Rapid monitoring and evaluation system and method for railway freight operation state junction point

Abstract

The invention discloses a rapid monitoring and evaluation system and method for a railway freight operation state junction point, and relates to the technical field of railway freight, and the method comprises the steps: collecting cargo load distribution data and vehicle dynamic operation state data in real time, and carrying out the preprocessing; on the basis of the preprocessed data, a cargo unbalance loading prediction model is constructed, dynamic prediction is conducted on cargo unbalance loading risks in combination with train operation condition data, and an unbalance loading prediction result and an unbalance loading adjustment suggestion are generated; making a personalized preventive maintenance plan based on a carriage health state evaluation result; the unbalance loading prediction result and the vehicle running state data are associated with the digital identity information of the compartment to form a dynamic state management mode of the full life cycle of the compartment, so that the system can comprehensively evaluate the health state of the compartment and generate a dynamic adjustment scheme, and intelligent monitoring and accurate maintenance of the state of the compartment are realized.

Description

Technical Field

[0001] The invention relates to the technical field of railway freight transport, in particular to a system and method for quickly monitoring and evaluating a handover point of railway freight transport operation status. Background Art

[0002] In recent years, the railway freight industry has developed rapidly, and intelligent monitoring and evaluation technologies have gradually become an important means to improve transportation safety and efficiency. Traditional monitoring methods rely on static detection equipment and manual inspections. Although they can detect abnormal cargo loading to a certain extent, they have problems such as long monitoring cycles, poor real-time performance, and limited coverage, making it difficult to meet the needs of modern railway freight for efficiency, accuracy, and intelligence. In particular, existing technologies are relatively limited in predicting the risk of cargo overloading, and usually only conduct a one-time assessment after loading is completed, lacking the ability to monitor dynamic overloading conditions in real time during train operation. Carriage status management is mostly focused on regular maintenance and post-accident repair, and fails to provide an intelligent monitoring and dynamic update mechanism for the entire life cycle.

[0003] The main deficiencies of existing technologies are reflected in two aspects: first, the lack of real-time monitoring and integrated analysis of cargo load distribution and vehicle dynamic operation status makes it difficult to timely discover and deal with the problem of cargo overloading during train operation; second, the existing carriage management system is difficult to achieve intelligent management of the carriage status throughout the life cycle, and cannot accurately evaluate and dynamically adjust the operating health status of the carriage. These problems not only affect the safety and efficiency of railway transportation, but also increase operating costs and maintenance complexity. Summary of the invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides a method for rapid monitoring and evaluation of railway freight operation status at the handover point to solve the problem of lack of real-time monitoring and fusion analysis of cargo load distribution and vehicle dynamic operation status in the prior art.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, the present invention provides a method for rapid monitoring and evaluation at a railway freight operation status handover point, which includes real-time collection of cargo load distribution data and vehicle dynamic operation status data, and preprocessing;

[0008] Based on the preprocessed data, a cargo overloading prediction model is constructed, and the cargo overloading risk is dynamically predicted in combination with the train operation condition data, generating overloading prediction results and overloading adjustment suggestions;

[0009] The load imbalance prediction results, load imbalance adjustment suggestions, cargo load distribution data and vehicle dynamic operation status data are associated with the digital identity information of the carriage, the full life cycle status information of the carriage is updated, and the carriage health status assessment results are generated;

[0010] Develop a personalized preventive maintenance plan based on the car health status assessment results.

[0011] As a preferred solution of the method for rapid monitoring and evaluation of the railway freight operation status at the handover point of the present invention, wherein: the cargo load distribution data includes the cargo weight at the handover point and the center of gravity position of the cargo;

[0012] The vehicle dynamic operation status data includes the truck speed, truck acceleration and truck vibration intensity at the junction point;

[0013] The preprocessing includes outlier detection and removal, noise filtering and data formatting.

[0014] As a preferred solution of the method for rapid monitoring and evaluation of the railway freight operation status at the handover point of the present invention, the cargo overloading index is defined based on the cargo weight and the displacement of the center of gravity of the cargo, and the real-time adjustment information of the rapid monitoring at the handover point is considered, and the expression is:

[0015]

[0016] Where P represents the cargo overload index, i represents the measurement point index, N represents the total number of measurement points, and W i represents the cargo weight at the i-th measurement point, W avg represents the average weight of all measurement points, η represents the proportionality coefficient that quantifies the effect of center of gravity movement on the degree of eccentric loading, and ΔG represents the displacement of the center of gravity;

[0017] The speed sensitivity factor is introduced to evaluate the load eccentricity risk caused by speed change. The expression is:

[0018]

[0019] Where I represents the speed sensitivity factor, v represents the current truck speed, v0 represents the reference speed, σ v Represents the standard deviation of speed fluctuation.

[0020] As a preferred solution of the method for rapid monitoring and evaluation of the railway freight operation status handover point of the present invention, the comprehensive overloading risk index is calculated by comprehensively considering the overloading degree index of the cargo, the speed sensitivity factor, the acceleration and the vibration intensity of the freight car, and the expression is:

[0021]

[0022] Among them, R represents the comprehensive offloading risk index, α represents the speed influence coefficient, a represents the acceleration of the freight car, β represents the acceleration influence coefficient, V represents the vibration intensity of the freight car, and γ represents the vibration intensity influence coefficient.

[0023] As a preferred solution of the rapid monitoring and evaluation method at the handover point of the railway freight operation status described in the present invention, wherein: based on the results of rapid monitoring at the handover point and the offloading prediction results, generating offloading adjustment suggestions includes the following steps.

[0024] Define the offloading risk thresholds T1 and T2, and determine whether there is an offloading risk, where T1 is the low offloading risk threshold and T2 is the medium offloading risk threshold.

[0025] When R ≤ T1, it indicates that the handover point monitoring shows slight offloading, and it is recommended to slightly adjust the cargo position to make the center of gravity closer to the center line of the carriage.

[0026] When T1 < R ≤ T2, it indicates that the handover point monitoring shows moderate offloading, and it is recommended to redistribute the cargo weight, reduce the unilateral load, and adjust the overall distribution.

[0027] When R > T2, it indicates that the handover point monitoring shows severe offloading, stop the train, conduct a comprehensive inspection and adjust the cargo configuration, and continue to drive after ensuring safety.

[0028] As a preferred solution of the rapid monitoring and evaluation method at the handover point of the railway freight operation status described in the present invention, wherein: associate the offloading prediction results, offloading adjustment suggestions, cargo load distribution data, and vehicle dynamic operation status data with the digital identity information of the carriage, update the full life cycle status information of the carriage, and generate the carriage health status evaluation result includes the following steps.

[0029] Assign a unique identifier to each carriage, associate the monitoring data at the handover point, and create a corresponding database entry for each carriage.

[0030] According to the offloading prediction results and adjustment suggestions in the database, update the status fields of the carriage, introduce a comprehensive health index, quantify the health status of the carriage, and generate the carriage health status evaluation result. The expression is:

[0031]

[0032] Among them, H represents the comprehensive health index, λ represents the sensitivity coefficient, S0 represents the maximum acceptable offloading risk level, δ represents the influence proportional coefficient of maintenance and faults on the health index, F j represents the influence factor of the jth maintenance times, and M represents the total number of maintenance times.

[0033] As a preferred solution of the method for rapid monitoring and evaluation of the railway freight operation status handover point of the present invention, wherein: based on the carriage health status evaluation result, formulating a personalized preventive maintenance plan includes the following steps:

[0034] According to the carriage health status assessment results, risk levels are classified and health status assessment thresholds are set;

[0035] Combined with the historical fault records and health status assessment results of the carriage, the maintenance cycle is introduced and the expression is:

[0036]

[0037] Among them, Z m represents the recommended maintenance cycle, Z0 represents the standard maintenance cycle, and k represents the influence coefficient of adjusting the maintenance frequency with the change of health status;

[0038] Based on the maintenance cycle recommended by the monitoring results at the handover point, observe whether the comprehensive health index within the recommended maintenance cycle is lower than the health status assessment threshold. When the comprehensive health index is lower than the health status assessment threshold, the replacement warning is automatically triggered and the replacement operation is performed.

[0039] In a second aspect, the present invention provides a rapid monitoring and evaluation system for railway freight operation status at a handover point, comprising:

[0040] Data acquisition module, which collects cargo load distribution data and vehicle dynamic operation status data in real time and performs preprocessing;

[0041] The load imbalance prediction module builds a cargo load imbalance prediction model based on preprocessed data, dynamically predicts cargo load imbalance risk in combination with train operation condition data, and generates load imbalance prediction results and load imbalance adjustment suggestions;

[0042] The status assessment module associates the load imbalance prediction results, load imbalance adjustment suggestions, cargo load distribution data and vehicle dynamic operation status data with the digital identity information of the carriage, updates the full life cycle status information of the carriage, and generates the health status assessment results of the carriage;

[0043] The personalized maintenance module formulates a personalized preventive maintenance plan based on the car health status assessment results.

[0044] In a third aspect, the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, any step of the method for rapid monitoring and evaluation of the railway freight operation status handover point as described in the first aspect of the present invention is implemented.

[0045] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements any step of the method for rapid monitoring and evaluation of the railway freight operation status handover point as described in the first aspect of the present invention.

[0046] The beneficial effects of the present invention are as follows: by collecting cargo load distribution and vehicle dynamic operating status data in real time and preprocessing the data, the problem of insufficient real-time monitoring of cargo overloading in the prior art is solved, and overloading information can be dynamically obtained during train operation to ensure the accuracy and timeliness of monitoring data; the cargo overloading prediction model based on the deep learning algorithm is combined with the train operating condition data to dynamically predict the overloading risk and generate adjustment suggestions, breaking through the limitations of traditional static monitoring, and can identify and deal with potential overloading problems in advance during train operation, thereby improving the safety and operation efficiency of railway freight; by associating the overloading prediction results and vehicle operating status data with the digital identity information of the carriage, a dynamic status management mode for the entire life cycle of the carriage is formed, which can comprehensively evaluate the health status of the carriage and generate dynamic adjustment plans, thereby realizing intelligent monitoring and precise maintenance of the carriage status. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0048] Figure 1 This is a flow chart of the method for rapid monitoring and evaluation of the railway freight operation status handover point in Example 1.

[0049] Figure 2 This is a module diagram of the rapid monitoring and evaluation system at the handover point of the railway freight operation status in Example 1. DETAILED DESCRIPTION

[0050] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0051] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0052] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0053] Example 1, reference Figure 1 and Figure 2 , which is the first embodiment of the present invention, provides a method for rapid monitoring and evaluation of railway freight operation status at a handover point, comprising the following steps:

[0054] S1. Collect cargo load distribution data and vehicle dynamic operation status data in real time and perform preprocessing.

[0055] S1.1. The cargo load distribution data includes the cargo weight and the center of gravity position of the cargo at the handover point; the vehicle dynamic operation status data includes the truck speed, truck acceleration and truck vibration intensity at the handover point.

[0056] Specifically, the weight of the cargo at each point is measured using pressure sensors installed under the carriage floor or on the load-bearing beams, which can provide accurate weight readings.

[0057] The acceleration and vibration intensity of the freight train are captured using three-dimensional accelerometers installed at key locations on the carriage to evaluate the dynamic behavior of the train during operation.

[0058] The center of gravity of the cargo is determined by a gyroscope, which can accurately detect the center of gravity deviation, especially when the cargo is unevenly loaded.

[0059] S1.2. Preprocessing includes outlier detection and removal, noise filtering and data formatting.

[0060] Common methods for outlier detection and removal include the Z-score method, which calculates the standard score for each data point and sets a threshold. Data points exceeding this threshold are considered outliers and deleted. The box plot method can also be used to define the boundaries of outliers using the upper and lower quartiles based on the distribution of the data, thereby effectively identifying and removing extreme values.

[0061] Noise filtering is usually performed using low-pass filters and Kalman filters. Low-pass filters are suitable for smoothing high-frequency noise and keeping the main characteristics of the signal unchanged. Kalman filters can effectively eliminate random noise and are particularly suitable for data processing in dynamic environments.

[0062] Formatting refers to unifying the data collected by different sensors into the same unit, such as converting all weight data into kilograms (kg), and organizing the data according to a predetermined data structure (such as CSV, JSON, etc.) to facilitate subsequent query and analysis.

[0063] S2. Based on the preprocessed data, a cargo overloading prediction model is constructed, and the cargo overloading risk is dynamically predicted in combination with the train operation condition data to generate overloading prediction results and overloading adjustment suggestions.

[0064] S2.1. Based on the cargo weight and the displacement of the cargo center of gravity, define the cargo overloading index, and consider the real-time adjustment information of the fast monitoring at the handover point. The expression is:

[0065]

[0066] Where P represents the cargo overload index, i represents the measurement point index, N represents the total number of measurement points, and W i represents the cargo weight at the i-th measurement point, W avg represents the average weight of all measurement points, η represents the proportional coefficient that quantifies the effect of center of gravity movement on the degree of eccentric loading, and ΔG represents the displacement of the center of gravity.

[0067] The speed sensitivity factor is introduced to evaluate the load eccentricity risk caused by speed change. The expression is:

[0068]

[0069] Where I represents the speed sensitivity factor, v represents the current truck speed, v0 represents the reference speed, σ v Represents the standard deviation of speed fluctuation.

[0070] Specifically, the reference speed can be obtained by calculating the average speed of various cargo transportation under normal circumstances. When the speed is close to the reference speed, I is close to 1; when the speed deviates greatly from the reference speed, I decreases, reflecting a higher risk.

[0071] It should be noted that the deviation quantification clearly reflects the weight difference at each measuring point, which helps to identify the specific location that may cause uneven loading.

[0072] S2.2. The comprehensive overloading risk index is calculated by combining the cargo overloading degree index, speed sensitivity factor, acceleration and vibration intensity of the truck. The expression is:

[0073]

[0074] Among them, R represents the comprehensive offloading risk index, α represents the speed influence coefficient, a represents the acceleration of the freight car, β represents the acceleration influence coefficient, V represents the vibration intensity of the freight car, and γ represents the vibration intensity influence coefficient.

[0075] It should be noted that by comprehensively considering multiple parameters, a comprehensive risk assessment system is formed to ensure the comprehensiveness and accuracy of the assessment results. By adjusting each coefficient, the influence of different factors on the final risk index can be flexibly controlled to adapt to different application scenarios.

[0076] S2.3. Generate offloading adjustment suggestions based on the results of rapid monitoring at the handover point and the offloading prediction results.

[0077] Define the offloading risk thresholds T1 and T2 to determine whether there is an offloading risk, where T1 is the low offloading risk threshold and T2 is the medium offloading risk threshold;

[0078] When R ≤ T1, it indicates that the handover point monitoring shows slight offloading. It is recommended to slightly adjust the cargo position to make the center of gravity closer to the center line of the carriage;

[0079] When T1 < R ≤ T2, it indicates that the handover point monitoring shows medium offloading. It is recommended to redistribute the cargo weight, reduce the unilateral load, and adjust the overall distribution;

[0080] When R > T2, it indicates that the handover point monitoring shows severe offloading. Immediately stop the train, conduct a comprehensive inspection, adjust the cargo configuration, and continue to drive after ensuring safety.

[0081] It should be noted that by setting different offloading risk thresholds, hierarchical management of offloading risks is achieved, improving the pertinence and effectiveness of response measures; corresponding adjustment measures are taken according to different risk levels to ensure that problems can be solved in the first time and transportation safety is guaranteed.

[0082] S3. Associate the offloading prediction results, offloading adjustment suggestions, cargo load distribution data, and vehicle dynamic operation state data with the digital identity information of the carriage, update the full life cycle state information of the carriage, and generate the carriage health state assessment results.

[0083] S3.1. Assign a unique identifier to each carriage, associate the monitoring data at the handover point, and create a corresponding database entry for each carriage.

[0084] Specifically, the database design should include but not be limited to the following fields:

[0085] Basic carriage information: such as model, production date, manufacturer, etc.

[0086] Real-time monitoring data: including cargo load distribution data, vehicle dynamic operation state data, etc.

[0087] Historical records: such as past eccentric load prediction results, adjustment suggestions, maintenance records, etc.

[0088] Status fields: such as current health index, date of last checkup, etc.

[0089] It should be noted that by assigning a unique identifier to each carriage, the data record of each carriage is ensured to be unique, data confusion and incorrect association are avoided, subsequent data query and maintenance work is facilitated, and management efficiency is improved.

[0090] S3.2. Update the state field of the carriage according to the load eccentricity prediction results and adjustment suggestions in the database, introduce the comprehensive health index, quantify the health state of the carriage, and generate the carriage health state assessment result, which is expressed as:

[0091]

[0092] Where H represents the comprehensive health index, λ represents the sensitivity coefficient, S0 represents the maximum acceptable risk level of eccentric load, δ represents the proportional coefficient of the impact of maintenance and failure on the health index, and F j represents the influencing factor of the j-th maintenance times, and M represents the total maintenance times.

[0093] Specifically, the carriage health status assessment results include but are not limited to:

[0094] Current health score: Displays the specific value of the comprehensive health index.

[0095] Trend analysis: Plot historical health index change curves to help identify long-term trends.

[0096] Maintenance Recommendations: Provides specific maintenance recommendations based on the assessment results, such as replacing parts or adjusting cargo configuration.

[0097] S4. Develop a personalized preventive maintenance plan based on the car health status assessment results.

[0098] According to the carriage health status assessment results, risk levels are classified and health status assessment thresholds are set;

[0099] Combined with the historical fault records and health status assessment results of the carriage, the maintenance cycle is introduced and the expression is:

[0100]

[0101] Among them, Z mIt represents the recommended maintenance cycle, Z0 represents the standard maintenance cycle (collecting and analyzing the historical fault records of the car body, understanding the frequencies and causes of various faults, and determining the time and frequency required for each type of maintenance through the records of previous maintenance work), and k represents the influence coefficient for adjusting the change of the maintenance frequency with the health status;

[0102] Based on the recommended maintenance cycle, observe whether the comprehensive health index within the recommended maintenance cycle is lower than the health status assessment threshold. When the comprehensive health index is lower than the health status assessment threshold, automatically trigger a replacement warning and perform the replacement operation.

[0103] Specifically, according to the assessment result H of the car body health status, the car bodies are divided into different risk levels. The specific classification is as follows:

[0104] Low risk (green): H > 0.8, indicating that the car body is in good condition, with a relatively low offloading risk and less maintenance requirements.

[0105] Medium risk (yellow): 0.5 < H ≤ 0.8, indicating that there is a certain risk in the car body, and attention needs to be paid and appropriate measures need to be taken.

[0106] High risk (red): H ≤ 0.5, indicating that there is a relatively high risk in the car body, and immediate actions need to be taken for maintenance or adjustment.

[0107] When the comprehensive health index is lower than the health status assessment threshold, automatically trigger a replacement warning to remind relevant personnel to prepare corresponding spare parts and arrange the replacement operation. Specifically, it includes:

[0108] Generate a warning notice: Automatically generate a detailed warning report, including the car body numbers affected, the components to be replaced, and the recommended operation steps.

[0109] Task assignment: Send the warning information to the relevant maintenance team and mark it as a priority item in the management system.

[0110] Perform the replacement operation: Complete the replacement operation in a timely manner according to the预定 schedule and operation guide to ensure that the car body returns to a safe operating state.

[0111] This embodiment also provides a rapid monitoring and assessment system at the handover point of the railway freight operation status, including:

[0112] The data acquisition module collects cargo load distribution data and vehicle dynamic operation status data in real time and performs preprocessing. The overload prediction module builds a cargo overload prediction model based on the preprocessed data, dynamically predicts the risk of cargo overload in combination with the train operation condition data, and generates overload prediction results and overload adjustment suggestions. The status assessment module associates the overload prediction results, overload adjustment suggestions, cargo load distribution data and vehicle dynamic operation status data with the digital identity information of the carriage, updates the full life cycle status information of the carriage, and generates the health status assessment results of the carriage. The personalized maintenance module formulates a personalized preventive maintenance plan based on the health status assessment results of the carriage.

[0113] This embodiment also provides a computer device, which is suitable for the method of rapid monitoring and evaluation at the handover point of railway freight operation status, including: a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement the method of rapid monitoring and evaluation at the handover point of railway freight operation status proposed in the above embodiment.

[0114] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0115] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, it implements the method for quickly monitoring and evaluating the railway freight operation status at the handover point as proposed in the above embodiment; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, referred to as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, referred to as EEPROM), erasable programmable read-only memory (Erasable Programmable Read Only Memory, referred to as EPROM), programmable read-only memory (Programmable Red-Only Memory, referred to as PROM), read-only memory (Read-Only Memory, referred to as ROM), magnetic storage, flash memory, disk or optical disk.

[0116] In summary, the present invention solves the problem of insufficient real-time monitoring of cargo overloading in the prior art by collecting cargo load distribution and vehicle dynamic operating status data in real time and preprocessing the data. It can dynamically obtain overloading information during train operation to ensure the accuracy and timeliness of monitoring data. The cargo overloading prediction model based on the deep learning algorithm is combined with the train operating condition data to dynamically predict the overloading risk and generate adjustment suggestions, breaking through the limitations of traditional static monitoring. It can identify and deal with potential overloading problems in advance during train operation, thereby improving the safety and operation efficiency of railway freight. By associating the overloading prediction results and vehicle operating status data with the digital identity information of the carriage, a dynamic status management mode for the entire life cycle of the carriage is formed, which can comprehensively evaluate the health status of the carriage and generate dynamic adjustment plans, thereby realizing intelligent monitoring and precise maintenance of the carriage status.

[0117] Example 2, referring to Table 1, is the second example of the present invention. To further verify the technical solution of the present invention, experimental simulation data of a method for rapid monitoring and evaluation at a handover point of railway freight operation status are provided.

[0118] The prediction and adjustment of cargo overloading risk under different load distribution and train operation conditions were simulated. Two freight trains were selected for comparative testing, one of which was managed in a traditional way (control group) and the other was managed by the intelligent monitoring and evaluation method of the present invention (experimental group). Each train consists of 10 carriages, loaded with cargo of different types and weights.

[0119] The details are shown in Table 1 below:

[0120] Table 1 Experimental data record table

[0121]

[0122]

[0123] In the control group, due to the lack of real-time monitoring methods, the overloading risk index of each carriage was generally high, ranging from 0.72 to 0.78.

[0124] After the experimental group adopted the intelligent monitoring and evaluation management of the present invention, the overload risk index decreased significantly, with the lowest value dropping to 0.55 and the highest value being only 0.60. This shows that the present invention can effectively identify and reduce potential overload problems and improve transportation safety.

[0125] The health index of the control group was lower, ranging from 0.62 to 0.68, reflecting the poor health of the carriages under the traditional management model.

[0126] The health index of the experimental group was significantly improved, reaching 0.82 to 0.88. This is due to the refined management of the carriage's full life cycle status information, which keeps the carriage in a better working condition and reduces the possibility of failure.

[0127] Under the same conditions of total cargo weight, average speed, maximum acceleration and vibration intensity, the experimental group not only achieved a lower risk of overloading, but also maintained a higher level of health. This means that even in the face of complex operating environments, the train can run smoothly and safely, reducing the risk caused by overloading.

[0128] Based on the results of the carriage health assessment, the experimental team developed a more scientific and reasonable preventive maintenance plan, avoiding unnecessary regular inspections and repairs while also promptly addressing possible problems. This precise maintenance strategy effectively extended the service life of the carriage and reduced the number and cost of repairs.

[0129] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for rapid monitoring and evaluation of railway freight operation status at a handover point, characterized in that: include, Collect cargo load distribution data and vehicle dynamic operation status data in real time and perform pre-processing; Based on the preprocessed data, a cargo overloading prediction model is constructed, and the cargo overloading risk is dynamically predicted in combination with the train operation condition data, generating overloading prediction results and overloading adjustment suggestions; The load imbalance prediction results, load imbalance adjustment suggestions, cargo load distribution data and vehicle dynamic operation status data are associated with the digital identity information of the carriage, the full life cycle status information of the carriage is updated, and the carriage health status assessment results are generated; Develop a personalized preventive maintenance plan based on the car health status assessment results.

2. The method for rapid monitoring and evaluation of railway freight operation status at a handover point according to claim 1, characterized in that: The cargo load distribution data includes the cargo weight at the handover point and the center of gravity position of the cargo; The vehicle dynamic operation status data includes the truck speed, truck acceleration and truck vibration intensity at the junction point; The preprocessing includes outlier detection and removal, noise filtering and data formatting.

3. The method for rapid monitoring and evaluation of railway freight operation status at a handover point according to claim 2, characterized in that: Based on the cargo weight and the displacement of the cargo center of gravity, the cargo overload degree index is defined, and the real-time adjustment information of the rapid monitoring at the handover point is considered. The expression is: Where P represents the cargo overload index, i represents the measurement point index, N represents the total number of measurement points, and W i represents the cargo weight at the i-th measurement point, W avg represents the average weight of all measurement points, η represents the proportionality coefficient that quantifies the effect of center of gravity movement on the degree of eccentric loading, and ΔG represents the displacement of the center of gravity; The speed sensitivity factor is introduced to evaluate the load eccentricity risk caused by speed change. The expression is: Where I represents the speed sensitivity factor, v represents the current truck speed, v0 represents the reference speed, σ v Represents the standard deviation of speed fluctuation.

4. The method for rapid monitoring and evaluation of railway freight operation status at a handover point according to claim 3, characterized in that: The comprehensive overload risk index is calculated by combining the cargo overload degree index, speed sensitivity factor, acceleration and vibration intensity of the truck. The expression is: Among them, R represents the comprehensive overload risk index, α represents the speed influence coefficient, a represents the acceleration of the truck, β represents the acceleration influence coefficient, V represents the vibration intensity of the truck, and γ represents the vibration intensity influence coefficient.

5. The method for rapid monitoring and evaluation of railway freight operation status at a handover point according to claim 4, characterized in that: Based on the results of rapid monitoring at the junction and the eccentric load prediction results, generating eccentric load adjustment suggestions includes the following steps: Define the overloading risk thresholds T1 and T2 to determine whether there is an overloading risk, where T1 is a low overloading risk threshold and T2 is a medium overloading risk threshold; When R≤T1, it means that the handover point monitoring shows slight overloading, and it is recommended to fine-tune the position of the cargo so that the center of gravity is close to the center line of the carriage; When T1<R≤T2, it means that the monitoring of the handover point shows moderate overloading. It is recommended to redistribute the cargo weight, reduce the load on one side, and adjust the overall distribution; When R>T2, it means that the monitoring at the handover point shows serious overloading. The train should be stopped for a comprehensive inspection and adjustment of the cargo configuration, and then continue to run after it is safe.

6. The method for rapid monitoring and evaluation of railway freight operation status at a handover point according to claim 5, characterized in that: The following steps are included to associate the load imbalance prediction results, load imbalance adjustment suggestions, cargo load distribution data and vehicle dynamic operation status data with the digital identity information of the carriage, update the full life cycle status information of the carriage, and generate the carriage health status assessment results: Assign a unique identifier to each car, associate the monitoring data at the handover point, and create a corresponding database entry for each car; According to the load eccentricity prediction results and adjustment suggestions in the database, the state field of the carriage is updated, and the comprehensive health index is introduced to quantify the health status of the carriage and generate the carriage health status assessment result. The expression is: Where H represents the comprehensive health index, λ represents the sensitivity coefficient, S0 represents the maximum acceptable risk level of eccentric load, δ represents the proportional coefficient of the impact of maintenance and failure on the health index, and F j represents the influencing factor of the j-th maintenance times, and M represents the total maintenance times.

7. The method for rapid monitoring and evaluation of railway freight operation status at a handover point according to claim 6, characterized in that: Based on the results of the car health assessment, a personalized preventive maintenance plan is developed, which includes the following steps: According to the carriage health status assessment results, risk levels are classified and health status assessment thresholds are set; Combined with the historical fault records and health status assessment results of the carriage, the maintenance cycle is introduced and the expression is: Among them, Z m represents the recommended maintenance cycle, Z0 represents the standard maintenance cycle, and k represents the influence coefficient of adjusting the maintenance frequency with the change of health status; Based on the maintenance cycle recommended by the monitoring results at the handover point, observe whether the comprehensive health index within the recommended maintenance cycle is lower than the health status assessment threshold. When the comprehensive health index is lower than the health status assessment threshold, the replacement warning is automatically triggered and the replacement operation is performed.

8. A system for rapid monitoring and evaluation of railway freight operation status at a handover point, based on the method for rapid monitoring and evaluation of railway freight operation status at a handover point according to any one of claims 1 to 7, characterized in that: include, Data acquisition module, which collects cargo load distribution data and vehicle dynamic operation status data in real time and performs preprocessing; The load imbalance prediction module builds a cargo load imbalance prediction model based on preprocessed data, dynamically predicts cargo load imbalance risk in combination with train operation condition data, and generates load imbalance prediction results and load imbalance adjustment suggestions; The status assessment module associates the load imbalance prediction results, load imbalance adjustment suggestions, cargo load distribution data and vehicle dynamic operation status data with the digital identity information of the carriage, updates the full life cycle status information of the carriage, and generates the health status assessment results of the carriage; The personalized maintenance module formulates a personalized preventive maintenance plan based on the car health status assessment results.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method for rapid monitoring and evaluation of the railway freight operation status handover point according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for rapid monitoring and evaluation of the railway freight operation status handover point according to any one of claims 1 to 7 are implemented.

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