Rail train intelligent sanding control system and method based on adhesion utilization efficiency evaluation

Abstract

The invention relates to a rail train intelligent sanding control system and method based on adhesion utilization efficiency evaluation, and relates to the technical field of rail transit vehicle traction and brake control. The system comprises a data sensing layer, an intelligent control core and an execution and evaluation layer. According to the invention, the technical scheme of efficiency index + adaptive control + hybrid architecture is adopted, so that the fundamental change of the control normal form is realized; the economical efficiency and the environmental protection property are obviously improved; the operation quality and safety are improved; data values are mined, and fine management is achieved; the method is low in cost and high in additional value.

Description

Technical Field

[0001] This invention relates to the field of traction and braking control technology for rail transit vehicles, and in particular to an intelligent sand-spreading control system and method for rail trains based on adhesion utilization efficiency evaluation. Background Technology

[0002] Adhesion control is a core technology for ensuring the traction / braking performance and operational safety of railcars. Sanding systems, by spraying silica sand onto the rail surface to increase the wheel-rail adhesion coefficient, are a key means of addressing low adhesion conditions. However, existing sanding control technologies have significant limitations:

[0003] Passive reactive control mode: The current mainstream control strategy is a negative feedback closed loop of "idling / slippage occurs → detection → sand application suppression". This mode has inherent lag: from the time adhesion instability is detected to the time sand application takes effect, the train has already experienced a interruption of traction / braking force, affecting running stability and comfort, and may even lead to penalty braking due to severe slippage, affecting punctuality.

[0004] The control parameters are rigid and lack adaptability: key parameters such as sand application trigger threshold, duration, and spray volume are usually preset fixed values ​​or a limited number of modes. This makes it impossible to adapt to the dynamically changing adhesion characteristics under different track conditions (slopes, curves), different rail surface conditions (dry, wet, oily), and different weather conditions, resulting in coarse control: either insufficient sand application fails to effectively suppress slippage, or excessive sand application wastes sand and exacerbates wear on the rail surface and vehicle running gear.

[0005] Open-loop control, lacking economic assessment: Existing systems only focus on "suppressing idling / coasting" as the sole control objective, without evaluating the economic costs of achieving this objective. The system cannot know whether the current sand application rate is appropriate or excessive, resulting in an "unrestricted" input of energy and materials, which is inconsistent with the trends of green and intelligent development.

[0006] Disconnected from operations management: Drivers' operating habits (such as traction level lifting rate) have a direct impact on adhesion utilization efficiency, but existing systems cannot quantify the impact of different driving behaviors on sand spreading consumption, and cannot provide data support for optimizing driving standards and implementing energy-saving driving training.

[0007] Therefore, the existing technology is a lagging, rigid, uneconomical and unoptimized control method, and there is an urgent need for a new intelligent sand-spreading control scheme that can achieve forward-looking, adaptive and highly efficient control. Summary of the Invention

[0008] The present invention aims to solve the technical problems in the prior art by providing a smart sand spreading control system and method for rail trains based on adhesion utilization efficiency evaluation.

[0009] To solve the above-mentioned technical problems, the technical solution of the present invention is as follows:

[0010] A smart sand-spreading control system for rail trains based on adhesion utilization efficiency evaluation includes interconnected layers: a data sensing layer, an intelligent control core, and an execution and evaluation layer.

[0011] The data perception layer is used to collect data on traction / braking force, vehicle speed, idling / slippage, and sand spreading status in real time;

[0012] The core of intelligent control includes: an efficiency calculation model, a line history database, and an adaptive intelligent sand-spreading strategy controller. The efficiency calculation model is used to receive data collected by the data perception layer and calculate the adhesion utilization efficiency value in real time. The line history database is used to store the historical adhesion situation and optimal efficiency control parameters of different line sections. The adaptive intelligent sand-spreading strategy controller is used to dynamically adjust the sand-spreading control parameters or generate feedforward commands based on efficiency feedback and line information.

[0013] The execution and evaluation layer includes a driving behavior analysis module, which is used to associate driver IDs with efficiency data, perform statistical analysis, and generate evaluation reports.

[0014] Efficiency Calculation Model

[0015] A method applicable to the above-mentioned intelligent sand-spreading control system for rail trains based on adhesion utilization efficiency evaluation includes the following steps:

[0016] Step 1: Real-time acquisition of multi-source data;

[0017] The application data sensing layer collects in real time the actual traction force or electric braking force output by the traction converter, the vehicle speed and idle / slippage amount provided by the brake control unit, and the real-time working status and estimated flow of the sand spreading system.

[0018] Step 2: Real-time calculation of adhesion utilization efficiency;

[0019] Based on the data from step 1, the adhesion utilization efficiency value under the current working condition is calculated in real time according to the predetermined efficiency calculation model. The efficiency value represents the effective mechanical output converted from a unit of sand consumption.

[0020] Step 3: Generate adaptive control strategy;

[0021] An adaptive intelligent sand-spreading strategy controller is applied to compare the real-time efficiency value obtained in step 2 with the preset target efficiency range, and to query the historical database of the line in conjunction with the real-time position information of the train; based on the comparison and query results, the trigger threshold and spray quantity parameters of the sand-spreading control are dynamically adjusted; if the train is about to enter a known low adhesion zone, a feedforward control command is generated to start preventive sand-spreading.

[0022] Step 4: Control command execution and driving behavior evaluation;

[0023] The control commands generated in step 3 are executed to control the sand spreading system. At the same time, the driving behavior analysis module is used to record and statistically analyze the efficiency values ​​and sand consumption data under different driver identifiers, and generate a driving behavior evaluation report.

[0024] In the above technical solution, step 1 specifically involves: the data perception layer acquiring data from the vehicle bus in real time: reading the current actual force F_actual from the traction converter; reading the vehicle speed v and idling / coasting amount λ from the BCU; acquiring the sand spreading valve status from the sand spreading system and estimating the instantaneous flow rate Q_sand; and acquiring the GPS location and the currently logged-in driver ID from the TCMS.

[0025] In the above technical solution, step 2 specifically includes:

[0026] First, calculate the current effective mechanical power or power change P_effective = F_actual * v;

[0027] Subsequently, the efficiency value η is calculated according to the formula η = P_effective / (Q_sand * K);

[0028] Wherein, K is a comprehensive coefficient used for dimensional unification and system calibration; this efficiency value η is fed back to the adaptive intelligent sand-spreading strategy controller in real time.

[0029] In the above technical solution, step 3 specifically includes:

[0030] Step 31: Efficiency feedback control;

[0031] The efficiency value η is continuously monitored. If the efficiency value η continues to be lower than the target range, the adaptive intelligent sand spreading strategy controller will fine-tune the parameters, increase the idling trigger threshold or reduce the sand spreading pulse width to reduce sand consumption and bring the efficiency value η back to the healthy range.

[0032] Step 32: Feedforward predictive control;

[0033] The adaptive intelligent sanding strategy controller queries the historical database of the line based on the GPS location. If the database shows that there is a historically low adhesion slope 500 meters ahead, the controller immediately bypasses the feedback loop and directly outputs a command: to start sanding at a low frequency and duty cycle before idling occurs, to pre-wet the rail surface and prevent a sharp drop in adhesion.

[0034] In the above technical solution, step 4 specifically includes:

[0035] Control commands are sent to the sand-spreading actuator for execution; simultaneously, all efficiency data, driver ID, and location information are synchronized to the driving behavior analysis module; this module analyzes the data and outputs a report.

[0036] The present invention has the following beneficial effects:

[0037] The advantages of the intelligent sand-spreading control system and method for rail trains based on adhesion utilization efficiency evaluation of the present invention specifically include:

[0038] The control paradigm has undergone a fundamental transformation: from "passive response" to "proactive prediction and efficiency optimization." By introducing and tracking the core indicator of "efficiency," the control objective has been upgraded, moving from ensuring "stability" to pursuing "high efficiency and stability."

[0039] Significantly improves economic efficiency and environmental friendliness: The adaptive algorithm can accurately match the supply of sand and the adhesion requirements, avoiding waste under fixed parameter control, and can greatly reduce sand consumption (expected to be 20%-50%), directly saving material costs and maintenance workload, while reducing sand dust pollution to the track and the environment.

[0040] Improved operational quality and safety: The feedforward control mode can make "fine-tuning" to prevent adhesion from deteriorating, avoiding the impact and traction / braking blockage caused by severe slippage / slippage, making train operation smoother and more comfortable, and improving safety.

[0041] Unlocking the value of data for refined management: For the first time, the efficiency of sticky utilization is objectively linked to driver operation, providing indisputable data support for the operations department to implement precise energy-saving driving training and performance management, and further exploring the potential for energy conservation and consumption reduction from the perspective of human-machine collaboration.

[0042] Low implementation cost and high added value: This invention is based on core algorithm and software innovation, which can be achieved by upgrading existing vehicle control software without modifying major hardware equipment, but can bring significant safety, economic and social benefits, and is easy to promote in new vehicle design and existing vehicle modification. Attached Figure Description

[0043] The present invention will now be described in further detail with reference to the accompanying drawings and specific embodiments.

[0044] Figure 1 This is a schematic diagram of the logical architecture, data flow, and control flow of the intelligent sand-spreading control system for rail trains based on adhesion utilization efficiency evaluation of the present invention. Detailed Implementation

[0045] The inventive concept of this invention is as follows:

[0046] The intelligent sand-spreading control system and method for rail trains based on adhesion utilization efficiency evaluation of this invention, with its technical solution of efficiency index + adaptive control + hybrid architecture, is not a simple combination of conventional design or common knowledge for those skilled in the art. Existing technologies have never considered the "economic efficiency" of sand spreading, nor have they ever attempted to use historical track data for feedforward control of sand spreading. Expanding the control objective from "stability" to "high-efficiency stability," and constructing a completely new control architecture and algorithm for this purpose, is not obvious to those skilled in the art and requires overcoming long-standing technical biases.

[0047] This invention is the first in the field of rail vehicle control to propose the economic indicator of "adhesion utilization efficiency" and to create its real-time calculation method. This indicator dynamically links the "input" (cost) of sand spreading with the "output" (benefits) of adhesion effect, providing an unprecedented and quantifiable control objective for closed-loop optimization.

[0048] This invention abandons fixed-parameter control and creates an adaptive control mechanism with real-time efficiency as the core feedback. The controller continuously fine-tunes its parameters to ensure the system always operates in the "high-efficiency zone," achieving online self-learning and self-optimization.

[0049] This invention creatively combines closed-loop optimization based on efficiency feedback with feedforward prediction based on historical line data to form a hybrid intelligent control architecture. It can both make economic optimizations for the current state and proactively prevent future risks, thus balancing optimality and robustness.

[0050] The present invention will now be described in detail with reference to the accompanying drawings.

[0051] like Figure 1 As shown, the intelligent sand-spreading control system for rail trains based on adhesion utilization efficiency evaluation of the present invention includes, interconnected layers: a data sensing layer, an intelligent control core, and an execution and evaluation layer; wherein:

[0052] The data perception layer is used to collect data on traction / braking force, vehicle speed, idling / slippage, and sand spreading status in real time;

[0053] The core of intelligent control includes: an efficiency calculation model, a line history database, and an adaptive intelligent sand-spreading strategy controller. The efficiency calculation model is used to receive data collected by the data perception layer and calculate the adhesion utilization efficiency value (η) in real time. The line history database is used to store the historical adhesion situation and optimal efficiency control parameters of different line sections. The adaptive intelligent sand-spreading strategy controller is used to dynamically adjust the sand-spreading control parameters or generate feedforward commands based on efficiency feedback and line information.

[0054] The execution and evaluation layer includes a driving behavior analysis module, which is used to associate driver IDs with efficiency data, perform statistical analysis, and generate evaluation reports.

[0055] The embodiments of the present invention will be described in further detail below with reference to the accompanying drawings. Figure 1 This is a schematic diagram of the logical architecture, data flow, and control flow of the system of the present invention.

[0056] The intelligent sand-spreading control system method for rail trains based on adhesion utilization efficiency evaluation of the present invention is applicable to the intelligent sand-spreading control system for rail trains of the present invention, and includes the following steps:

[0057] Step 1: Real-time acquisition of multi-source data: Using the data perception layer, the actual traction force or electric braking force output by the traction converter, the vehicle speed and slip / glide amount provided by the brake control unit (BCU), and the real-time working status and estimated flow rate of the sand spreading system are acquired in real time.

[0058] Step 2: Real-time calculation of adhesion utilization efficiency: Applying the efficiency calculation model, based on the data from Step 1, and according to the predetermined efficiency calculation model, calculate the adhesion utilization efficiency value (η) under the current working condition in real time; the efficiency value (η) represents the effective mechanical output converted from unit sand consumption;

[0059] Step 3: Adaptive control strategy generation: Apply the adaptive intelligent sand spreading strategy controller to compare the real-time efficiency value (η) obtained in Step 2 with the preset target efficiency range, and query the historical database of the line in conjunction with the real-time position information of the train; based on the comparison and query results, dynamically adjust the trigger threshold and injection quantity parameters of the sand spreading control; if the train is about to enter a known low adhesion zone, generate a feedforward control command to start preventive sand spreading.

[0060] Step 4: Control command execution and driving behavior evaluation: Execute the control commands generated in Step 3 to control the sand spreading system; at the same time, use the driving behavior analysis module to record and statistically analyze the efficiency value (η) and sand consumption data under different driver identifiers over a long period of time, and generate a driving behavior evaluation report.

[0061] Specifically:

[0062] Step 1 is as follows:

[0063] Real-time acquisition of multi-source data: Real-time acquisition of actual traction force or electric braking force output from the traction converter, vehicle speed and slip / coast amount provided by the brake control unit (BCU), and real-time operating status and estimated flow rate of the sand spreading system. The data perception layer acquires data in real-time from the vehicle bus: Reads the current actual force F_actual from the traction converter (traction force is positive, braking force is negative); reads the vehicle speed v and slip / coast amount λ from the BCU; obtains the sand spreading valve status from the sand spreading system and estimates the instantaneous flow rate Q_sand (e.g., through pulse width modulation (PWM) duty cycle estimation); obtains the GPS location and the currently logged-in driver ID from the TCMS.

[0064] Step 2 is as follows:

[0065] Real-time calculation of adhesion utilization efficiency: Based on the data from step 1, and according to a predetermined efficiency calculation model, the adhesion utilization efficiency value (η) under the current working condition is calculated in real time; the efficiency value (η) represents the effective mechanical output converted from unit sand consumption. The core model of the efficiency calculation model receives the above data.

[0066] First, calculate the current effective mechanical power (or power change): P_effective = F_actual * v.

[0067] Subsequently, the efficiency value η is calculated according to the formula η = P_effective / (Q_sand * K);

[0068] Wherein, K is a comprehensive coefficient used for dimensional uniformity and system calibration. This efficiency value η is fed back to the adaptive intelligent sand-spreading strategy controller in real time.

[0069] Step 3 specifically involves:

[0070] Adaptive control strategy generation: The real-time efficiency value (η) obtained in step 2 is compared with the preset target efficiency range, and the historical database of the line is queried in conjunction with the real-time position information of the train; based on the comparison and query results, the trigger threshold and injection quantity parameters of sand spreading control are dynamically adjusted; if the train is about to enter a known low adhesion zone, a feedforward control command is generated to start preventive sand spreading.

[0071] The adaptive intelligent sand-spreading strategy controller is the system's brain. It executes decisions in two modes, including steps 31 and 32, where:

[0072] Step 31, Efficiency Feedback Control: Continuously monitor the efficiency value η. If the efficiency value η remains below the target range, it indicates poor economic efficiency in sand spreading, possibly due to excessive sand quantity. The adaptive intelligent sand spreading strategy controller will fine-tune parameters, such as slightly increasing the idling trigger threshold or decreasing the sand spreading pulse width, to reduce sand consumption and bring the efficiency value η back to a healthy range.

[0073] Step 32, feedforward predictive control: The adaptive intelligent sanding strategy controller queries the line historical database according to the GPS position. If the database shows that the next 500 meters is a historical low adhesion ramp, the adaptive intelligent sanding strategy controller immediately bypasses the feedback loop and directly outputs an instruction: before wheel spin occurs, start sanding at a lower frequency and duty cycle to "pre-wet the rail surface" and prevent a sharp drop in adhesion.

[0074] Step 4 is specifically as follows:

[0075] Execution of control instructions and evaluation of driving behavior: Execute the control instructions generated in Step 3 to control the operation of the sanding system; at the same time, long-term record and count the efficiency values (η) and sand consumption data under different driver identifiers to generate a driving behavior evaluation report. The control instructions are sent to the sanding actuator for execution. At the same time, all efficiency data, driver ID, and location information are synchronized to the driving behavior analysis module. This driving behavior analysis module analyzes the massive data and can output a report: "The average efficiency value of Driver A on Line L is η1, and the sand consumption is S1; the average efficiency value of Driver B under the same conditions is η2 (η2 > η1), and the sand consumption is S2 (S2 < S1)". This report is displayed through a visualization interface for optimizing training and management.

[0076] Obviously, the above embodiments are merely examples given for clear illustration and not limitations on the implementation manners. For those of ordinary skill in the art, other different forms of changes or variations can be made based on the above description. It is not necessary and impossible to enumerate all the implementation manners here. And the obvious changes or variations derived therefrom are still within the protection scope of this invention.

Claims

1. A smart sand-spreading control system for rail trains based on adhesion utilization efficiency evaluation, characterized in that, It includes the following interconnected layers: data perception layer, intelligent control core, and execution and evaluation layer; The data perception layer is used to collect data on traction / braking force, vehicle speed, idling / slippage, and sand spreading status in real time; The core of intelligent control includes: an efficiency calculation model, a line history database, and an adaptive intelligent sand-spreading strategy controller. The efficiency calculation model is used to receive data collected by the data perception layer and calculate the adhesion utilization efficiency value in real time. The line history database is used to store the historical adhesion situation and optimal efficiency control parameters of different line sections. The adaptive intelligent sand-spreading strategy controller is used to dynamically adjust the sand-spreading control parameters or generate feedforward commands based on efficiency feedback and line information. The execution and evaluation layer includes a driving behavior analysis module, which is used to associate driver IDs with efficiency data, perform statistical analysis, and generate evaluation reports.

2. A method for intelligent sand spreading control of rail trains based on adhesion utilization efficiency evaluation, applicable to the system described in claim 1, characterized in that, Includes the following steps: Step 1: Real-time acquisition of multi-source data; The application data sensing layer collects in real time the actual traction force or electric braking force output by the traction converter, the vehicle speed and idle / slippage amount provided by the brake control unit, and the real-time working status and estimated flow of the sand spreading system. Step 2: Real-time calculation of adhesion utilization efficiency; Based on the data from step 1, the adhesion utilization efficiency value under the current working condition is calculated in real time according to the predetermined efficiency calculation model. The efficiency value represents the effective mechanical output converted from a unit of sand consumption. Step 3: Generate adaptive control strategy; An adaptive intelligent sand-spreading strategy controller is applied to compare the real-time efficiency value obtained in step 2 with the preset target efficiency range, and to query the historical database of the line in conjunction with the real-time position information of the train; based on the comparison and query results, the trigger threshold and spray quantity parameters of the sand-spreading control are dynamically adjusted; if the train is about to enter a known low adhesion zone, a feedforward control command is generated to start preventive sand-spreading. Step 4: Control command execution and driving behavior evaluation; The control commands generated in step 3 are executed to control the sand spreading system. At the same time, the driving behavior analysis module is used to record and statistically analyze the efficiency values ​​and sand consumption data under different driver identifiers, and generate a driving behavior evaluation report.

3. The method according to claim 2, characterized in that, Step 1 specifically involves the data perception layer acquiring data from the vehicle bus in real time: reading the current actual force F_actual from the traction converter; reading the vehicle speed v and idling / coasting amount λ from the BCU; obtaining the sand spreading valve status from the sand spreading system and estimating the instantaneous flow rate Q_sand; and obtaining the GPS location and the currently logged-in driver ID from the TCMS.

4. The method according to claim 3, characterized in that, Step 2 is as follows: First, calculate the current effective mechanical power or power change P_effective = F_actual * v; Subsequently, the efficiency value η is calculated according to the formula η = P_effective / (Q_sand * K); Wherein, K is a comprehensive coefficient used for dimensional unification and system calibration; this efficiency value η is fed back to the adaptive intelligent sand-spreading strategy controller in real time.

5. The method according to claim 4, characterized in that, Step 3 specifically includes: Step 31: Efficiency feedback control; The efficiency value η is continuously monitored. If the efficiency value η continues to be lower than the target range, the adaptive intelligent sand spreading strategy controller will fine-tune the parameters, increase the idling trigger threshold or reduce the sand spreading pulse width to reduce sand consumption and bring the efficiency value η back to the healthy range. Step 32: Feedforward predictive control; The adaptive intelligent sanding strategy controller queries the historical database of the line based on the GPS location. If the database shows that there is a historically low adhesion slope 500 meters ahead, the controller immediately bypasses the feedback loop and directly outputs a command: to start sanding at a low frequency and duty cycle before idling occurs, to pre-wet the rail surface and prevent a sharp drop in adhesion.

6. The method according to claim 2, characterized in that, Step 4 is as follows: Control commands are sent to the sand-spreading actuator for execution; simultaneously, all efficiency data, driver ID, and location information are synchronized to the driving behavior analysis module; this module analyzes the data and outputs a report.

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