refers to a vehicle traveling on the smart rail highway and whose operating status needs to be monitored.

[0021] In this embodiment, the target smart rail highway refers to a track or road designed specifically for intelligent and automated transportation systems, used for specific types of autonomous driving or intelligent vehicle operations. The smart rail highway is equipped with various sensors, communication equipment, energy management systems, etc.

[0022] In this embodiment, the running bump condition refers to the phenomenon of vehicle vibration, shaking or instability caused by factors such as road conditions, traffic environment or the vehicle's own motion state during the driving process of the target vehicle.

[0023] In this embodiment, the original bump data is the initial data recorded by real-time monitoring of the target vehicle by vehicle sensors when the target vehicle is traveling on the smart rail highway, including: acceleration, speed change, vibration intensity and other information.

[0024] In this embodiment, preprocessing refers to the process of cleaning, sorting and converting the original bump data collected from the vehicle sensor.

[0025] In this embodiment, the first bump data refers to bump information collected by vehicle sensors and pre-processed.

[0026] In this embodiment, the preset warning threshold refers to a standard value set by the system and is used to determine whether the first turbulence data exceeds a safe range.

[0027] In this embodiment, the early warning system is a system based on real-time data monitoring, analysis and response, and its main function is to detect potential risks or abnormal situations in advance and issue alarms in time to take corresponding measures.

[0028] In this embodiment, the bump level classification is based on the vehicle bump data monitored by the sensor, and the bump intensity is divided into different levels according to certain standards and algorithms, and each level represents the degree of bump during the operation of the vehicle.

[0029] In this embodiment, the control algorithm is a core algorithm for adjusting the DC-DC converter in the target smart rail highway according to the real-time monitored bumpy data and other key parameters (such as capacitor configuration, power output, etc.).

[0030] In this embodiment, the DC-DC converter is a power conversion device responsible for converting the input DC voltage into different voltages required by the target smart rail highway system to ensure the stability and efficiency of the power system under different operating conditions.

[0031] In this embodiment, the operating parameters refer to various settings or adjustment parameters for controlling the operation of the DC-DC converter.

[0032] In this embodiment, the energy forecasting software is a key tool or system used to predict and plan the energy demand and allocation of the system.

[0033] In this embodiment, the capacitor is an important component for balancing power fluctuations and ensuring stable operation of the system. It alleviates instantaneous fluctuations in the power system by storing electrical energy, thereby playing a role in stabilizing power supply.

[0034] In this embodiment, the configuration strategy refers to how to optimize and adjust the configuration and distribution of capacitors according to different operating conditions (such as the operating state of the vehicle, road bumps, and system power requirements).

[0035] In this embodiment, segmentation processing refers to dividing the target smart rail highway according to different bumpiness levels, and dividing the entire road into multiple sections.

[0036] In this embodiment, the preset safety level refers to a bump intensity threshold that is preset during the operation of the target smart rail highway based on the design standards of the road, the stability requirements of the vehicle, and the safety specifications of the power system.

[0037] In this embodiment, the section of attention refers to a section on the target smart rail highway that may have an impact on the vehicle and its power system due to the bump intensity exceeding the preset safety level.

[0038] The working principle and beneficial effects of the above technical solution are: by real-time monitoring of vehicle bumps, dynamically adjusting DC-DC converter parameters and issuing early warnings, optimizing capacitor configuration, and marking attention sections in sections, intelligent regulation and safety early warnings are achieved, thereby improving the operational stability and energy efficiency management of the smart rail highway.

[0039] Embodiment 2: On the basis of the above-mentioned embodiment 1, the running bumps of the target vehicle on the target smart rail highway are monitored in real time through the vehicle sensor to obtain the original bumps data, including: Get a detailed route based on the preset plan of the target smart rail highway; Select the target vehicle and select the corresponding vehicle sensor type according to the bump parameter type; Assign a unique first number to the installation position of the target vehicle, assign a unique second number to each vehicle sensor, and assign the vehicle sensor to the target vehicle according to a one-to-one matching relationship between the installation position and the vehicle sensor; Based on the detailed route, the target vehicle is started and driven from the starting point to the end point of the target smart rail highway; The bump parameter type sensor of the target vehicle is monitored in real time to obtain the original bump data.

[0040] In this embodiment, the preset plan refers to a detailed route map or plan layout map that is pre-planned and designed in the target smart rail highway system, which displays information such as the planning, layout, location, sections, intersections, obstacles, etc. of the highway.

[0041] In this embodiment, the first number refers to a unique identifier assigned to the installation location of the target vehicle.

[0042] In this embodiment, the second number refers to a unique identifier of the sensor of the target vehicle.

[0043] In this embodiment, the detailed route refers to a specific path for the target vehicle to travel designed based on a preset plan of the target smart rail highway.

[0044] In this embodiment, the bump parameter type refers to different types of bump data that can quantify and describe the vehicle's driving state during the monitoring of the vehicle's driving, and is related to factors such as vehicle body vibration, stability, and road conditions.

[0045] The working principle and beneficial effects of the above technical solution are: by selecting suitable sensors and configuring a unique number for each target vehicle, the vehicle bump data is monitored based on a detailed route, thereby achieving accurate bump parameter collection, providing reliable data support for subsequent intelligent adjustment and safety warning, and improving the stability and safety of the transportation system.

[0046] Embodiment 3: On the basis of the above-mentioned embodiment 2, the original jolting data is preprocessed to obtain the first jolting data, including: If the original turbulence data of any turbulence parameter type is of video type, then the original video is processed frame by frame to obtain an original video sequence; Obtain any original image in the original video sequence, eliminate interference jitter factors from the original image according to a stability algorithm to obtain a standard image, and use an edge detection algorithm to extract edge points in the standard image to obtain an edge image; The original video sequence is processed to generate an edge image sequence, any edge image in the edge image sequence is selected as a central image, an edge image to the right of a time node of the central image is selected as a comparison image, the central image and the comparison image are projected into a standard coordinate system, and the motion state of the target vehicle is evaluated; Get any edge pixel point of the central image and compare and analyze it with the corresponding edge pixel point in the comparison image, and satisfy the following formula, and then get the motion vector of the i-th pixel point: : ; Where n means there are n edge pixels in the center image. Indicates the convolution of the edge pixel of the i-th center image in the x direction. It means to perform convolution in the x direction on the edge pixel of the i-th image. Indicates the convolution of the edge pixel of the i-th center image in the y direction. Indicates the convolution of the edge pixel of the i-th contrast image in the y direction. Indicates the motion vector direction of the i-th pixel in the center image, Represents the motion vector modulus of the i-th pixel in the center image, Indicates the time interval from the center image to the contrast image; By calculating the motion vector of each pixel in any image of the edge image sequence, the pixels are clustered into different areas, including moving areas and static areas, using a clustering algorithm; Performing time series normalization processing on the original bump data of other bump parameter types according to the time nodes of the edge image sequence to obtain normalized bump data; The first turbulence data includes an edge image sequence and motion vectors corresponding to the edge images and standardized turbulence data of other turbulence parameter types.

[0047] In this embodiment, frame-by-frame processing refers to a process of independently analyzing and processing each frame of an image in a video.

[0048] In this embodiment, the original video sequence refers to a series of continuous image frames extracted from the original video file.

[0049] In this embodiment, the stability algorithm is an algorithm for eliminating jitter interference in an image, ensuring that the image is stable and can accurately reflect the motion state of the scene.

[0050] In this embodiment, the interfering jitter factor refers to unintentional movement or change caused by equipment or environmental factors during the video shooting process, which affects the stability and clarity of the video image.

[0051] In this embodiment, the standard image refers to an image that has been processed by a stability algorithm to eliminate interference jitter factors.

[0052] In this embodiment, the edge detection algorithm is an image processing technology used to identify areas in an image where brightness changes significantly, that is, edges in the image, representing the outline of an object.

[0053] In this embodiment, edge points refer to pixel points in an image where features such as brightness, color, and texture change significantly.

[0054] In this embodiment, the center image refers to a specific image selected from the edge image sequence as a benchmark image or reference image for analysis.

[0055] In this embodiment, the comparison image refers to a frame of image selected from the edge image sequence to be compared with the current “center image”.

[0056] In this embodiment, the standard coordinate system is a coordinate system used to unify and normalize position and motion analysis in image processing, the abscissa (x) represents the width of the image, and the ordinate (y) represents the height of the image.

[0057] In this embodiment, the motion state refers to the dynamic behavior of the target vehicle in the video sequence, especially the characteristics of its position, speed, acceleration, etc. that change over time.

[0058] In this embodiment, the motion vector is a vector that describes the displacement of each pixel or pixel block in the image between two frames, and reflects the movement of the object or area in the image.

[0059] In this embodiment, the modulus refers to the size of the motion vector, that is, the displacement distance of each pixel point in the time series.

[0060] In this embodiment, the motion region refers to a region where motion changes can be detected after motion vector analysis in the image processing of the video sequence.

[0061] In this embodiment, the static area refers to an area where the motion vector is close to zero when processing an edge image sequence.

[0062] In this embodiment, time series standardization processing refers to converting a set of data so that the data has a unified standard in the time dimension.

[0063] In this embodiment, the standardized bump data refers to data obtained by performing time series standardization processing on other types of bump parameter data, aiming to make different types of bump data have a unified standard scale.

[0064] The working principle and beneficial effects of the above technical solution are: through frame-by-frame video processing, stability algorithm to eliminate interference, edge detection to extract motion information, and combined with motion vector analysis and clustering, the vehicle motion state is accurately evaluated. Different bump data are integrated through time series standardization, which improves the accuracy and safety of traffic monitoring.

[0065] Embodiment 4: Based on the above embodiment 3, if the first turbulence data reaches a preset warning threshold, starting the warning system includes: Obtaining standardized turbulence data of any turbulence parameter type, and if the standardized turbulence data reaches a preset warning threshold, obtaining an abnormal time interval corresponding to the occurrence of abnormal turbulence; Acquire the corresponding edge image group in the edge image sequence according to the abnormal time interval, analyze the modulus and direction of the motion vector field of all edge images, identify the motion area and the static area, and determine the bump intensity of any motion area in the edge image group based on the motion intensity and direction of the motion area; If the intensity of turbulence reaches the preset warning threshold, the warning system will be activated.

[0066] In this embodiment, the preset warning threshold refers to a numerical threshold preset by the system when analyzing turbulence data (eg, an abnormal time interval obtained by normalizing turbulence data).

[0067] In this embodiment, the abnormal time interval refers to a period of time during which the standardized turbulence data monitored by the system exceeds a preset threshold, thereby triggering the recognition of an abnormal situation.

[0068] In this embodiment, the edge image group refers to a plurality of image frames containing edge points extracted within a certain time period.

[0069] In this embodiment, the motion intensity refers to a measure of the degree and intensity of motion by analyzing the motion vector field of the object in the edge image.

[0070] In this embodiment, the jitter intensity is a quantitative indicator for measuring the degree of jitter caused by movement or vibration of an image or object within a certain period of time.

[0071] The working principle and beneficial effects of the above technical solution are: monitoring anomalies through standardized bump data, identifying the motion area and bump intensity through edge image motion vector analysis, and activating the early warning system if the threshold is exceeded, thereby improving the real-time monitoring and response capabilities to vehicle bump anomalies.

[0072] Embodiment 5: On the basis of the above-mentioned embodiment 4, based on the early warning system, the first turbulence data is classified into turbulence levels, including: Based on the early warning system, the first turbulence data within the abnormal time interval is classified into turbulence levels, specifically: ;in, Indicates the level of turbulence. represents the turbulence intensity of the jth motion region, Indicates the mean value of the turbulence intensity within the abnormal time interval, represents the preset turbulence intensity threshold, P( ) represents the intensity function, Indicates the maximum turbulence intensity of all motion areas within the abnormal time interval, ( ) represents the logarithmic function, represents the error condition function.

[0073] In this embodiment, the preset turbulence intensity threshold is a key reference value used to determine whether abnormal turbulence occurs in the system.

[0074] The working principle and beneficial effects of the above technical solution are: by performing mean and maximum value analysis on the turbulence intensity within the abnormal time interval, and combining the intensity function with the error condition, the turbulence level is accurately divided, which effectively improves the monitoring accuracy of the turbulence degree and provides a reliable basis for automatic warning and response.

[0075] Embodiment 6: On the basis of the above-mentioned embodiment 5, the operating parameters of the DC-DC converter in the target smart rail highway are adjusted in combination with the control algorithm, including: According to the bump level, the target voltage of the DC-DC converter in the target smart rail highway is determined in combination with the bump level-target voltage mapping table; The target voltage and the output voltage of the current DC-DC converter are input into the PID control model, the error of each proportional control iteration is obtained, and the model parameters of the PID control model are adjusted according to the size of the error to obtain the final PID control model; The regulated operating parameters of the DC-DC converter are output based on the final PID control model.

[0076] In this embodiment, the bump level-target voltage mapping table is a mapping tool that defines the relationship between the bump level and the target voltage.

[0077] In this embodiment, the output voltage refers to the actual output voltage of the DC-DC converter.

[0078] In this embodiment, the target voltage is a voltage value that depends on the current turbulence level and is determined by a turbulence level-target voltage mapping table.

[0079] In this embodiment, the model parameters refer to three basic parameters that affect the output of the PID controller, namely, proportional gain, integral gain, and differential gain.

[0080] In this embodiment, the PID control model is a commonly used feedback control algorithm, which is used to automatically adjust the output of the system and control it to be as close to the target value as possible.

[0081] In this embodiment, the final PID control model is an optimal model obtained by automatically optimizing the operating parameters of the DC-DC converter by processing the error between the target voltage and the current voltage during the adjustment process.

[0082] In this embodiment, the adjusted operating parameters include: duty cycle, switching frequency, dead time, etc.

[0083] The working principle and beneficial effects of the above technical solution are: by adjusting the target voltage according to the bump level, dynamically optimizing the control parameters in combination with the PID control model, precise adjustment of the DC-DC converter is achieved, thereby improving the voltage stability and system response speed, and ensuring the stability of power supply during the operation of the smart rail highway.

[0084] Embodiment 7: On the basis of the above-mentioned embodiment 6, based on the energy prediction software and in combination with different operating parameters of the DC-DC converter, a configuration strategy for the capacitor of the target smart rail highway is planned, including: According to the regulating operation parameters of the DC-DC converter, a corresponding regulating operation instruction is generated and executed on the DC-DC converter; If the operating parameters of the DC-DC converter are greater than or equal to the preset standard threshold range, the bidirectional chopper module in the DC-DC converter will boost the input voltage to the rated voltage and supply power to the capacitor of the target smart rail highway for energy storage. The storage energy of the capacitor is calculated based on the prediction model combined with the different operating parameters of the DC-DC converter. If the grid voltage fluctuation in the DC-DC converter operating parameters is abnormal or arcing occurs, the capacitor will be reduced in voltage based on the bidirectional chopper module in the DC-DC converter. According to the target voltage of the capacitor reduction, the speed and direction of the capacitor discharge are controlled by adjusting the DC-DC converter duty cycle.

[0085] According to the regulating operation parameters of the DC-DC converter, a corresponding regulating operation instruction is generated and executed on the DC-DC converter; If the grid voltage in the DC-DC converter operating parameters is less than the preset standard threshold, the bidirectional chopper module in the DC-DC converter will boost the input voltage to the rated voltage and supply power to the capacitor of the target smart rail highway for energy storage. The storage energy of the capacitor is calculated based on the prediction model combined with the different operating parameters of the DC-DC converter. If the grid voltage in the DC-DC converter operating parameters is greater than or equal to the preset standard threshold, the capacitor is reduced in voltage based on the bidirectional chopper module in the DC-DC converter. According to the target voltage of the capacitor reduction, the speed and direction of the capacitor discharge are controlled by adjusting the DC-DC converter duty cycle.

[0086] In this embodiment, the grid voltage refers to the voltage of the power grid in the power system.

[0087] In this embodiment, the preset standard threshold value range is a set voltage value range used to determine the step-up or step-down operation of the DC-DC converter.

[0088] In this embodiment, the bidirectional chopper module is a power electronic module that can achieve voltage step-up and step-down, supports bidirectional energy flow, and can either step up the input voltage to a target voltage or step down the input voltage to a desired voltage.

[0089] In this embodiment, the rated voltage refers to a standard voltage value expected during the design and operation of the power equipment system in a specific application.

[0090] In this embodiment, the prediction model refers to a mathematical model used to predict the change of capacitor storage energy according to various input parameters (such as grid voltage, load demand, time, temperature, converter working state, etc.).

[0091] In this embodiment, reducing the power output of DCDC can protect the pantograph and the contact network from excessive impact and prevent the pantograph from jumping, falling off, and arcing abnormalities.

[0092] In this embodiment, when the grid voltage is normal, the bidirectional chopper module in the DCDC boosts the input voltage to DC2000V to supply power to the capacitor C1 for energy storage; when the grid voltage arcs and the power is lost, the bidirectional chopper module chops and reduces the energy of the capacitor C1 to 1200V / 150kW / 50ms to supply power to the isolation module. When the grid voltage is within the normal range, the DC-DC uses a bidirectional chopper to boost the voltage provided by the grid to DC2000V and stores it in the large-capacity capacitor C1, so that a short but high-power emergency power supply can be provided when the power supply is interrupted; In this embodiment, when the grid voltage fluctuates violently or arcing occurs, causing the pantograph to be disconnected and power off, capacitor C1 is an emergency energy reserve. The bidirectional chopper will quickly reduce the energy on the capacitor to DC1200V, with a power of up to 150kW, and power the isolation module (such as motor controller, charger, etc.) in the form of short pulses (50ms). This design allows the operation of key systems to be maintained in a short period of time, such as ensuring that basic auxiliary equipment or on-board information systems continue to work until the power grid is restored or the vehicle can enter other emergency modes.

[0093] In this embodiment, the circuit is quickly cut off under the condition of instantaneous voltage arcing or high voltage power failure to prevent further energy loss and equipment damage, and an additional pantograph-catenary monitoring system (such as a pantograph-catenary pressure sensor or a video monitoring system) is introduced to monitor the contact status of the pantograph and the contact network in real time, quickly identify arcing problems and take timely early warnings.

[0094] The working principle and beneficial effects of the above technical solution are: by adjusting the operating parameters of the DC-DC converter, the input voltage is boosted and the capacitor energy is stored or the voltage is reduced and discharged, and the capacitor energy storage and release are optimized in combination with the prediction model, thereby improving the energy management efficiency of the smart rail highway system and ensuring the stability and intelligent regulation of the power supply.

[0095] Embodiment 8: On the basis of the above-mentioned embodiment 7, the target intelligent rail highway is segmented, including: According to all the bump levels involved in the operation of different target vehicles, they are respectively added to the target smart rail highway; The target intelligent rail highway is processed in sections according to the additional results.

[0096] The working principle and beneficial effect of the above technical solution are: according to the different bump levels during the operation of the target vehicle, segmented processing is performed to optimize the overall adjustment and response of the smart rail highway. By accurately dividing the bump levels, the adaptability and comfort of the highway are improved, ensuring the stability and safety during operation.

[0097] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A protection and early warning method for a mobile charging smart rail highway, characterized in that: include: Step 1: Use vehicle sensors to monitor the running bumps of the target vehicle on the target smart rail highway in real time to obtain the original bump data; Step 2: Preprocessing the original turbulence data to obtain first turbulence data, and if the first turbulence data reaches a preset warning threshold, starting the warning system; Step 3: Based on the early warning system, the first bump data is classified into bump levels, and the operating parameters of the DC-DC converter in the target smart rail highway are adjusted in combination with the control algorithm; Step 4: Based on the energy prediction software, the capacitor configuration strategy of the target smart rail highway is planned in combination with the different operating parameters of the DC-DC converter, and an early warning is issued according to the bump level; Step 5: Divide the target smart rail highway into sections, and mark the sections with bump levels greater than the preset safety level as caution sections. When the target vehicle runs to the caution section, the DC-DC converter is controlled to stop power output.

2. A protection and early warning method for a mobile charging smart rail highway according to claim 1, characterized in that: Through vehicle sensors, the target vehicle's running bumps on the target smart rail highway are monitored in real time to obtain the original bump data, including: Get a detailed route based on the preset plan of the target smart rail highway; Select the target vehicle and select the corresponding vehicle sensor type according to the bump parameter type; Assign a unique first number to the installation position of the target vehicle, assign a unique second number to each vehicle sensor, and assign the vehicle sensor to the target vehicle according to a one-to-one matching relationship between the installation position and the vehicle sensor; Based on the detailed route, the target vehicle is started and driven from the starting point to the end point of the target smart rail highway; The bump parameter type sensor of the target vehicle is monitored in real time to obtain the original bump data.

3. A protection and early warning method for a mobile charging smart rail highway according to claim 2, characterized in that: The original turbulence data is preprocessed to obtain the first turbulence data, including: If the original turbulence data of any turbulence parameter type is of video type, then the original video is processed frame by frame to obtain an original video sequence; Obtain any original image in the original video sequence, eliminate interference jitter factors from the original image according to a stability algorithm to obtain a standard image, and use an edge detection algorithm to extract edge points in the standard image to obtain an edge image; The original video sequence is processed to generate an edge image sequence, any edge image in the edge image sequence is selected as a central image, an edge image to the right of a time node of the central image is selected as a comparison image, the central image and the comparison image are projected into a standard coordinate system, and the motion state of the target vehicle is evaluated; Get any edge pixel point of the central image and compare and analyze it with the corresponding edge pixel point in the comparison image, and satisfy the following formula, and then get the motion vector of the i-th pixel point: : ; Where n means there are n edge pixels in the center image. Indicates the convolution of the edge pixel of the i-th center image in the x direction. It means to perform convolution in the x direction on the edge pixel of the i-th image. Indicates the convolution of the edge pixel of the i-th center image in the y direction. Indicates the convolution of the edge pixel of the i-th contrast image in the y direction. Indicates the motion vector direction of the i-th pixel in the center image, Represents the motion vector modulus of the i-th pixel in the center image, Indicates the time interval from the center image to the contrast image; By calculating the motion vector of each pixel in any image of the edge image sequence, the pixels are clustered into different areas, including moving areas and static areas, using a clustering algorithm; Performing time series normalization processing on the original bump data of other bump parameter types according to the time nodes of the edge image sequence to obtain normalized bump data; The first turbulence data includes an edge image sequence and motion vectors corresponding to the edge images and standardized turbulence data of other turbulence parameter types.

4. A protection and early warning method for a mobile charging smart rail highway according to claim 3, characterized in that: If the first turbulence data reaches a preset warning threshold, the warning system is activated, including: Obtaining standardized turbulence data of any turbulence parameter type, and if the standardized turbulence data reaches a preset warning threshold, obtaining an abnormal time interval corresponding to the occurrence of abnormal turbulence; Acquire the corresponding edge image group in the edge image sequence according to the abnormal time interval, analyze the modulus and direction of the motion vector field of all edge images, identify the motion area and the static area, and determine the bump intensity of any motion area in the edge image group based on the motion intensity and direction of the motion area; If the intensity of turbulence reaches the preset warning threshold, the warning system will be activated.

5. A protection and early warning method for a mobile charging smart rail highway according to claim 4, characterized in that: Based on the early warning system, the first turbulence data is classified into turbulence levels, including: Based on the early warning system, the first turbulence data within the abnormal time interval is classified into turbulence levels, specifically: ;in, Indicates the level of turbulence. represents the turbulence intensity of the jth motion region, Indicates the mean value of the turbulence intensity within the abnormal time interval, represents the preset turbulence intensity threshold, P( ) represents the intensity function, Indicates the maximum turbulence intensity of all motion areas within the abnormal time interval, ( ) represents the logarithmic function, represents the error condition function.

6. A protection and early warning method for a mobile charging smart rail highway according to claim 5, characterized in that: Combined with the control algorithm, the operating parameters of the DC-DC converter in the target smart rail highway are adjusted, including: According to the bump level, the target voltage of the DC-DC converter in the target smart rail highway is determined in combination with the bump level-target voltage mapping table; The target voltage and the output voltage of the current DC-DC converter are input into the PID control model, the error of each proportional control iteration is obtained, and the model parameters of the PID control model are adjusted according to the size of the error to obtain the final PID control model; The regulated operating parameters of the DC-DC converter are output based on the final PID control model.

7. A protection and early warning method for a mobile charging smart rail highway according to claim 6, characterized in that: Based on the energy prediction software and combined with the different operating parameters of the DC-DC converter, the capacitor configuration strategy for the target smart rail highway is planned, including: According to the regulating operation parameters of the DC-DC converter, a corresponding regulating operation instruction is generated and executed on the DC-DC converter; Among them, if the grid voltage in the DC-DC converter operating parameters is greater than or equal to the preset standard threshold range and is less than the preset standard threshold, the bidirectional chopper module in the DC-DC converter will boost the input voltage to the rated voltage and supply power to the capacitor of the target smart rail highway for energy storage, and calculate the storage energy of the capacitor based on the prediction model combined with different operating parameters of the DC-DC converter; If the grid voltage in the DC-DC converter operating parameters is greater than or equal to the preset standard threshold value, fluctuates abnormally, or arcs, trips, or loses power, the capacitor is reduced in voltage based on the bidirectional chopper module in the DC-DC converter. According to the target voltage of the capacitor reduction, the speed and direction of the capacitor discharge are controlled by adjusting the DC-DC converter duty cycle.

8. A protection and early warning method for a mobile charging smart rail highway according to claim 7, characterized in that: The target intelligent rail highway is divided into sections, including: According to all the bump levels involved in the operation of different target vehicles, they are respectively added to the target smart rail highway; The target intelligent rail highway is processed in sections according to the additional results.

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