Marshalling railway freight equipment energy consumption optimization method and system based on data fusion

Abstract

The invention discloses a marshalling railway freight equipment energy consumption optimization method and system based on data fusion, and relates to the technical field of rail transit. The system comprises a dynamic data acquisition module used for acquiring real-time data in the operation process of railway freight equipment; the core calculation module is used for calculating the real-time parameters by using a multi-coupling effect model to obtain the atmospheric resistance energy consumption of the railway freight equipment; the edge calculation layer is used for constructing a dynamic wind attack angle correction model according to the operation state of the railway freight equipment and correcting the atmospheric resistance energy consumption calculation process by using the dynamic wind attack angle correction model, and the intelligent analysis layer is used for optimizing the advancing strategy of the railway freight equipment according to the atmospheric resistance energy consumption; and the feedback control module is used for feeding back and controlling the railway freight equipment in real time according to the optimized traveling strategy of the railway freight equipment. According to the invention, accurate modeling, optimization and safety control of atmospheric resistance energy consumption in a dynamic environment can be realized.

Description

Technical Field

[0001] The present invention relates to the field of rail transit technology, and in particular to a method and system for optimizing energy consumption of marshaling railway freight equipment based on data fusion. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] With the continuous growth of railway freight volume, the energy consumption of railway freight equipment has received increasing attention. Atmospheric drag is one of the main sources of energy consumption in train operation, especially in high-speed or long-distance transportation. By accurately calculating the atmospheric drag energy consumption in the marshaling state, energy utilization can be optimized: reasonable planning of traction power configuration, reducing fuel or electricity consumption. It can reduce transportation costs: energy saving is directly converted into operational economy, which is particularly important for railway systems with large freight volumes. It can also support the application of new energy technologies: provide data support for the battery capacity design of electric or hybrid trucks.

[0004] The calculation of atmospheric resistance of traditional railway freight equipment mostly relies on static models, which do not fully consider the impact of dynamic environment (such as sudden changes in wind speed and wind direction), synergy effect of marshaling (such as the effect of wake flow of various types of railway freight cars on the atmospheric resistance of the following car) and coupling of infrastructure (such as transient wind pressure impact in tunnels), resulting in insufficient release of energy-saving potential. Moreover, static parameters not only have poor real-time performance, but also cannot adapt to changes in dynamic wind fields and track curvature, resulting in large calculation errors of atmospheric resistance and its energy consumption. In addition, the existing technology lacks a quantitative prediction mechanism for the risk of derailment caused by the superposition of crosswinds and curved sections.

[0005] In summary, how to realize the real-time and accurate calculation of the atmospheric resistance energy consumption of railway freight equipment based on multiple influencing factors, and to predict risks and improve and optimize according to the calculated energy consumption, has become a technical problem that needs to be urgently solved in the existing technology. Summary of the invention

[0006] In view of the shortcomings of the prior art, the purpose of the present invention is to provide a method and system for optimizing energy consumption of marshaled railway freight equipment based on data fusion, which can realize accurate modeling, optimization and safe control of atmospheric resistance energy consumption in a dynamic environment.

[0007] In order to achieve the above object, the present invention is implemented through the following technical solutions: The first aspect of the present invention provides a system for optimizing energy consumption of marshaling railway freight equipment based on data fusion, comprising: Dynamic data acquisition module, used to obtain real-time data during the operation of railway freight equipment; The core calculation module is used to calculate real-time parameters using the multi-coupling effect model to obtain the atmospheric resistance energy consumption of railway freight equipment. Among them, the multi-coupling effect model is constructed based on the formation effect, wind direction effect, and tunnel effect; The data processing module includes an edge computing layer and an intelligent analysis layer. The edge computing layer is used to construct a dynamic wind attack angle correction model based on the operating state of railway freight equipment, and use the dynamic wind attack angle correction model to correct the atmospheric resistance energy consumption calculation process. The intelligent analysis layer is used to optimize the travel strategy of railway freight equipment based on the atmospheric resistance energy consumption; The feedback control module is used to perform real-time feedback and control on railway freight equipment according to the optimized travel strategy of railway freight equipment.

[0008] Furthermore, in the core calculation module, the formation effect is the shielding effect of the front vehicle's wake on the rear vehicle brought about by the formation status of railway freight equipment, the wind direction effect is the atmospheric resistance change effect brought about by the relative wind attack angle during the operation of railway freight equipment, and the tunnel effect is the transient pressure wave resistance effect brought about by the tunnel environment.

[0009] Furthermore, in the edge computing layer, it also includes calculating the equivalent synthetic wind speed by combining vehicle speed, natural wind speed and direction, and tunnel ventilation parameters; the equivalent synthetic wind speed fuses the wind speed vector, vehicle heading angle, and track curvature to construct a dynamic wind attack angle correction model, and uses the dynamic wind attack angle correction model to correct the atmospheric resistance energy consumption calculation process by real-time correcting the atmospheric resistance coefficient.

[0010] Furthermore, the intelligent analysis layer also includes a safety warning layer, which is used to predict the derailment risk probability based on the dynamic critical overturning coefficient algorithm, fusing the wind load spectrum, track curvature, and vehicle center of mass offset; perform anomaly detection in real time, extract the characteristics of the real-time atmospheric resistance energy consumption, and warn of abnormal working conditions according to the comparison result of the real-time atmospheric resistance energy consumption and the dynamic threshold.

[0011] Furthermore, it also includes a static database module, including a track GIS database and a vehicle characteristic database, which are used to store the track and vehicle static parameters of railway freight equipment.

[0012] Furthermore, it also includes an environment perception and communication module, which uses distributed meteorological monitoring stations, tunnel entrance wind speed monitoring devices, and track status monitoring sensors deployed along the line to obtain environment perception information, and uses 5G or LoRa hybrid networking to achieve low-latency data transmission.

[0013] Even further, the feedback control module provides the driver with the real-time atmospheric resistance change trend and the optimized travel strategy of railway freight equipment through the environment perception and communication module.

[0014] The second aspect of the present invention provides an energy consumption optimization method for grouped railway freight equipment based on data fusion, including the following steps: Obtain real-time data during the operation of railway freight equipment; Use a multi-coupling effect model to calculate real-time parameters to obtain the atmospheric resistance energy consumption of railway freight equipment. Among them, a multi-coupling effect model is constructed according to the formation effect, wind direction effect, and tunnel effect; Construct a dynamic wind attack angle correction model according to the operating state of railway freight equipment, and use the dynamic wind attack angle correction model to correct the calculation process of atmospheric resistance energy consumption; Optimize the traveling strategy of railway freight equipment according to the atmospheric resistance energy consumption; Perform real-time feedback and control on railway freight equipment according to the optimized traveling strategy of railway freight equipment.

[0015] The third aspect of the present invention provides a medium on which a program is stored, and when the program is executed by a processor, it implements the steps in the energy consumption optimization method for grouped railway freight equipment based on data fusion as described in the second aspect of the present invention.

[0016] The fourth aspect of the present invention provides a device, including a memory, a processor, and a program stored on the memory and executable on the processor. When the processor executes the program, it implements the steps in the energy consumption optimization method for grouped railway freight equipment based on data fusion as described in the second aspect of the present invention.

[0017] The above one or more technical solutions have the following beneficial effects: The present invention discloses an energy consumption optimization method and system for grouped railway freight equipment based on data fusion, and proposes a brand-new atmospheric resistance energy consumption calculation model, which considers various influencing factors, such as the shape, size, formation mode, operating speed, wind direction and wind speed of the equipment. This model can more accurately reflect the atmospheric resistance energy consumption level of railway freight equipment in the formation state, and constructs a dynamic wind attack angle correction model through edge computing to correct the construction of the dynamic wind attack angle correction model to obtain a more accurate real-time energy consumption calculation result of atmospheric resistance. According to the real-time energy consumption calculation of atmospheric resistance, railway freight enterprises can formulate more scientific energy conservation and emission reduction measures, reduce operating costs, and improve economic benefits.

[0018] The present invention proposes a brand-new evaluation model for the atmospheric resistance energy consumption level, which combines fluid mechanics, multi-body dynamics and the actual operating characteristics of railway freight equipment. Based on this model, it can more accurately evaluate the atmospheric resistance energy consumption level of railway freight equipment in each formation state, which is convenient for optimizing the formation form of railway wagons.

[0019] Through efficient data collection and processing, the present invention can obtain the operation data and environmental parameters of railway freight equipment in the formation state in real time. These data include the running speed, position information, wind direction and speed of the equipment. Through the efficient processing and analysis of the system, the atmospheric resistance energy consumption level can be accurately obtained. Through real-time monitoring and feedback, the present invention can monitor the atmospheric resistance energy consumption of railway freight equipment in real time and feed the data back to relevant personnel or systems so as to take timely measures for optimization and adjustment.

[0020] Advantages of additional aspects of the present invention will be partly given in the following description, partly will become obvious from the following description, or will be understood through the practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The accompanying drawings forming a part of this specification are used to provide a further understanding of the present invention. The schematic embodiments and descriptions thereof of the present invention are used to explain the present invention and do not constitute an improper limitation to the present invention.

[0022] Figure 1 is the architecture diagram of the formation railway freight equipment energy consumption optimization system based on data fusion in Embodiment 1 of the present invention; Figure 2 is the schematic diagram of the dynamic wind attack angle correction model in Embodiment 1 of the present invention; Figure 3 is the calculation flow chart of the multi-coupling effect model in Embodiment 1 of the present invention; Figure 4 is the logic diagram of the dynamic critical tipping coefficient risk warning algorithm in Embodiment 1 of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0023] It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which the present invention belongs.

[0024] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components and / or combinations thereof; Embodiment 1: Embodiment 1 of the present invention provides a formation railway freight equipment energy consumption optimization system based on data fusion, as Figure 1 shown, including: The dynamic data acquisition module is used to obtain real-time data during the operation of railway freight equipment. The real-time data includes environmental data, vehicle operation data, and static parameters. In this embodiment, the railway freight equipment is a railway freight train.

[0025] The dynamic data acquisition module includes an integrated nine-axis IMU (collecting three-dimensional acceleration and angular velocity), a GNSS or INS combined navigation module, a barometric pressure sensor, a temperature sensor, and an array of wind speed and direction sensors. Among them, the nine-axis IMU is used to collect information such as three-dimensional acceleration and angular velocity, the GNSS or INS combined navigation module is used to collect position information, the barometric pressure sensor is used to collect barometric pressure information, the temperature sensor is used to collect temperature information, and the array of wind speed and direction sensors is installed at positions such as the front of the vehicle, the rear of the vehicle, and the roof of the vehicle to collect information such as wind direction and wind speed. The core calculation module is used to calculate the real-time parameters using a multi-coupling effect model to obtain the atmospheric resistance energy consumption of the railway freight equipment.

[0026] In this embodiment, a multi-coupling effect model is constructed based on the formation effect, the wind direction effect, and the tunnel effect. The formation effect is the shielding effect of the wake of the front vehicle on the rear vehicle brought about by the formation condition of the railway freight equipment. Considering the shielding effect of the wake of the front vehicle on the rear vehicle, an atmospheric resistance reduction coefficient is introduced. The wind direction effect is the atmospheric resistance change effect brought about by the relative wind attack angle during the operation of the railway freight equipment. The relative wind attack angle is calculated using the dynamic coordinate system method, and a lookup table of the atmospheric resistance coefficient for mixed formations is established. The tunnel effect is the transient pressure wave resistance effect brought about by the tunnel environment, and a pressure wave resistance calculation model is established based on the one-dimensional unsteady compressible flow theory.

[0027] The multi-coupling effect model is as Figure 3 shown. The input parameters include dynamic data and static data. The dynamic data includes wind speed, wind direction, acceleration, vehicle speed, position, and heading angle. The static data includes vehicle length, vehicle spacing, formation parameters, line map, and tunnel parameters. First, data preprocessing is performed according to the input parameters, including real-time calculation of the vehicle operation speed, real-time calculation of the vehicle position on the track, judgment of the tunnel entrance and exit flag bits, and calculation of the tunnel blockage ratio. Then, formation effect coupling calculation, tunnel effect coupling calculation, wind direction effect coupling calculation, and non-linear coupling energy consumption model calculation are performed. Finally, the real-time atmospheric resistance value of the formation and the aerodynamic energy consumption of the formation implementation are output.

[0028] In a specific embodiment, when railway freight equipment operates on a line, there are tunnel operation and non-tunnel operation conditions, and the atmospheric resistance energy consumption is different in these two cases. Therefore, in order to facilitate the study of the atmospheric resistance energy consumption during the operation of railway freight equipment under formation conditions, a rigid body dynamics model of railway freight equipment based on multiple mass points is first established, assuming that the operation speed of the railway freight equipment is v. For the sake of easy explanation, the first railway freight equipment of a railway freight train is numbered 1, and the railway freight equipment is sequentially numbered in the reverse operation direction. The railway freight equipment j is the railway freight equipment numbered j.

[0029] For the formation reduction coefficient , initially, the parameter data (library) can be obtained through wind tunnel tests combined with flow field simulations. Later, the atmospheric resistance energy consumption characteristic data (library) under different climate zones and different formation forms are obtained through collection and accumulation, supporting the artificial intelligence AI to make more intelligent formation reasoning decisions.

[0030] In this embodiment, since standard couplers are used for railway freight cars, a fixed spacing is generally adopted between the railway freight carriages in a formation. In a mixed formation of railway freight, the formation reduction coefficient of a certain vehicle j is used to quantify the reduction effect of the wake of the front vehicle type on the aerodynamic resistance of this vehicle. Its calculation needs to comprehensively consider the influence of vehicle type combination, formation length, vehicle position and tail effect. The specific method for coupling calculation of the formation effect is as follows: (1). Among them, is the formation reduction coefficient of a certain vehicle j, is the reference reduction coefficient of the front vehicle, is the vehicle type combination correction factor, is the formation length correction factor, is the vehicle position attenuation factor, is the tail correction factor.

[0031] In this embodiment, the formation reduction coefficient of the j-th vehicle is determined by the above five parts. Specifically: 1. The reference reduction coefficient of the front vehicle ( ): The inherent reduction ability of the front vehicle j-1, which can be obtained through wind tunnel tests or CFD simulations. Typical values: tank car 0.25, gondola car 0.35, flat car (standard container) 0.22, boxcar 0.30.

[0032] 2. The vehicle type combination correction factor ( ): Reflects the synergistic effect of the front vehicle - rear vehicle combination, which can be obtained through wind tunnel tests or CFD simulations. Typical values: tank car → gondola car: 1.2, flat car → boxcar: 0.9, gondola car → flat car: 0.7.

[0033] 3. Formation length correction factor ( ): The attenuation of the wake superposition due to the total number of carriages in the formation, \(f_{length}=1 - 0.008(N - 1)\), with a minimum value of 0.4; 4. Vehicle position attenuation factor ( ): The influence of the vehicle position on the wake energy. The more rearward the position, the more the energy of the leading vehicle's wake attenuates after passing through multiple carriages. The calculation method ; 5. Tail correction factor ( ): The aerodynamic drag of the last vehicle increases due to the tail vortex, and a compensation reduction coefficient is required. It takes the value of 0.85 when vehicle \(j\) is at the tail, and 1.0 otherwise.

[0034] It can be obtained from the above formula that for long formations (\(N>30\)), low - resistance vehicle types (such as tank cars) should be preferentially arranged at the tail to reduce the impact. And avoid having gondolas as the tail vehicle to reduce the vortex resistance.

[0035] Considering the part of the atmospheric resistance during the operation of the vehicle on the track, for a single railway freight equipment \(j\), the energy consumption due to atmospheric resistance during track operation is: (2).

[0036] In the formula: is the total energy consumption due to atmospheric resistance during the track operation of railway freight equipment \(j\), with the unit of J; is the basic energy consumption due to atmospheric resistance during the track operation of railway freight equipment \(j\), with the unit of J; is the additional energy consumption due to atmospheric resistance during the operation of railway freight equipment \(j\) in the tunnel, that is, the energy consumption generated only when operating in the tunnel, with the unit of J. is the effective coefficient of the additional energy consumption due to atmospheric resistance. It takes the value of 1 when railway freight equipment \(j\) is in the tunnel section after trajectory calculation, and 0 otherwise.

[0037] Considering all railway freight equipment on the freight train, the total energy consumption due to atmospheric resistance during the track operation of railway freight equipment under formation conditions is: (3).

[0038] In the formula: is the total energy consumption due to atmospheric resistance during the track operation of all railway freight equipment of the freight train at any time \(t\), with the unit of J; \(\sum\) represents the summation of the energy consumption due to atmospheric resistance of all railway freight equipment in a certain freight train, and \(n\) is the total number of railway freight equipment.

[0039] Considering the definition of the particle velocity and the work done by the resistance, the basic energy consumption due to atmospheric resistance and the additional energy consumption due to atmospheric resistance in the tunnel It can be further expressed as: (4), (5).

[0040] In the formula: is the basic atmospheric resistance of railway freight equipment j during operation, with the unit of N; is the additional atmospheric resistance of railway freight equipment j when running in a tunnel, with the unit of N, is the running speed of railway freight equipment j.

[0041] For the calculation of the basic atmospheric resistance during the operation of railway freight equipment, the formula for the basic atmospheric resistance of railway freight equipment j in a tunnel under formation conditions can be expressed as: (6).

[0042] In the formula: is the relative wind angle; is the running speed of railway freight equipment j; is the atmospheric density of railway freight equipment j at the running position, with the unit of kg / m 3 ; is the formation reduction coefficient of the atmospheric running resistance of railway freight equipment j under formation conditions. This coefficient is related to the formation position of the vehicle and the front and rear vehicle types and can be obtained through wind tunnel tests combined with flow field simulation; is the atmospheric resistance coefficient of railway freight equipment j, obtained through wind tunnel tests or flow field simulation; is the positive area of the windward projection of railway freight equipment j, with the unit of m 2 ; is the natural wind speed of the atmosphere during operation, which is the tunnel wind when in the tunnel, with the unit of m / s.

[0043] Let represent the ratio of the natural wind speed of the atmosphere during operation to the running speed, , that is . Then formula (6) can be expressed as: (7).

[0044] When railway freight equipment j is in the tunnel section during operation, considering the influence of geothermal heat usually existing in the tunnel, there is a temperature gradient distribution inside the tunnel. Therefore, it is necessary to first calculate the atmospheric density of the tunnel at its position: (8).

[0045] In the formula: is the atmospheric density of railway freight equipment j at the running position in the tunnel, with the unit of kg / m 3 ; is the initial atmospheric density; is the initial air pressure; is the air pressure at the running position of the railway freight equipment j in the tunnel, with the unit of Pa; is the temperature at the running position of the railway freight equipment j in the tunnel, with the unit of °C.

[0046] When calculating the basic atmospheric resistance during the operation of the railway freight equipment, the natural wind speed calculation formula in the tunnel section is: (9).

[0047] In the formula: is the natural wind speed in the tunnel section; is the atmospheric density at the running position of the railway freight equipment j in the tunnel, with the unit of kg / m 3 ; is the air density at the altitude h, where h is the altitude, with the unit of kg / m 3 ; is the excess static pressure difference between the tunnel openings, which can be calculated and measured by an atmospheric pressure measuring instrument, with the unit of Pa; is the wind speed of the air flow at the tunnel opening, with the unit of m / s; is the height difference between the two openings, with the unit of m; is the tunnel friction factor; is the tunnel length, with the unit of m; is the hydraulic diameter of the tunnel, with the unit of m; is the local resistance coefficient of the tunnel, is the acceleration due to gravity, taking 9.81 m / s 2 .

[0048] The tunnel effect is coupled and calculated using the pressure wave resistance calculation model: When the railway freight equipment j is in the tunnel section during operation, the additional resistance formula of the tunnel can be expressed as: (10), (11).

[0049] In the formula: is the additional atmospheric resistance when the railway freight equipment j is running in the tunnel; is the running speed of the railway freight equipment j; is the calculation coefficient of the tunnel additional resistance; is the tunnel blockage ratio when the freight train passes through the sd-th section of the tunnel; is the tunnel blockage ratio when the freight train passes through the Liangfengya Tunnel; is the total length of the freight train, with the unit of m; is the tunnel length of the additional resistance line for the sd - th section of the tunnel, with the unit of m; is the distance between the ends of two adjacent freight equipment j and freight equipment j - 1 under the formation condition, with the unit of m; is the distance between the ends of two adjacent freight equipment j + 1 and freight equipment j under the formation condition, with the unit of m; and are respectively the vehicle lengths of two adjacent freight equipment j + 1 and freight equipment j under the formation condition, with the unit of m.

[0050] Considering the above formula, the total atmospheric resistance energy consumption of railway freight equipment during track operation under the formation condition can be expressed as: (12), The above formula is the non - linear coupling energy consumption model, which can be further expressed as: (13).

[0051] According to the non - linear coupling energy consumption model, the real - time atmospheric resistance value of the formation and the real - time aerodynamic energy consumption of the formation are output. Specifically, the real - time aerodynamic energy consumption of the formation is obtained according to formula (13), and the real - time aerodynamic resistance value of the formation is obtained by adding formula (7) and formula (10) and summing for each vehicle.

[0052] The data processing module includes an edge computing layer and an intelligent analysis layer. The edge computing layer is used to construct a dynamic wind attack angle correction model based on the operating state of railway freight equipment, and use the dynamic wind attack angle correction model to correct the calculation process of atmospheric resistance energy consumption. The intelligent analysis layer is used to optimize the traveling strategy of railway freight equipment according to the atmospheric resistance energy consumption.

[0053] In the edge computing layer, the track curvature is inversely deduced based on the lateral acceleration spectrum, and the equivalent synthetic wind speed is calculated by combining the vehicle speed, natural wind speed and direction, and tunnel ventilation parameters; the equivalent synthetic wind speed fuses the wind speed vector, vehicle heading angle and track curvature to construct a dynamic wind attack angle correction model, and uses the dynamic wind attack angle correction model to correct the calculation process of atmospheric resistance energy consumption by real - time correcting the atmospheric resistance coefficient. As Figure 2 shown, input parameters such as environmental wind direction angle, leading vehicle heading angle, vehicle length, vehicle spacing, head vehicle acceleration and environmental wind speed, and calculate them sequentially through the vehicle heading angle calculation module, dynamic attack angle calculation module and atmospheric resistance coefficient correction module, and output the effective wind attack angle and the corrected atmospheric resistance coefficient.

[0054] The specific steps are as follows: The edge computing layer includes a vehicle heading angle calculation module, a dynamic attack angle calculation module and an atmospheric resistance coefficient correction module: (1) The edge computing layer first calculates speed and displacement.

[0055] In a specific embodiment, generally, railway freight equipment is not powered, and data acquisition equipment is usually installed on the locomotive. For calculating the air resistance, it can be considered that the speed of the locomotive is the same as that of the railway freight carriages. Therefore, for the acceleration in the forward direction a compensated integral calculation is performed to obtain the running speed of the freight train.

[0056] (14).

[0057] is the speed at the initial moment of integration, with the unit of m / s; is the gravitational acceleration, taking 9.81 m / s 2 ; is the slope of the track ramp during operation.

[0058] The above calculation formula always includes the calculation of the influence of the ramp on the vehicle speed.

[0059] Since acceleration data is used in many places in this embodiment, an indirect calculation method is adopted for calculating the running speed of the train, that is, the acceleration signal collected by the acceleration sensor is integrated to obtain the speed. In other embodiments, various methods such as directly collecting by on-vehicle equipment and detecting by equipment beside the track can also be used to obtain the speed of the train in the forward direction.

[0060] For a railway freight train under formation, it is necessary to consider the positions of each railway freight equipment on the line in order to calculate the air resistance under different environmental conditions at different positions. Taking the middle position between the two bogies of the railway freight equipment numbered 1 as the starting point for recording the position of this train of railway freight equipment, then the position X of the first railway freight equipment running on the track line at time t 1 can be obtained by integrating the speed v, and the cumulative error (error less than 0.05%) is periodically corrected by the GNSS absolute position. Based on the displacement calculation formula as follows: (15).

[0061] Except for the first railway freight equipment, the running position of other freight equipment j (at this time, j≥2) on the track at time t can be expressed as: (16).

[0062] In the formula: is the running position of the railway freight equipment j on the track at time t, with the unit of m; n is the total number of railway freight equipment under the formation condition; is the distance between the ends of two adjacent freight equipment j and freight equipment j-1 under the formation condition, with the unit of m; and They are the vehicle lengths of two adjacent freight equipments j-1 and j under formation conditions, with the unit of m.

[0063] (2) Secondly, the edge computing layer performs curve and straight line recognition. Specifically, it dynamically checks against the designed line map in real time. Among them, the dynamic check of the line map includes trajectory matching and position matching. Trajectory matching means whether the displacement points at the calculated moments obtained by the numerical calculation of formula (16) are consistent with the displacement points measured by the satellite GPS navigation module. Position matching means whether the orbital curve or straight line information calculated according to formula (17) is consistent with the information in the orbital designed line map.

[0064] In a specific implementation, the wind direction effect is coupled and calculated. Based on the lateral acceleration , combined with the vehicle speed v and the current position superelevation angle the radius of curvature R of the track is inversely calculated in real time: (17).

[0065] Automatic recognition of straight or curve sections is achieved by whether R is infinite (∞). The running trajectories in this embodiment include straight lines, curves, and slopes. After the straight or curve sections of the sections are recognized, the GPS and inertial navigation data are fused by Kalman filtering for positioning. According to the speed calculation formula (14) including slope calculation and based on the displacement calculation formula (15), the digital trajectory of the vehicle operation and the position on the track line are generated.

[0066] (3) Finally, the edge computing layer models the relative angle of the windmill.

[0067] In a specific implementation, based on the vehicle heading angle and the environmental wind direction angle , the relative wind angle is calculated as: (18).

[0068] Usually, the heading angle of the traction locomotive is output by CNSS or INS or calculated by cumulative change along the track line direction from the starting position, while the heading angles of each railway freight equipment are calculated by combining the locomotive heading angle with the positions of each vehicle on the track. In formula (18), the vehicle speeds of each vehicle are the same, considering the vector synthesis of the vehicle speed and the relative wind speed.

[0069] (19).

[0070] In the formula, is the heading angle of the first railway freight car; is the heading angle of the jth railway freight car. The vehicle heading angle is updated dynamically in real time according to the vehicle operation position.

[0071] (20).

[0072] Wherein, is the corrected atmospheric drag coefficient, is the atmospheric drag coefficient of vehicle j before correction; is the non-linear fitting coefficient, which can be obtained by numerical simulation method (CFD). is the curvature-angle of attack coupling coefficient, generally taking values from 0.1 to 0.3 rad·m.

[0073] In the above calculation process, the vehicle heading angle calculation module is used for the calculations of formulas (14), (17) and (19), the dynamic angle of attack calculation module is used for the calculation of formula (18), and the atmospheric drag coefficient correction module is used for the calculation of formula (20).

[0074] After that, the equivalent synthetic wind speed can be calculated by combining the vehicle speed, natural wind speed and direction, and tunnel ventilation parameters. By fusing the wind speed vector, vehicle heading angle and track curvature, a dynamic wind angle of attack correction model can be constructed to correct the atmospheric drag coefficient in real time.

[0075] The dynamic wind angle of attack correction model in this embodiment corrects the atmospheric drag coefficient in real time by fusing the wind speed vector, vehicle heading angle and track curvature, and the error rate < 5%. The intelligent analysis layer calculates the atmospheric drag through the formation-tunnel-wind direction coupling model and generates energy consumption optimization and safety warning strategies.

[0076] In a specific implementation manner, a case library of atmospheric drag characteristics in different climate zones and different formation forms is accumulated to support case AI reasoning and decision-making. A reasonable formation sequence of each railway freight equipment is recommended to make it have the minimum energy consumption of running atmospheric drag during operation. The reasoning of the core algorithm for energy consumption optimization is as follows: Through the operation atmospheric drag and energy consumption algorithms of the core operation module, it can be obtained that the atmospheric drag and energy consumption of railway freight equipment are independent of the transport load, and are only related to the vehicle shape, size, aerodynamic performance, and formation sequence.

[0077] Under the condition of the same energy consumption, the shorter the required transport time and the longer the transport distance (i.e., the vehicle running speed), the higher the energy efficiency of the atmospheric drag of the railway freight equipment under the formation. Considering that eliminating the speed term helps to converge the diffusion effect of the energy efficiency value, for the air resistance energy efficiency there is the following definition: (21).

[0078] Generally, the energy efficiency of the atmospheric drag operation of railway freight equipment under the formation should be compared at the same speed and the same time, that is If it is a constant term, then Equation (21) can be further expressed as: (22).

[0079] In the formula, is the energy efficiency of the atmospheric resistance per unit time of railway freight equipment under the formation at the running speed .

[0080] It can be seen from Equation (22) that the energy efficiency of the atmospheric resistance per unit time of railway freight equipment under the formation at the running speed must have a minimum value. Its minimum value is mainly determined by Equation (23): (23).

[0081] That is, under the condition of the same operating environment, the mixed formation of different vehicle types will lead to different values of the formation reduction coefficient , thus generating the optimal vehicle formation form. That is: (24).

[0082] Among them, is the minimum value of the energy efficiency of the atmospheric resistance.

[0083] In addition, the optimal vehicle speed is dynamically planned by using the reinforcement learning algorithm. For example, the speed is reduced in the strong wind section to reduce the atmospheric resistance, and the speed is increased after passing the strong wind section to achieve energy reduction and consumption reduction.

[0084] An energy-saving driving strategy based on model predictive control (MPC) is established to generate the optimal speed curve under the line conditions of the next 3-5 kilometers based on the current environmental data, and to balance the real-time atmospheric resistance energy consumption and operation efficiency.

[0085] Under the formation state, the air flow between vehicles interferes with each other (such as the head streamline, the gap between car bodies, the tail vortex, etc.), affecting the overall resistance. It can be improved from the following aspects: Improvement of the vehicle body shape: Design a more streamlined head and reduce the concave and convex structures on the vehicle body surface. Optimization of the formation method: Adjust the vehicle spacing, close the gap or adopt a coupler design to reduce the turbulence. Application of new materials: Select lightweight or low wind resistance materials to reduce energy consumption.

[0086] Since the atmospheric resistance is proportional to the square of the speed, the higher the speed, the more significant the resistance energy consumption. By analyzing the resistance characteristics under the formation state, the economic speed range can be determined, and the best balance point between energy consumption and transportation efficiency can be found. It can support the research on speed increase and provide a theoretical basis for the feasibility of high-speed freight trains. It can also be used as a reference for dynamic scheduling, optimizing the train operation diagram according to the resistance characteristics, and reducing the peak-valley fluctuation of energy consumption.

[0087] The optimized formation plan in this embodiment has flexibility and adaptability. Due to the significant differences in aerodynamic characteristics for different cargo types (such as containers and bulk cargo) and formation lengths (short formations and long formations), the optimal formation method can be selected according to the cargo type. It can also optimize the connection efficiency between railways and other transportation modes (such as sea transportation and road transportation).

[0088] The intelligent analysis layer also includes a safety warning layer, which is used to predict the derailment risk probability based on the dynamic critical overturning coefficient algorithm, integrating the wind load spectrum, track curvature, and vehicle centroid offset; perform real-time anomaly detection, extract features from the real-time atmospheric resistance energy consumption, and warn of abnormal working conditions according to the comparison result between the real-time atmospheric resistance energy consumption and the dynamic threshold.

[0089] In a specific implementation, based on the dynamic critical overturning coefficient (DCOT) algorithm, integrating the wind load spectrum, track curvature, and vehicle centroid, predict the derailment risk probability and trigger a three-level braking response, as Figure 4 shown. The input parameters include real-time data and static data. The real-time data includes wind speed, wind direction, acceleration, vehicle speed, position, and heading angle. The static data includes vehicle size and weight, center of gravity height, track spacing, and wheel-rail friction coefficient. And data preprocessing is performed according to the input parameters, including real-time calculation of the vehicle's dynamic wind attack angle, calculation of the vehicle's lateral windward area, calculation of the vehicle's dynamic lateral wind load, and calculation of the track curve radius R. Then, dynamic overturning moment calculation, anti-overturning moment calculation, and calculation of the dynamic critical overturning coefficient (DCOT index) are carried out. Finally, risk level determination and hierarchical control output are performed. Among them, a risk level determination threshold is set. When DCOT ≤ 0.8, it is determined as a first-level risk, and early warning and continuous monitoring are carried out. When 0.8 < DCOT < 1.2, it is determined as a second-level risk, and the train is controlled to brake and decelerate for sliding. When 1.2 ≤ DCOT, it is determined as a third-level risk, and the train is controlled to perform emergency braking.

[0090] More specifically, the formula for calculating the dynamic overturning moment is: .

[0091] Among them, is the dynamic overturning moment, is the weight of the vehicle, is the height of the vehicle's center of gravity, is the vehicle's dynamic lateral wind load.

[0092] The formula for calculating the anti-overturning moment is: .

[0093] Among them, is the anti-overturning moment, is the wheel-rail friction coefficient, is the spacing between the two tracks.

[0094] The calculation formula of dynamic critical overturning coefficient (DCOT index) is: .

[0095] in, is the dynamic critical overturning coefficient.

[0096] Real-time anomaly detection, feature extraction of atmospheric resistance time series data, and setting of dynamic thresholds to warn of abnormal operating conditions. When the risk threshold exceeds the limit, the graded braking command is triggered within 0.5 seconds. Among them, the atmospheric resistance time series data is the atmospheric resistance data of all operating moments in the historical data.

[0097] Atmospheric drag not only affects energy consumption, but is also closely related to the dynamic stability of train operation. This embodiment evaluates the risk of derailment by calculating the aerodynamic load of a marshaled train in a strong wind environment. The tunnel effect is simulated to predict the impact of air pressure fluctuations in the tunnel on the train and tunnel structure. The marshaling stability is also verified to avoid body swing or fatigue of connecting parts caused by aerodynamic interference.

[0098] The feedback control module is used to provide real-time feedback and control of the railway freight equipment according to the optimized railway freight equipment travel strategy, adjust the vehicle speed and marshaling spacing in real time, and trigger graded braking instructions. The feedback control module provides the driver with real-time atmospheric resistance change trends and optimized railway freight equipment travel strategies through the environmental perception and communication module, and dynamically smooths energy consumption fluctuations.

[0099] The feedback control module is also used to provide a visual interface to display the vehicle's real-time position, wind field distribution, atmospheric resistance, energy consumption curve, energy consumption heat map and risk warning information. The static database module includes the track GIS database and the vehicle characteristics database, which are used to store the track and vehicle static parameters of railway freight equipment. The track GIS database contains data such as slope, curve radius, tunnel parameters, etc. The vehicle characteristics database contains aerodynamic parameters such as length, wind-exposed area, atmospheric drag coefficient, and marshaling configuration parameters such as vehicle spacing and atmospheric drag reduction coefficient.

[0100] The environmental perception and communication module uses distributed meteorological monitoring stations, tunnel entrance wind speed monitoring devices and track status monitoring sensors deployed along the line to obtain environmental perception information, and adopts 5G or LoRa hybrid networking to achieve low-latency data transmission.

[0101] The infrastructure optimization module is used to reversely guide route and vehicle design based on historical data. It includes a data mining layer and a reverse design layer.

[0102] The data mining layer is used to generate digital portraits of line atmospheric characteristics based on historical operation data, identify high - resistance bottleneck sections, such as specific tunnel - curve combinations; by comparing the real - time calculated curve radius with the design value, adopt a line map self - correction mechanism to construct a database of track geometric deviation correction amounts. The reverse design layer is used to guide the optimization of the tunnel cross - section (such as expanding the cross - sectional area by 10% - 15% to reduce the transient wind pressure impact), optimize the track curvature design, and reduce the peak value of atmospheric resistance by 20% - 30%.

[0103] The system of this embodiment can be adapted to various models of freight trains and is applicable to different geographical environments (such as plateaus, coasts, deserts). It can design adaptive formation plans according to different climate and terrain conditions and is used to evaluate the operation reliability of trains in alpine or high - temperature regions.

[0104] This embodiment gives a system operation process: 1. The dynamic data acquisition module collects the original signal at a frequency of 100Hz, and generates 1 - second - level feature vectors after pre - processing by the edge node.

[0105] 2. The core algorithm module updates the atmospheric resistance parameters every 5 seconds and predicts the resistance change in the next 30 seconds in combination with the line conditions.

[0106] 3. The intelligent analysis layer generates an energy consumption assessment report every minute and generates an energy - saving potential analysis for every 10 - kilometer journey.

[0107] 4. The control instructions are transmitted through the dedicated railway 5G network, and the end - to - end delay is controlled within 50ms.

[0108] The present invention discloses a real - time collaborative optimization system and method for atmospheric resistance energy consumption of railway freight formations. Through multi - dimensional data fusion and edge intelligent computing, it realizes the coupled modeling of dynamic wind field - formation effect - tunnel effect, and solves the problems of insufficient accuracy and poor real - time performance of traditional models. The system has the ability of millisecond - level energy consumption optimization and safety warning, promoting the green and intelligent upgrade of railway freight. Through the method of multi - source information fusion, combination of physical models and data - driven, the system realizes the accurate perception and dynamic optimization of atmospheric resistance in complex operation environments, providing a closed - loop intelligent solution for railway freight energy - efficiency management.

[0109] Embodiment Two: Embodiment Two of the present invention provides an energy - consumption optimization method for formation railway freight equipment based on data fusion, including the following steps: Obtain the real - time data during the operation of railway freight equipment.

[0110] Use a multi - coupling effect model to calculate the real - time parameters to obtain the atmospheric resistance energy consumption of railway freight equipment, where a multi - coupling effect model is constructed according to the formation effect, wind - direction effect, and tunnel effect.

[0111] Construct a dynamic wind attack angle correction model according to the operating state of railway freight equipment, and use the dynamic wind attack angle correction model to correct the calculation process of atmospheric resistance energy consumption.

[0112] Optimize the traveling strategy of railway freight equipment according to the atmospheric resistance energy consumption.

[0113] Perform real-time feedback and control on the railway freight equipment according to the optimized traveling strategy of the railway freight equipment.

[0114] Embodiment Three: Embodiment Three of the present invention provides a medium with a program stored thereon, and when the program is executed by a processor, it implements the steps in the energy consumption optimization method for grouped railway freight equipment based on data fusion as described in Embodiment Two of the present invention.

[0115] Embodiment Four: Embodiment Four of the present invention provides a device, including a memory, a processor, and a program stored on the memory and executable on the processor. When the processor executes the program, it implements the steps in the energy consumption optimization method for grouped railway freight equipment based on data fusion as described in Embodiment Two of the present invention.

[0116] The steps involved in Embodiments Two, Three, and Four above correspond to those in Embodiment One, and the specific implementation manners can refer to the relevant description part of Embodiment One.

[0117] Those skilled in the art should understand that the above-mentioned modules or steps of the present invention can be implemented by a general-purpose computer device. Optionally, they can be implemented by program codes executable by a computing device, so that they can be stored in a storage device and executed by the computing device, or they can be separately made into individual integrated circuit modules, or multiple modules or steps among them can be made into a single integrated circuit module to implement. The present invention is not limited to any specific combination of hardware and software.

[0118] Although the specific implementation manners of the present invention are described above in conjunction with the drawings, it is not a limitation to the protection scope of the present invention. Those skilled in the art should understand that based on the technical solutions of the present invention, various modifications or deformations that do not require creative labor by those skilled in the art are still within the protection scope of the present invention.

Claims

1. A system for optimizing energy consumption of railway freight equipment based on data fusion, characterized in that: include: Dynamic data acquisition module, used to obtain real-time data during the operation of railway freight equipment; The core calculation module is used to calculate the real-time parameters using the multi-coupling effect model to obtain the atmospheric resistance energy consumption of railway freight equipment. The multi-coupling effect model is constructed based on the marshaling effect, wind direction effect and tunnel effect. The data processing module includes an edge computing layer and an intelligent analysis layer. The edge computing layer is used to build a dynamic wind angle correction model according to the operating status of railway freight equipment, and use the dynamic wind angle correction model to correct the atmospheric resistance energy consumption calculation process; The intelligent analysis layer is used to optimize the travel strategy of railway freight equipment according to the atmospheric drag energy consumption; The feedback control module is used to provide real-time feedback and control to the railway freight equipment according to the optimized railway freight equipment travel strategy.

2. The energy consumption optimization system for marshaling railway freight equipment based on data fusion according to claim 1 is characterized in that: In the core calculation module, the marshaling effect refers to the shielding effect of the wake of the leading vehicle on the following vehicle caused by the marshaling condition of the railway freight equipment; the wind direction effect refers to the atmospheric resistance change effect caused by the relative wind attack angle during the operation of the railway freight equipment; and the tunnel effect refers to the transient pressure wave resistance effect brought about by the tunnel environment.

3. The energy consumption optimization system for marshaling railway freight equipment based on data fusion according to claim 1, characterized in that: The edge computing layer also includes calculating the equivalent synthetic wind speed by combining vehicle speed, natural wind speed and direction, and tunnel ventilation parameters; The equivalent synthetic wind speed integrates the wind speed vector, vehicle heading angle and track curvature to construct a dynamic wind angle correction model. The dynamic wind angle correction model is used to correct the atmospheric drag energy consumption calculation process by correcting the atmospheric drag coefficient in real time.

4. The energy consumption optimization system for marshaling railway freight equipment based on data fusion according to claim 1 is characterized in that: The intelligent analysis layer also includes a safety warning layer, which is used to predict the probability of derailment risk based on a dynamic critical rollover coefficient algorithm, integrating wind load spectrum, track curvature and vehicle center of mass offset; Perform anomaly detection in real time, extract features of real-time atmospheric drag energy consumption, and warn of abnormal operating conditions based on the comparison results between real-time atmospheric drag energy consumption and dynamic thresholds.

5. The energy consumption optimization system for marshaling railway freight equipment based on data fusion according to claim 1, characterized in that: It also includes a static database module, including a track GIS database and a vehicle characteristic database, which are used to store track and vehicle static parameters of railway freight equipment.

6. The energy consumption optimization system for marshaling railway freight equipment based on data fusion according to claim 1, characterized in that: It also includes an environmental perception and communication module, which uses distributed meteorological monitoring stations deployed along the line, tunnel entrance wind speed monitoring devices and track status monitoring sensors to obtain environmental perception information, and adopts 5G or LoRa hybrid networking to achieve low-latency data transmission.

7. The energy consumption optimization system for marshaling railway freight equipment based on data fusion according to claim 6, characterized in that: The feedback control module provides the driver with real-time atmospheric resistance change trends and optimized railway freight equipment travel strategies through the environmental perception and communication modules.

8. A method for optimizing energy consumption of railway freight equipment based on data fusion, characterized in that: The following steps are involved: Obtain real-time data on the operation of railway freight equipment; The multi-coupling effect model is used to calculate the real-time parameters and obtain the atmospheric resistance energy consumption of railway freight equipment. The multi-coupling effect model is constructed based on the marshaling effect, wind direction effect and tunnel effect. A dynamic wind attack angle correction model is constructed according to the operating status of railway freight equipment, and the atmospheric resistance energy consumption calculation process is corrected using the dynamic wind attack angle correction model. Optimize the travel strategy of railway freight equipment according to the energy consumption of atmospheric resistance; Provide real-time feedback and control of railway freight equipment based on the optimized railway freight equipment travel strategy.

9. A computer-readable storage medium, characterized in that: A plurality of instructions are stored therein, and the instructions are suitable for being loaded by a processor of a terminal device and executed by the method for optimizing energy consumption of marshaled railway freight equipment based on data fusion as described in claim 8.

10. A terminal device, characterized in that: It includes a processor and a computer-readable storage medium, the processor is used to implement each instruction; the computer-readable storage medium is used to store multiple instructions, and the instructions are suitable for being loaded by the processor and executed by the energy consumption optimization method of marshaling railway freight equipment based on data fusion as described in claim 8.

Images