Data processing method and system for steel rail intelligent grinding scheme

Abstract

The present application relates to the technical field of rail grinding, and provides a data processing method and system for a smart rail grinding scheme, which comprises the following steps: a field smart computing platform collects grinding basic data, imports and analyzes a global grinding scheme; according to the field measured continuous profile and the grinding threshold, the entire section is divided into multiple small intervals that do not need to be worked and small intervals that need to be worked, and grinding process parameters of the small intervals that need to be worked are generated; a wireless communication network between the field smart computing platform and field grinding small machines is built; and the field smart computing platform distributes the grinding process parameters of the multiple small intervals that need to be worked to the field grinding small machines. The present application can realize intelligent and accurate splitting of the grinding scheme, real-time distribution of wireless signals, multi-machine collaborative scheduling, accurate execution of field work, automatic quality checking, adaptive correction of the scheme, full-closed-loop intelligent work, and improvement of the quality and efficiency of field fine grinding and maintenance of rails.

Description

Technical Field

[0001] This invention relates to the field of rail grinding technology, and in particular to a data processing method and system for a digital intelligent rail grinding solution. Background Technology

[0002] Rails endure repeated train rolling, frictional impact, and environmental erosion over long periods, making them prone to various localized defects such as profile distortion, surface ripples, weld protrusions, thickened edges, and spalling. Compared to large rail grinding vehicles suitable for batch grinding of long sections, small portable rail grinding machines (small machines) offer advantages such as mobility, adaptability to complex sections (turnouts, welds, curved sections, confined spaces), and low construction costs, making them core equipment for routine railway maintenance, defect repair, and preventative maintenance. However, the current operation mode of small machines in the industry is still in the traditional, extensive manual stage, with low levels of intelligence, digitalization, and closed-loop management, presenting many technical challenges that urgently need to be addressed. 1. The small-scale rail grinding solution lacks intelligent task breakdown, resulting in poor task adaptability. Existing rail grinding solutions are mostly overall macro-level solutions at the kilometer or section level, only indicating the overall grinding indicators and profile repair requirements for each section. They fail to address the discontinuous gridding and interval breakdown specific to the single-point, small-segment, and refined operation characteristics of small-scale machines. On-site, the process relies entirely on manual interpretation and subjective task breakdown by operators, leading to inconsistent accuracy and a high risk of overlapping grinding intervals, missed grinding areas, insufficient grinding of key defect areas, and over-grinding of normal areas. The standardization of operations is extremely low.

[0003] 2. Outdated work instruction transmission and lack of a digital distribution mechanism. Currently, small-scale grinding tasks rely on paper instructions, verbal communication, and offline file transfers for distribution, lacking a real-time wireless distribution channel. This results in issues such as delayed instruction transmission, content discrepancies, and untimely updates. When line defects or maintenance standards are temporarily adjusted, the on-site work parameters cannot be updated synchronously, easily leading to batch work failing to meet standards.

[0004] 3. The operation process is an open-loop model, lacking quality verification and iterative correction. Traditional small-scale machine operation follows an open-loop process of "scheme issuance - manual grinding - completion acceptance." The grinding process relies on manual experience to control the grinding angle, grinding pressure, and number of grinding passes. After the operation is completed, acceptance is based solely on manual visual inspection or simple sampling, lacking precise verification using measured continuous profile data. It is impossible to quantify grinding deviations, identify residual defects, and reverse-engineer grinding process parameters and operation plans based on actual grinding results. This results in poor grinding quality consistency and a high rate of defect recurrence.

[0005] 4. Disorderly coordination among multiple devices leads to low work efficiency. When multiple small grinding machines operate in parallel on site, the lack of intelligent task allocation and anti-conflict scheduling mechanisms easily leads to problems such as cross-interference in work areas, waste of manpower and equipment, and inability to monitor work progress in real time. At the same time, the back-end cannot perceive equipment status, work progress, and construction quality in real time, and there are no digital supervision and traceability means throughout the process.

[0006] 5. Lack of a complete data loop hinders intelligent operation and maintenance iteration. In traditional operation modes, grinding plan data, operation process data, quality inspection data, and equipment operation data are fragmented, failing to form a data loop, unable to accumulate a working condition matching database, making it difficult to achieve continuous optimization of grinding processes, and unable to adapt to the development needs of intelligent operation and maintenance and digital maintenance of railways.

[0007] Therefore, there is a need for a data processing method and system for a rail intelligent grinding solution, which can intelligently and accurately divide a large section grinding solution into discontinuous gridded small sections that need to be worked on, greatly reducing the length of rails to be ground on site; wirelessly and in real time distribute parameters of the small sections to be worked on, coordinate and schedule multiple machines, accurately execute on-site operations, automatically check quality, adaptively correct the solution, and achieve fully closed-loop intelligent operation, thereby improving the quality and efficiency of rail fine grinding and maintenance.

[0008] The information disclosed in the background section is only intended to enhance the understanding of the background of this application, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0009] The main purpose of this invention is to overcome the problems of existing small-scale rail grinding operation schemes, such as crude segmentation, delayed instruction transmission, lack of correction in open-loop operation, disordered multi-machine collaboration, and lack of quality control. It provides a data processing method and system for a smart rail grinding scheme, which can realize intelligent and precise segmentation of grinding schemes, wireless real-time distribution, multi-machine collaborative scheduling, precise execution of on-site operations, automatic quality verification, adaptive scheme correction, and fully closed-loop intelligent operation, thereby improving the quality and efficiency of refined rail grinding and maintenance.

[0010] To achieve the above objectives, the first aspect of the present invention provides a data processing method for a rail intelligent grinding scheme, comprising the following steps: S1: The on-site digital computing platform collects basic polishing data, imports and analyzes the global polishing plan based on the basic polishing data; S2: The on-site digital computing platform divides the entire section into multiple small sections that do not require operation and small sections that require operation based on the on-site measured continuous profile and grinding threshold, and generates grinding process parameters for the small sections that require operation. S3: Establish a wireless communication network between the on-site digital computing platform and the 3D rail digital instrument and the on-site grinding machine; S4: The on-site digital computing platform distributes the grinding process parameters of multiple small areas that need to be operated to the on-site grinding machines; S5: On-site grinding machine performs grinding operations; S6: The 3D rail digitizer collects the profile of the polished rail and uploads it to the on-site digital computing platform; the on-site digital computing platform determines whether the profile of the polished rail is qualified. If it is not qualified, proceed to step S7. S7: Generate secondary grinding process parameters and send them to the corresponding on-site grinding machine for re-grinding.

[0011] According to an exemplary embodiment of the present invention, the data processing method of the intelligent rail grinding scheme further includes: S8: archiving the data from steps S1 to S7.

[0012] According to an exemplary embodiment of the present invention, in step S1, the basic data for polishing includes: The actual continuous profile data of the target line section, the type of defects, the location of defects, the grinding deviation value, the curve radius of the line, and the train traffic density; The global polishing scheme includes target profile, maximum allowable polishing amount, polishing quality threshold, operation time limit, and safety constraint standards.

[0013] According to an exemplary embodiment of the present invention, in step S2, the size of the small area to be worked is adapted to the coverage range of a single operation of the on-site grinding machine.

[0014] According to an exemplary embodiment of the present invention, in step S2, the generation of polishing process parameters includes: The global grinding scheme is divided into discontinuous gridded independent work intervals, generating single interval grinding depth, grinding angle, number of grinding passes, feed rate, work range, and quality pass threshold.

[0015] According to an exemplary embodiment of the present invention, step S2 further includes: dividing the small groups that need to perform tasks into three levels: urgent, important, and routine, and completing the task sorting.

[0016] According to an exemplary embodiment of the present invention, step S3 further includes: the on-site grinding machine completing a self-check of the equipment status.

[0017] According to an exemplary embodiment of the present invention, step S5 further includes: transmitting the polishing process data back to the on-site intelligent computing platform, and issuing an early warning if the polishing behavior is in violation of regulations.

[0018] According to an exemplary embodiment of the present invention, in step S6, the on-site intelligent computing platform determines whether the rail profile is qualified based on the polished profile, which includes: the on-site intelligent computing platform comparing the measured profile with the target profile; Calculate the profile deviation value, polishing residue, and GQI quality score; The calculated values ​​are used to determine whether the work quality level is qualified according to the maintenance standards.

[0019] According to a second aspect of the present invention, the present invention provides a data processing system for a rail grinding scheme, comprising: an on-site intelligent computing platform, multiple 3D rail digitizers, and multiple on-site grinding machines; the on-site intelligent computing platform communicates with the 3D rail digitizers and the on-site grinding machines within the same wireless communication network; The on-site intelligent computing platform collects basic grinding data, imports and analyzes the global grinding plan based on the basic grinding data; according to the on-site measured continuous profile and grinding threshold, the entire section is divided into multiple small intervals that do not require operation and small intervals that require operation, and the grinding process parameters of the small intervals that require operation are generated; the grinding process parameters of the multiple small intervals that require operation are distributed to the on-site grinding machines. A small grinding machine is used for grinding operations on site; The 3D rail digitizer is used to collect the profile of the polished rail and upload it to the on-site digital computing platform. The on-site intelligent computing platform determines whether the rail is qualified based on the profile of the polished rail. If it is not qualified, it generates secondary polishing process parameters and sends them to the corresponding on-site polishing machine for re-polishing.

[0020] This solution enables intelligent and precise breakdown of grinding plans, real-time wireless distribution, multi-machine collaborative scheduling, precise execution of on-site operations, automatic quality verification, adaptive plan correction, and fully closed-loop intelligent operation, thereby improving the quality and efficiency of refined rail grinding and maintenance. Specifically, this is reflected in the following six aspects: 1. Intelligent and precise task breakdown significantly improves the standardization of operations. This solution uses a grid-based intelligent breakdown algorithm to break down the traditional extensive large-section overall solution into multiple independent small-scale operation intervals that require sub-meter level operation. This refined task is matched with the operating characteristics of small grinding machines, completely eliminating reliance on manual experience and effectively solving industry problems such as missed grinding, repeated grinding, and uneven grinding. It achieves unified operation standards and precise and controllable parameters for all intervals.

[0021] 2. Wireless real-time distribution and scheduling significantly improves multi-machine collaboration efficiency. A two-way wireless network transmission system is established to enable real-time distribution of task instructions and process parameters, as well as real-time feedback of equipment status and work progress. It supports intelligent collaboration of multiple terminals and anti-conflict scheduling, eliminating information deviations and delays caused by manual handover, greatly improving the efficiency of cluster operations, and adapting to large-scale, multi-area, and refined maintenance scenarios.

[0022] 3. Fully closed-loop operation management ensures precise and controllable grinding quality. A complete closed-loop system of "splitting-distribution-operation-inspection-verification-correction" is constructed, breaking the traditional open-loop operation mode. By using measured data to iteratively optimize grinding schemes and process parameters, dynamic calibration of grinding quality and closed-loop rectification of defects are achieved. The grinding pass rate and operation consistency are greatly improved, effectively inhibiting the recurrence of rail defects.

[0023] 4. Full-process digital traceability supports intelligent operation and maintenance iteration. It achieves full-scale data association and archiving of plans, operations, testing, equipment, and corrections, constructing a standardized digital maintenance ledger. This enables full-dimensional traceability and querying, while accumulating massive amounts of operating condition and process data to continuously optimize and refine models, achieving autonomous iterative upgrades of maintenance technology.

[0024] 5. Reduce reliance on manual labor and save maintenance costs. Significantly reduce reliance on the experience of senior operators, allowing newcomers to quickly learn standardized operations, reducing human error and rework costs. At the same time, precise grinding reduces excessive grinding wear on rails, extends rail service life, and significantly reduces the overall maintenance cost of the track.

[0025] 6. Adaptable to various working conditions and highly versatile. This solution is suitable for fine grinding operations on different track types, lines, and local defects of high-speed rail, conventional rail, and subway. It is compatible with various small portable grinding equipment, making it highly adaptable and widely applicable. Attached Figure Description

[0026] The above and other objects, features, and advantages of this application will become more apparent from the detailed description of exemplary embodiments with reference to the accompanying drawings. The drawings described below are merely some embodiments of this application, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort.

[0027] Figure 1 The diagram illustrates the steps of the data processing method for the intelligent rail grinding solution. Detailed Implementation

[0028] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the embodiments set forth herein; rather, they are provided so that this application will be thorough and complete, and will fully convey the concept of the exemplary embodiments to those skilled in the art. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted.

[0029] Furthermore, the described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. Numerous specific details are provided in the following description to give a thorough understanding of embodiments of this application. However, those skilled in the art will recognize that the technical solutions of this application can be practiced without one or more of the specific details, or other methods, components, apparatuses, steps, etc., can be employed. In other instances, well-known methods, apparatuses, implementations, or operations are not shown or described in detail to avoid obscuring various aspects of this application.

[0030] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0031] The flowcharts shown in the accompanying drawings are merely illustrative and do not necessarily include all content and operations / steps, nor do they necessarily have to be performed in the described order. For example, some operations / steps can be broken down, while others can be combined or partially combined; therefore, the actual execution order may change depending on the specific circumstances.

[0032] It should be understood that although the terms first, second, third, etc., may be used herein to describe various components, these components should not be limited by these terms. These terms are used to distinguish one component from another. Therefore, the first component discussed below may be referred to as the second component without departing from the teachings of this application. As used herein, the term "and / or" includes all combinations of any one and more of the associated listed items.

[0033] Those skilled in the art will understand that the accompanying drawings are merely schematic diagrams of exemplary embodiments, and the modules or processes in the drawings are not necessarily essential for implementing this application, and therefore cannot be used to limit the scope of protection of this application.

[0034] According to a first specific embodiment of the present invention, the present invention provides a data processing system for a rail intelligent grinding scheme, comprising: an on-site intelligent computing platform, multiple 3D rail digitizers and multiple on-site grinding machines; the multiple on-site grinding machines are distributed in multiple sections of the rail, and the on-site intelligent computing platform can wirelessly communicate with the 3D rail digitizers and the on-site grinding machines.

[0035] The on-site intelligent computing platform collects basic grinding data, imports and analyzes the global grinding plan based on the basic grinding data; based on the on-site measured continuous profile and grinding threshold, the on-site intelligent computing platform divides the entire section into multiple small intervals that do not require operation and small intervals that require operation, and generates grinding process parameters for the small intervals that require operation; the on-site intelligent computing platform distributes the grinding process parameters of multiple small intervals that require operation to the on-site grinding machines; achieving the beneficial effect of accurately locating the on-site operation intervals and greatly reducing the amount of grinding work.

[0036] A small grinding machine is used for grinding operations on site; The 3D rail digitizer is used to collect the profile of the polished rail and upload it to the on-site digital computing platform. The on-site intelligent computing platform determines whether the rail is qualified based on the profile of the polished rail. If it is not qualified, it generates secondary polishing process parameters and sends them to the corresponding on-site polishing machine for re-polishing.

[0037] According to a second specific embodiment of the present invention, the present invention provides a data processing method for a rail intelligent grinding scheme, which adopts the data processing system of the rail grinding scheme of the first specific embodiment.

[0038] This solution is a fully closed-loop intelligent operation method for the cluster operation of portable grinding machines in railways. It features intelligent disassembly, wireless network distribution, on-site adaptive grinding, real-time verification of work quality, and iterative correction of the solution. It is applicable to the fine-grained repair and grinding scenarios of rail defects such as local wear, corrugation, peeling, weld irregularities, and profile deviations in high-speed railways, conventional railways, and urban rail transit. It is especially suitable for the decentralized, multi-section, and fragmented maintenance operation conditions of manual-assisted small machines.

[0039] like Figure 1 As shown, it includes the following steps: S1: The on-site digital computing platform collects basic grinding data, imports and analyzes the global grinding plan based on the basic grinding data.

[0040] Basic data for grinding is collected using rail profile inspection equipment and defect inspection equipment.

[0041] The basic data for polishing includes: real-time continuous profile data of the target line section, defect type, defect location, polishing deviation value, line curve radius, and train traffic density.

[0042] The global polishing solution includes the target profile, maximum allowable polishing amount (polishing threshold), polishing quality threshold, operation time limit, and safety constraint standards. Through analysis, the core indicators of the global solution are extracted, and the structured and digital reconstruction of the global solution is completed, providing data support for subsequent intelligent decomposition.

[0043] S2: The on-site digital computing platform divides the entire section into multiple small intervals that do not require operation and small intervals that require operation based on the on-site measured continuous profile and grinding threshold, and generates grinding process parameters for the small intervals that require operation.

[0044] The on-site intelligent computing platform divides the entire grinding process into multiple work zones based on the overall grinding plan. This includes dividing continuous line sections into gridded work zones based on line mileage coordinates. These zones include areas requiring no grinding and areas requiring grinding. The size of the areas requiring grinding is adapted to the coverage range of a single grinding operation by the on-site grinding machine. A grid accuracy of 1m-5m is preferred. This achieves precise positioning of the on-site work zones, significantly reducing the amount of grinding work.

[0045] For example, if a 500-meter section of rail needs to be ground, the transformed digital grinding solution can be used to precisely divide the area into sections of 3 meters, 10 meters, 12 meters, 7 meters, and 9 meters, etc. The total length of the sections to be ground may be less than 50 meters. This can save a lot of work, improve the efficiency of small-machine grinding, reduce rail wear, and extend the overall service life of the rails.

[0046] The generated polishing process parameters include: By combining the measured profile deviations within each grid, the required grinding amount and severity level of defects in each small section are determined. Through intelligent big data comparison algorithms, the target profile is compared to calculate the step points where the grinding amount differs significantly along the line direction. The segmentation algorithm completes the precise segmentation of the section, dividing the global grinding plan into independent working small sections with discontinuous grids, and generating grinding depth, grinding angle, number of grinding passes, feed speed, working range, and quality qualification threshold for each small section.

[0047] Step S2 also includes: dividing the small areas requiring operation into three levels—urgent, important, and routine—based on profile deviation, severity of defects, and driving safety priority, completing task sorting, and avoiding problems such as missed grinding, repeated grinding, and overlapping areas.

[0048] S3: Establish a wireless communication network between the on-site digital computing platform and the 3D rail digital instrument and the on-site grinding machine.

[0049] A 4G / 5G+LoRa dual-mode wireless communication network was built to achieve bidirectional networking and binding between the on-site intelligent computing platform and all on-site grinding terminal units.

[0050] Step S3 also includes: the on-site grinding machine completing a self-check of the equipment status.

[0051] Before operation, each on-site grinding machine automatically completes a self-check, detecting core parameters such as the status of the grinding motor, attitude sensor, positioning module, communication module, and battery power. It then uploads the equipment's online status, available working conditions, and location information to the backend in real time. Based on the number of machines, their location, and working conditions, the backend dynamically matches the optimal work terminal with the grid area, achieving optimal allocation of equipment resources.

[0052] S4: The on-site digital computing platform distributes the grinding process parameters of multiple small areas that need to be operated to the on-site grinding machines.

[0053] The on-site intelligent computing platform matches the intelligently segmented, independent work areas within each grid with the on-site grinding machines. It then wirelessly distributes the refined grinding process parameters, work mileage range, quality standards, safety requirements, and work priorities of the corresponding grid to the corresponding on-site grinding machine terminals. Upon receiving the task, the terminal automatically stores it and displays a pop-up notification to the operator, simultaneously locking access to the work parameters. This prevents on-site personnel from arbitrarily modifying standard process parameters, ensuring standardized operations. For scenarios involving temporary changes in working conditions or urgent defect additions, the platform supports real-time task updates, dynamic reassignment, and the withdrawal of old tasks.

[0054] S5: On-site grinding machine performs grinding operations.

[0055] Step S5 also includes: sending the polishing process data back to the on-site intelligent computing platform, and issuing an early warning if the polishing behavior is in violation of regulations.

[0056] The operator arrives at the designated grid area with a small on-site grinding machine. The terminal displays the work position, grinding process parameters, and standard profile curve in real time. During the operation, the attitude sensor, speed sensor, and pressure sensor on the equipment collect process data such as grinding angle, grinding pressure, grinding wheel speed, operation time, and actual feed rate in real time. This data is transmitted back to the back-end platform in real time via wireless network. The back-end monitors the compliance of the operation throughout the process and provides real-time warnings and corrections for exceeding parameters or violating operating rules.

[0057] S6: The 3D rail digitizer collects the profile of the polished rail and uploads it to the on-site digital computing platform; the on-site digital computing platform determines whether the profile of the polished rail is qualified. If it is not qualified, proceed to step S7.

[0058] The on-site intelligent computing platform determines whether the rail is qualified based on the profile after grinding, including comparing the measured profile with the target profile. Calculate the profile deviation value, polishing residue, and GQI quality score; The calculated values ​​are used to determine whether the work quality level is qualified according to the maintenance standards.

[0059] Specifically, after the single-grid grinding operation is completed, a portable profile detection device is used to collect the continuously measured profile of the ground rail in real time, and the data is simultaneously uploaded to the on-site intelligent computing platform. The on-site intelligent computing platform automatically compares the measured profile with the target profile, calculates the profile deviation value, grinding residue, and GQI quality score, and determines the operation quality level according to maintenance standards: qualified, slightly deviated, or unqualified. It automatically identifies defects such as residual defects, insufficient grinding in certain areas, and over-grinding, generates a quality verification report, and accurately locates the problem area and deviation data.

[0060] S7: Generate secondary grinding process parameters and send them to the corresponding on-site grinding machine for re-grinding.

[0061] For work grids that fail verification or exhibit minor deviations, the backend automatically analyzes the causes of the deviations, distinguishing between issues such as unreasonable process parameters, non-standard operation, and abnormal equipment conditions. Based on the deviation data, the grinding process parameters for the corresponding intervals are adaptively corrected, optimizing the number of grinding passes, feed rate, and angle parameters to generate a secondary refined grinding plan. This plan is then wirelessly redistributed to the corresponding terminals for re-grinding until the profile quality meets the standards. Simultaneously, the deviation data and corrected parameters are entered into the process database to achieve iterative optimization of similar work conditions.

[0062] S8: Archive the data from steps S1 to S7.

[0063] After all grid tasks are completed and quality checks are passed, the backend integrates global plan data, intelligent splitting records, wireless distribution logs, equipment operation process data, quality inspection data, and plan correction records to form a complete digital archive of single-section grinding operations, which is automatically archived and stored. It supports multi-dimensional traceability queries by mileage, time, equipment, and defect type, while providing data support for subsequent maintenance of similar lines, realizing closed-loop management and technological iteration of the entire operation process.

[0064] The core innovation of this solution lies in: (1) Adaptive grid-based intelligent splitting innovation: The first intelligent splitting algorithm transforms the macro-grinding scheme of hundreds of meters section level into sub-meter level fine-grained tasks. Combining the location of rail defects, grinding amount threshold, equipment operating radius, and track conditions, it automatically completes grid-based task segmentation, grinding parameter matching, and priority classification, completely replacing manual subjective splitting and achieving precise task adaptation to the operating characteristics of small machinery.

[0065] (2) Innovation of multi-terminal wireless dynamic distribution and collaborative scheduling: A cloud-terminal two-way wireless network transmission system is built to realize the real-time wireless distribution of grinding tasks, process parameters and standard specifications. At the same time, the status of on-site equipment, operation progress and location information are collected in real time to realize intelligent scheduling and dynamic task adjustment of multiple small grinding machines to prevent conflicts, and solve the problems of disordered operation and interference overlap of multiple machines.

[0066] (3) Innovation of closed-loop operation control throughout the whole process: Construct a complete closed-loop operation logic of "scheme splitting - wireless distribution - on-site operation - real-time detection - quality verification - deviation analysis - scheme correction - secondary operation", break the traditional open-loop operation mode, and optimize the polishing process and task scheme through reverse iteration of measured profile data to achieve dynamic control of operation quality.

[0067] (4) Data-driven adaptive correction innovation: Based on the profile deviation before and after grinding, the residual state of the disease, and the equipment operating parameters, a working condition-process matching database is established to automatically correct the core parameters such as the number of grinding passes, feed rate, and grinding angle for different lines and different types of diseases, so as to achieve personalized and precise grinding and significantly reduce the disease recurrence rate.

[0068] (5) Full-dimensional digital traceability innovation: realize the full retention and association of scheme data, operation data, test data and equipment data, form a traceable, statistical and iterative digital maintenance ledger, and adapt to the construction needs of the smart railway digital operation and maintenance system.

[0069] This solution enables intelligent and precise breakdown of grinding plans, real-time wireless distribution, multi-machine collaborative scheduling, precise execution of on-site operations, automatic quality verification, adaptive plan correction, and fully closed-loop intelligent operation, thereby improving the quality and efficiency of refined rail grinding and maintenance. Specifically, this is reflected in the following six aspects: 1. Intelligent and precise task breakdown significantly improves the standardization of operations. This solution uses a grid-based intelligent breakdown algorithm to break down the traditional extensive, hundreds-of-meters-level overall solution into sub-meter-level fine-grained tasks, matching the operating characteristics of small grinding machines. It completely eliminates reliance on manual experience and effectively solves industry problems such as missed grinding, repeated grinding, and uneven grinding, achieving unified operation standards and precise and controllable parameters for all sections.

[0070] 2. Wireless real-time distribution and scheduling significantly improves multi-machine collaboration efficiency. A two-way wireless network transmission system is established to enable real-time distribution of task instructions and process parameters, as well as real-time feedback of equipment status and work progress. It supports intelligent collaboration of multiple terminals and anti-conflict scheduling, eliminating information deviations and delays caused by manual handover, greatly improving the efficiency of cluster operations, and adapting to large-scale, multi-area, and refined maintenance scenarios.

[0071] 3. Fully closed-loop operation management ensures precise and controllable grinding quality. A complete closed-loop system of "splitting-distribution-operation-inspection-verification-correction" is constructed, breaking the traditional open-loop operation mode. By using measured data to iteratively optimize grinding schemes and process parameters, dynamic calibration of grinding quality and closed-loop rectification of defects are achieved. The grinding pass rate and operation consistency are greatly improved, effectively inhibiting the recurrence of rail defects.

[0072] 4. Full-process digital traceability supports intelligent operation and maintenance iteration. It achieves full-scale data association and archiving of plans, operations, testing, equipment, and corrections, constructing a standardized digital maintenance ledger. This enables full-dimensional traceability and querying, while accumulating massive amounts of operating condition and process data to continuously optimize and refine models, achieving autonomous iterative upgrades of maintenance technology.

[0073] 5. Reduce reliance on manual labor and save maintenance costs. Significantly reduce reliance on the experience of senior operators, allowing newcomers to quickly learn standardized operations, reducing human error and rework costs. At the same time, precise grinding reduces excessive grinding wear on rails, extends rail service life, and significantly reduces the overall maintenance cost of the track.

[0074] 6. Adaptable to various working conditions and highly versatile. This solution is suitable for fine grinding operations on different track types, lines, and local defects of high-speed rail, conventional rail, and subway. It is compatible with various small portable grinding equipment, making it highly adaptable and widely applicable.

[0075] Exemplary embodiments of the present invention have been specifically shown and described above. It should be understood that the present invention is not limited to the detailed structures, arrangements, or implementations described herein; rather, the present invention is intended to cover various modifications and equivalent arrangements contained within the spirit and scope of the appended claims.

Claims

1. A data processing method for a steel rail numerical intelligent grinding scheme, characterized in that, Includes the following steps: S1: The on-site digital computing platform collects basic polishing data, imports and analyzes the global polishing plan based on the basic polishing data; S2: The on-site digital computing platform divides the entire section into multiple small sections that do not require operation and small sections that require operation based on the on-site measured continuous profile and grinding threshold, and generates grinding process parameters for the small sections that require operation. S3: Establish a wireless communication network between the on-site digital computing platform and the 3D rail digital instrument and the on-site grinding machine; S4: The on-site digital computing platform distributes the grinding process parameters of multiple small areas that need to be operated to the on-site grinding machines; S5: On-site grinding machine performs grinding operations; S6: The 3D rail digitizer collects the profile of the polished rail and uploads it to the on-site digital computing platform; the on-site digital computing platform determines whether the profile of the polished rail is qualified. If it is not qualified, proceed to step S7. S7: Generate secondary grinding process parameters and send them to the corresponding on-site grinding machine for re-grinding.

2. The data processing method for smart rail grinding scheme of claim 1, wherein, It also includes: S8: Archive the data from steps S1 to S7.

3. The data processing method for smart rail grinding scheme of claim 1, wherein, In step S1, the basic data for polishing includes: Measured profile data, defect types, defect locations, grinding deviation values, curve radii, and train traffic density of the target line section; The global polishing scheme includes target profile parameters, maximum allowable polishing amount, polishing quality threshold, operation time limit, and safety constraint standards.

4. The data processing method for smart rail grinding scheme of claim 1, wherein, In step S2, the size of the small area to be worked on is adapted to the coverage range of a single operation of the on-site grinding machine.

5. The data processing method for smart rail grinding solution according to claim 1, wherein, In step S2, the generated polishing process parameters include: The global grinding scheme is divided into discontinuous gridded independent work intervals, generating single interval grinding depth, grinding angle, number of grinding passes, feed rate, work range, and quality pass threshold.

6. The data processing method of smart rail grinding scheme according to claim 1, wherein, Step S2 also includes: dividing the small areas that need to be done into three levels: urgent, important, and routine, and sorting the tasks accordingly.

7. The data processing method for smart rail grinding solution of claim 1, wherein, Step S3 also includes: the on-site grinding machine completing a self-check of the equipment status.

8. The data processing method for smart rail grinding solution of claim 1, wherein, Step S5 also includes: sending the polishing process data back to the on-site intelligent computing platform, and issuing an early warning if the polishing behavior is in violation of regulations.

9. The data processing method for smart rail grinding solution according to claim 1, wherein, In step S6, the on-site intelligent computing platform determines whether the rail profile is qualified based on the polished profile, including: the on-site intelligent computing platform compares the measured profile with the target profile; Calculate the profile deviation value, polishing residue, and GQI quality score; The calculated values ​​are used to determine whether the work quality level is qualified according to the maintenance standards.

10. A data processing system for a rail grinding plan, characterized in that include: On-site digital computing platform, multiple 3D rail digitizers, and multiple on-site grinding machines; The on-site intelligent computing platform communicates with the 3D rail digital instrument and the on-site grinding machine within the same wireless communication network. The on-site digital computing platform collects basic polishing data, imports and analyzes the global polishing plan based on the basic polishing data; Based on the on-site measured continuous profile and grinding threshold, the entire section is divided into multiple small sections that do not require operation and small sections that require operation, and grinding process parameters for the small sections that require operation are generated; the grinding process parameters for the multiple small sections that require operation are then distributed to the on-site grinding machines. A small grinding machine is used for grinding operations on site; The 3D rail digitizer is used to collect the profile of the polished rail and upload it to the on-site digital computing platform. The on-site intelligent computing platform determines whether the rail is qualified based on the profile of the polished rail. If it is not qualified, it generates secondary polishing process parameters and sends them to the corresponding on-site polishing machine for re-polishing.

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