Redundant path design system and method for improving the stability of coal transmission system

Abstract

The present invention relates to the field of data processing technology, and discloses a redundant path design system and method for improving the stability of a coal transmission system. The method obtains historical operating data of the coal transmission system, constructs an equipment failure prediction model and a transportation demand prediction model, and predicts the equipment health status and transportation demand within a preset time; based on the prediction results and the network topology of the coal transportation system, a redundant path multi-objective optimization model is constructed to obtain a redundant path set; finally, intelligent flow control is performed based on the redundant path set; the present invention can improve the stability and adaptability of the coal transmission system, and can still ensure the continuity and efficiency of coal transportation under the risk of equipment failure and changes in transportation demand.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and more particularly to a redundant path design system and method for improving the stability of a coal transmission system. Background Art

[0002] In the prior art, the Chinese patent application with publication number CN117666367A discloses a remote control method and system for underground machinery in coal mines, which realizes real-time monitoring and updating of equipment status through a graph neural network algorithm. This method can effectively monitor equipment status and update node information, but the solution mainly focuses on monitoring and path planning at the equipment level, and fails to address complex issues such as path redundancy and flow distribution in the entire coal transportation system. At the same time, the application of graph neural networks in equipment status monitoring has improved the speed and accuracy of information processing, but in actual applications, due to the lack of unified modeling of multi-objective optimization of equipment failures and transportation needs, path redundancy design and flow management are difficult to fully play their due role. Therefore, although this solution has certain application value in the remote control of underground mechanical equipment, it still has certain defects in redundant path optimization and flow control of coal transportation systems.

[0003] The Chinese patent with the authorization announcement number CN118036841B discloses a dynamic material loading scheduling method and system based on smart mining services. By analyzing the mine material categories, storage volume and production business data, the decision tree network is used to evaluate the timeliness and feasibility of the loading scheduling strategy. This method has improved the timeliness and rationality of material loading scheduling to a certain extent, but it focuses on the loading scheduling of mine materials and pays more attention to the feasibility evaluation and scheduling optimization of transportation tasks. It has not yet considered how to effectively design redundant paths, perform flow management, or respond to dynamic changes in transportation paths in real time during transportation. Therefore, although this technology provides a scheduling optimization solution for mine transportation, its application in redundant path design and flow control in coal transportation systems is still insufficient.

[0004] In summary, existing technologies lack effective multi-objective optimization models that simultaneously consider redundant path design and flow distribution, resulting in the system's inability to flexibly respond to high loads or equipment failures. While existing equipment failure prediction and transportation demand forecasting models have achieved some success in their respective fields, existing technologies lack a unified application of these two types of prediction results, failing to effectively guide path optimization and flow scheduling decisions, limiting the efficiency of the entire coal transportation system. Summary of the Invention

[0005] In order to overcome the above-mentioned defects of the prior art, the present invention provides a redundant path design system and method for improving the stability of the coal transportation system. By combining equipment status prediction and transportation demand prediction, it solves the problem in the prior art that path planning and flow management cannot be optimized synchronously, and further improves the stability, reliability and transportation efficiency of the coal transportation system.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] The redundant path design method for improving the stability of the coal transportation system includes:

[0008] Acquire historical operating data of the coal transportation system and construct a first data set based on the historical operating data; construct and train an equipment failure prediction model and a transportation demand prediction model based on the first data set; use the trained equipment failure prediction model and transportation demand prediction model to predict the equipment health status and transportation demand within a preset time period to obtain a first prediction result and a second prediction result;

[0009] The network topology of the coal transportation system is obtained, and a redundant path multi-objective optimization model is constructed based on the first prediction result and the second prediction result, as well as the network topology of the coal transportation system; a redundant path set is obtained according to the redundant path multi-objective optimization model; and intelligent flow control is performed based on the redundant path set.

[0010] Furthermore, the historical operation data includes historical equipment operating parameters and historical coal transportation volume data; the historical equipment operating parameters and historical coal transportation volume data are sorted by time to form a historical equipment operating parameter sequence and a historical coal transportation volume data sequence;

[0011] The constructing the first data set includes:

[0012] Perform statistical analysis on the historical equipment operating condition parameter sequence, extract historical health status characteristics, and form a historical health status feature sequence;

[0013] Conduct time series analysis on the coal transportation volume data series, extract historical transportation demand indicators, and form a historical transportation demand indicator series;

[0014] The historical health status feature sequence and the historical transportation demand indicator sequence are combined to form a first data set.

[0015] Furthermore, the construction and training of the equipment failure prediction model and the transportation demand prediction model includes:

[0016] Using a time series prediction algorithm, we build an equipment failure prediction model based on the historical health status feature sequence. Using a regression prediction algorithm, we build a transportation demand prediction model based on the historical transportation demand indicator sequence.

[0017] The first prediction result includes the equipment failure rate FA within a preset time, and the second prediction result includes the predicted transportation volume TR within a preset time.

[0018] Furthermore, the construction of the redundant path multi-objective optimization model includes:

[0019] Acquire a network topology structure of a coal transportation system, wherein the network topology structure of the coal transportation system includes a path set and a node set;

[0020] The equipment failure rate FA and the predicted transport volume TR within the preset time are used as the constraints of the redundant path multi-objective optimization model;

[0021] Defining decision variables of a redundant path multi-objective optimization model, wherein the decision variables include a path selection variable and a traffic distribution ratio;

[0022] determining an objective function of a redundant path multi-objective optimization model, wherein the objective function includes minimizing coal transportation costs, minimizing equipment failure risks, and maximizing transportation efficiency;

[0023] According to the path set and node set in the network topology structure of the coal transportation system, as well as the decision variables and objective functions of the redundant path multi-objective optimization model, a redundant path multi-objective optimization model is constructed.

[0024] Furthermore, obtaining the redundant path set according to the redundant path multi-objective optimization model includes:

[0025] Solve the redundant path multi-objective optimization model and obtain a Pareto efficient solution set; each solution in the Pareto efficient solution set corresponds to a redundant path solution;

[0026] Select the main path from the Pareto efficient solution set;

[0027] Using the main path plan, calculate the transportation risk and determine whether a backup path is needed; if no backup path is needed, the main path is used to form a redundant path set; if a backup path is needed, the backup path is selected from the remaining Pareto efficient solution set after eliminating the main path plan; the main path and the backup path are combined to form a redundant path set.

[0028] Furthermore, selecting the main path from the Pareto efficient solution set includes:

[0029] Calculate the normalized value y of each solution in the Pareto efficient solution set on each objective l,g , the objectives include coal transportation cost, equipment failure risk and transportation efficiency, y l,grepresents the normalized value of the lth solution in the Pareto efficient solution set on the gth objective; 1≤g≤m, m is the total number of objectives; l is the index of the solution in the Pareto efficient solution set;

[0030] Set the target weight vector w=[w1,w2,…,w m ],w m is the weight of the mth target; the weighted summation method is used, based on the target weight vector w and the normalized value y of each solution on each target l,g , calculate the comprehensive evaluation index for each solution; among them, w1+w2+…+w m =1;

[0031] The solution with the largest comprehensive evaluation index is taken as the optimal solution, and the redundant path solution corresponding to the optimal solution is selected as the main path solution;

[0032] According to the main path plan, the node sequence that the main path passes through is determined to form the physical topology structure of the main path.

[0033] Furthermore, the physical topology structure forming the main path includes:

[0034] Step S22241, initialize the node sequence N of the main path main is an empty collection;

[0035] Step S22242: Select the starting point s' of the main path as the current node and add it to N main middle;

[0036] Step S22243: For the current node s', find the path selection variable x that satisfies the main path solution. s't' =1 next node t';

[0037] Step S22244, add node t' to N main and set node t' as the new current node;

[0038] Step S22245, repeating steps S22243 and S22244 until the end of the main path is reached;

[0039] Step S22246: Set the node sequence N of the main path main The physical topology that serves as the primary path.

[0040] Further, determining whether a backup path is needed includes:

[0041] Select variable x according to the path in the main path scheme i'j' , determine the path set E that the main path passes through main ;x i'j'Indicates whether to select the path (i', j') from node i' to node j'. If so, then x i'j' =1, otherwise x i'j' =0;

[0042] For each path (i', j')∈E on the main path main , according to the flow distribution ratio f of path (i', j') i'j' , allocate the predicted transport volume TR to the path (i', j'), and obtain the transport volume TR allocated to the path (i', j') i'j' ;

[0043] For each path (i', j')∈E on the main path main , combined with the equipment failure probability p of path (i', j') i'j' Economic losses caused by failure L i'j' , and the transport volume TR allocated to the path (i', j') i'j' , calculate the transportation risk R of path (i',j') i'j' ;

[0044] Sum the transportation risks of all paths on the main path to obtain the total transportation risk R of the main path main ;

[0045] The total transportation risk R of the main path main Compared with the preset risk threshold R th Compare; if R main >R th , then a backup path is required; otherwise, no backup path is required.

[0046] Furthermore, selecting a backup path from the remaining Pareto efficient solution set after eliminating the primary path solution includes:

[0047] Eliminate the optimal solution corresponding to the main path from the Pareto efficient solution set to form a set of alternative paths;

[0048] Calculate the transportation risk R of each alternative path in the alternative path set b , selecting the first N1 solutions with the lowest transportation risk in the alternative path set to form a first alternative path set, wherein the first alternative path set includes N1 first alternative paths;

[0049] Calculate the similarity SI between the first backup path and the main path k , SI k Indicates the similarity between the kth first backup path and the primary path; 1≤k≤N1;

[0050] According to the similarity SI kThe first backup paths are sorted in ascending order, and the first N2 first backup paths are selected as final backup paths; N2≤N1.

[0051] A redundant path design system for improving the stability of a coal transportation system, which is used to implement the redundant path design method for improving the stability of a coal transportation system, includes:

[0052] Model building module: obtaining historical operating data of the coal transmission system, building a first data set based on the historical operating data; building and training an equipment failure prediction model and a transportation demand prediction model based on the first data set;

[0053] Prediction module: Uses the trained equipment failure prediction model and transportation demand prediction model to predict the equipment health status and transportation demand within a preset time, and obtains the first prediction result and the second prediction result;

[0054] Multi-objective optimization module: obtains the network topology of the coal transportation system, constructs a redundant path multi-objective optimization model based on the first prediction result and the second prediction result, as well as the network topology of the coal transportation system; obtains a redundant path set based on the redundant path multi-objective optimization model;

[0055] Control module: performs intelligent flow control based on a set of redundant paths.

[0056] An electronic device includes a memory, a central processing unit, and a computer program stored in the memory and executable on the central processing unit. When the central processing unit executes the computer program, the redundant path design method for improving the stability of a coal transmission system is implemented.

[0057] A computer-readable storage medium stores a computer program, which, when executed, implements the redundant path design method for improving the stability of a coal transportation system.

[0058] Compared with the prior art, the present invention has the following beneficial effects:

[0059] The redundant path design method proposed in this invention improves the stability of coal transportation systems. By constructing equipment failure prediction models and transportation demand prediction models, it can accurately predict equipment health status and changes in transportation demand within a preset timeframe. Based on the prediction results and combined with the network topology of the coal transportation system, a multi-objective optimization model for redundant paths is constructed. This model comprehensively considers multiple objectives, including transportation cost, equipment failure risk, and transportation efficiency, to optimize path selection and traffic distribution and obtain an optimal set of redundant paths. By assessing the transportation risk of the primary path, adding backup paths when necessary, and selecting highly differentiated backup paths based on path similarity, it effectively disperses transportation risk and improves system robustness.

[0060] Furthermore, the present invention incorporates an intelligent flow control mechanism that monitors traffic on both primary and backup paths in real time, calculates flow balance, and dynamically adjusts flow distribution to ensure a balanced distribution of load across paths. This dynamic flow adjustment mechanism can adapt to fluctuations in transport demand, ensuring transport efficiency while mitigating risks associated with uneven flow.

[0061] The present invention makes full use of data-driven modeling and optimization methods, combines equipment status prediction, transportation demand prediction and path optimization, and constructs a complete set of redundant path design solutions. This solution can maximize the continuity, stability and efficiency of coal transportation through reasonable planning of redundant paths and intelligent flow control in the case of equipment failure risks and changes in transportation demand. Compared with traditional static path planning, the present invention has stronger adaptability and robustness, and can cope with complex and changeable actual transportation scenarios. This has important practical significance for improving the overall performance of the coal transportation system, reducing transportation costs, and improving resource utilization. The present invention provides new ideas and methods for the intelligent and digital transformation of the coal transportation industry, and has a positive role in promoting technological progress and sustainable development in the industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0063] Figure 1 This is a principle flow chart of the redundant path design method for improving the stability of the coal transportation system in the present invention;

[0064] Figure 2 A flow chart of a method for constructing a first data set in a redundant path design method for improving the stability of a coal transportation system according to the present invention;

[0065] Figure 3 A flow chart of a method for constructing and training an equipment failure prediction model and a transportation demand prediction model in a redundant path design method for improving the stability of a coal transportation system according to the present invention;

[0066] Figure 4 A flow chart of a method for constructing a redundant path multi-objective optimization model in a redundant path design method for improving the stability of a coal transportation system according to the present invention;

[0067] Figure 5A flowchart of a method for obtaining a redundant path set according to a redundant path multi-objective optimization model in a redundant path design method for improving the stability of a coal transportation system according to the present invention;

[0068] Figure 6 A flow chart of a method for forming a physical topology structure of a main path in a redundant path design method for improving the stability of a coal transportation system according to the present invention;

[0069] Figure 7 A flow chart of a method for determining whether a backup path is needed in a redundant path design method for improving the stability of a coal transportation system according to the present invention;

[0070] Figure 8 A flow chart of a method for selecting a backup path from a set of remaining Pareto efficient solutions after eliminating a primary path solution in a redundant path design method for improving the stability of a coal transportation system according to the present invention;

[0071] Figure 9 This is a flow chart of a method for intelligent flow control based on a redundant path set in a redundant path design method for improving the stability of a coal transmission system according to the present invention;

[0072] Figure 10 This is a functional module diagram of the redundant path design system for improving the stability of the coal transportation system in the present invention. DETAILED DESCRIPTION

[0073] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0074] Example 1

[0075] See also Figure 1 As shown, this embodiment provides a redundant path design method for improving the stability of the coal transportation system, including:

[0076] Step S1000: Acquire historical operating data of the coal transportation system, construct a first data set based on the historical operating data; construct and train an equipment failure prediction model and a transportation demand prediction model based on the first data set; use the trained equipment failure prediction model and transportation demand prediction model to predict the equipment health status and transportation demand within a preset time period, and obtain a first prediction result and a second prediction result;

[0077] Furthermore, step S1000 includes:

[0078] Step S1100: Acquire historical operating data of the coal transportation system, the historical operating data including historical equipment operating parameters and historical coal transportation volume data; sort the historical equipment operating parameters and historical coal transportation volume data by time to form a historical equipment operating parameter sequence and a historical coal transportation volume sequence;

[0079] Specifically, historical equipment operating parameters refer to a series of status parameters of coal conveying equipment during operation. These parameters reflect the working condition of the equipment and can help identify potential failure risks or performance bottlenecks. Common equipment operating parameters include:

[0080] Speed: The operating speed of the device reflects the workload of the device.

[0081] Temperature: The temperature of the equipment during operation. Excessive temperature may be a precursor to failure.

[0082] Vibration frequency and amplitude: Vibration is a common precursor to mechanical equipment failure. Changes in vibration frequency and amplitude can indicate the degree of wear or damage to the equipment.

[0083] Rotation angle and speed: The rotation angle and speed of the equipment are important operating parameters, which are usually used to judge the load condition of the equipment.

[0084] Fault status: whether a device fault has occurred, as well as the time, duration, and type of fault.

[0085] Historical coal transportation data reflects the actual situation of the coal transportation process, including the amount of transportation and the specific parameters of the transportation process. This data is crucial for evaluating system operation efficiency, transportation demand, and whether there are bottlenecks. It mainly includes:

[0086] Coal order volume: refers to the total amount of coal that needs to be transported within a certain period of time.

[0087] Actual loading volume: refers to the actual amount of coal loaded.

[0088] Number of transport vehicles: the number of vehicles used to transport coal.

[0089] Single car load: the coal load per transport car.

[0090] Daily transport frequency: The number of transports carried out in a day reflects the operating frequency of the system.

[0091] Transport Time: The time required to complete a shipment, including loading, transporting, and unloading time.

[0092] By acquiring and sorting these historical data by time, two sets of sequences can be formed:

[0093] Historical equipment operating parameter sequence: Equipment operating parameter data sorted by time can help analyze the operating status and failure frequency of the equipment.

[0094] Historical coal transportation volume data series: transportation volume data sorted by time, used to reveal the changing trend of coal transportation demand and system load conditions.

[0095] In the process of optimizing and improving coal transportation systems, it is crucial to accurately acquire and process historical operating data. This data provides the basis for subsequent analysis, modeling, and optimization decisions.

[0096] Step S1200: Obtain a historical health status feature sequence and a historical transportation demand index sequence based on historical operation data to construct a first data set;

[0097] Furthermore, if Figure 2 As shown, step S1200 includes:

[0098] Step S1210 , statistically analyzing the historical equipment operating condition parameter sequence, extracting historical health status features, and forming a historical health status feature sequence;

[0099] Step S1220, performing time series analysis on the coal transportation volume data series, extracting historical transportation demand indicators, and forming a historical transportation demand indicator series;

[0100] Step S1230: Combine the historical health status feature sequence and the historical transportation demand index sequence to form a first data set.

[0101] Specifically, step S1200 aims to extract features and indicators that contribute to coal transportation system optimization through in-depth analysis of historical operating data. This process primarily involves two aspects: extracting health status features from equipment operating parameters and extracting transportation demand indicators from coal transportation volume data. These two indicator series are combined to form a complete data set for subsequent prediction and decision-making.

[0102] By statistically analyzing historical equipment operating parameters, we can extract important features that reflect the health of the equipment. These features can reveal the stability, reliability, and maintenance needs of the equipment, helping to assess whether the equipment is operating properly and predict possible failures. These historical health status features include equipment failure rate, mean time between failures (MTBF), mean time to repair (MTTR), equipment availability, and equipment utilization.

[0103] Equipment failure rate: This indicates the frequency of failures within a specific timeframe, typically calculated as the ratio of failures per unit time to total operating time. A high failure rate often indicates increased equipment instability and a potential risk of system downtime. Real-time monitoring of failure rates allows for proactive identification and maintenance measures, reducing the frequency of system downtime.

[0104] Mean Time Between Failures (MTBF): This indicates the average time between one equipment failure and the next. A higher MTBF indicates higher equipment reliability. Longer, trouble-free operation provides more stable production and reduces repair and replacement costs.

[0105] Mean Time to Repair (MTTR): This represents the average time from when a device fails to when it is repaired. A shorter MTTR means faster equipment repairs and less system downtime, improving system availability and efficiency.

[0106] Equipment availability rate: This indicates the percentage of equipment operating normally over a period of time. It is the ratio of normal operating time to total operating time. A higher equipment availability rate indicates greater system efficiency and stability, reducing the risk of production interruptions.

[0107] Equipment utilization rate: This indicates the proportion of time a device is in effective working condition to its total working time over a period of time. A higher equipment utilization rate means that the device is operating more efficiently, reducing idle time and wasted resources.

[0108] Coal transportation volume data reflects the changing demand trends during coal transportation. Through time series analysis, we can extract important indicators reflecting transportation demand fluctuations, seasonal changes, and long-term growth trends. The historical transportation demand indicators include daily average transportation volume, monthly transportation volume change rate, annual transportation volume growth rate, and transportation volume seasonality index.

[0109] Average daily transport volume: This reflects the fundamental scale of transport demand and is calculated by dividing the total transport volume during a specific period by the number of days. Average daily transport volume provides a fundamental perspective on transport demand and can be used to develop reasonable transport plans and ensure the proper allocation of transport resources.

[0110] Monthly Transportation Volume Change Rate: This reflects the short-term trend in transportation demand, indicating the difference between this month's transportation volume and the previous month's. By analyzing the monthly transportation volume change rate, we can quickly identify fluctuations and anomalies in transportation demand and adjust transportation strategies in a timely manner.

[0111] Annual Volume Growth Rate: This reflects the long-term trend in transportation demand, indicating the percentage increase in this year's volume compared to last year's. The annual volume growth rate can help identify long-term trends in transportation demand and aid future resource planning and demand forecasting.

[0112] Transport volume seasonality index: reflects the seasonal characteristics of transport demand. It evaluates transport demand in different seasons by analyzing the monthly distribution of historical data. The calculation steps are as follows:

[0113] Classify the transportation volume data of many years by month to obtain the multi-year average transportation volume of each month;

[0114] Calculate the average annual transport volume for all months (i.e. the average annual transport volume), compare the average monthly transport volume with the multi-year average transport volume, and calculate the seasonal index for each month;

[0115] If the seasonal index is greater than 1, it means that the transportation demand in that month is higher than the annual average; if it is less than 1, it means that it is lower than the annual average.

[0116] Through the seasonal index, we can better understand the cyclical fluctuations in transportation demand, optimize the allocation of transportation resources, and avoid excessive or insufficient transportation.

[0117] The historical health status feature sequences and historical transportation demand indicator sequences extracted in steps S1210 and S1220 are combined to form a first data set. This data set provides comprehensive input data for subsequent prediction models, optimization algorithms, and decision support systems. This helps the system predict equipment and transportation demand trends based on historical data and make more accurate scheduling and maintenance decisions.

[0118] Step S1300: constructing and training an equipment failure prediction model and a transportation demand prediction model based on the first data set;

[0119] Furthermore, if Figure 3 As shown, step S1300 includes:

[0120] Step S1310 , using a time series prediction algorithm to build an equipment failure prediction model based on historical health status feature sequences;

[0121] Step S1320: Using a regression prediction algorithm, a transportation demand prediction model is constructed based on the historical transportation demand indicator sequence.

[0122] Specifically, the core task of step S1300 is to use the first data set (composed of historical equipment operating parameter sequences and historical coal transportation volume data sequences) to construct and train two prediction models: an equipment failure prediction model and a transportation demand prediction model. These two models are used to predict the probability of equipment failure and the future trend of coal transportation volume, respectively.

[0123] The goal of equipment failure prediction models is to predict the probability of equipment failure at a certain point in the future based on historical equipment operating data. Using time series prediction algorithms, particularly deep learning models like long short-term memory (LSTM) and gated recurrent unit (GRU), can capture long-term dependencies in the historical equipment operating parameter sequences and accurately predict equipment failures.

[0124] LSTM is a special type of recurrent neural network (RNN) specifically designed to capture long-term dependencies in time series data. Compared to traditional RNNs, LSTM avoids the vanishing gradient problem by introducing memory cells and gating mechanisms (input gate, forget gate, and output gate), enabling it to effectively process long-term time series data. LSTM can better capture long-term patterns of changes in equipment operation, such as temperature fluctuations and gradually increasing vibration amplitude, which may be precursors to equipment failure. GRU is another improved RNN. Similar to LSTM, its structure is simpler, with only update and reset gates, resulting in higher computational efficiency. GRU may be more efficient than LSTM in processing shorter-term dependencies and is suitable for simpler and faster equipment failure prediction scenarios.

[0125] Equipment failure prediction model training process:

[0126] Input: Historical health status feature sequence, such as equipment temperature, vibration frequency, speed, and other parameters over a period of time.

[0127] Label: Whether the device will fail at the next moment, typically a binary label (0: not failed, 1: failed). For example, if historical data indicates that a device fails when its temperature exceeds a certain threshold, the label for the training data will be "1" (failed), otherwise it will be "0" (not failed).

[0128] Output: The model predicts the probability of a device failing within a preset time. The output value is a number between [0, 1], indicating the probability of a failure.

[0129] By training equipment failure prediction models, we can provide accurate early warnings of equipment health and identify potential failures in advance. For example, in the case of coal conveying equipment, if the temperature of the equipment continues to rise and exceeds the normal range, the failure prediction model can issue an early warning, allowing preventive maintenance measures to be taken, avoiding equipment downtime and reducing losses caused by unplanned outages.

[0130] The goal of a transportation demand forecasting model is to predict coal transportation demand (i.e., coal transportation volume) within a preset timeframe based on historical coal transportation data. To handle this continuous numerical prediction problem, a regression prediction algorithm is typically used. Commonly used regression algorithms include support vector machine regression (SVR) and gradient boosted decision tree (GBDT).

[0131] SVR is the application of support vector machines (SVM) to regression problems, performing regression predictions by finding a hyperplane that best fits the data. SVR has strong capabilities in modeling high-dimensional and nonlinear data, and is suitable for complex regression problems such as coal transportation volume forecasting. In particular, SVR can maintain good prediction accuracy when the data is noisy or fluctuates irregularly. GBDT is an ensemble learning algorithm that trains multiple decision trees and optimizes the model using the gradient boosting method to achieve high prediction accuracy in complex regression problems. By gradually optimizing and combining the prediction results of multiple decision trees, GBDT can accurately model nonlinear and complex transportation demand data, and is particularly suitable for situations with multiple influencing factors (such as seasonal changes, market demand fluctuations, etc.).

[0132] Transportation demand forecasting model training process:

[0133] Input: Historical transport demand indicator series, such as daily average transport volume, monthly transport volume change rate, and annual transport volume growth rate.

[0134] Label: The transportation volume at a certain moment in the future, usually a continuous value, such as the coal transportation volume in the next day (unit: tons), the total transportation volume in the next month (unit: tons).

[0135] Output: The model predicts the coal transportation volume within the preset time.

[0136] The transportation demand forecasting model accurately predicts future trends in coal transportation volumes. For example, given seasonal variations in coal production, the model can accurately predict the expected increase in transportation volume for a given month, providing a scientific basis for vehicle scheduling and resource allocation. This approach effectively reduces waste of transportation resources and improves system efficiency.

[0137] In order to ensure the generalization ability and prediction accuracy of the model, step S1300 also includes the data set division and model evaluation process.

[0138] Division of training set, validation set and test set:

[0139] The first data set is divided into a training set, a validation set, and a test set. Generally, the training set accounts for 70%-80%, the validation set accounts for 10%-15%, and the test set accounts for 10%-15%.

[0140] The training set is used to train the model, the validation set is used to tune the model's hyperparameters, and the test set is used to evaluate the model's final performance.

[0141] Model training:

[0142] The equipment failure prediction model and the transportation demand prediction model are trained using the training set data. By repeatedly optimizing the model parameters, the model can gradually improve its prediction accuracy.

[0143] Model evaluation and tuning:

[0144] Use the validation set data to evaluate the performance of the trained model. Common evaluation metrics include:

[0145] Equipment failure prediction model: accuracy, recall rate, F1 value, etc.

[0146] Transportation demand forecasting model: mean square error (MSE), mean absolute error (MAE), etc.

[0147] Adjust the model's hyperparameters based on the evaluation results to avoid overfitting or underfitting and ensure that the model has good generalization ability.

[0148] Cross-validation:

[0149] Cross-validation is a commonly used model evaluation method. By dividing the dataset into multiple subsets and alternating between different subsets for training and validation, it can effectively improve the stability and accuracy of the model and avoid the deviation caused by a single data partition.

[0150] Through a rational training, validation, and testing process, the model's prediction accuracy can be effectively improved and its reliability in practical applications can be ensured. For example, cross-validation can prevent the model from overfitting on a specific dataset, thereby improving the model's generalization ability and providing stable and accurate prediction results across different time periods and coal transportation scenarios.

[0151] Step S1400: Using the trained equipment failure prediction model and transport demand prediction model, the equipment health status and transport demand within a preset time are predicted to obtain a first prediction result and a second prediction result. The first prediction result includes the equipment failure rate FA within the preset time, and the second prediction result includes the predicted transport volume TR within the preset time.

[0152] Furthermore, step S1400 includes:

[0153] Step S1410: obtaining a real-time equipment operating condition parameter sequence, obtaining a real-time health status feature sequence based on the real-time equipment operating condition parameter sequence, and inputting the real-time health status feature sequence into a trained equipment failure prediction model to obtain a first prediction result;

[0154] Step S1420: Acquire a real-time coal transportation volume data sequence, obtain a real-time transportation demand index sequence based on the real-time coal transportation volume data sequence, input the real-time transportation demand index sequence into a trained transportation demand prediction model, and obtain a second prediction result.

[0155] Specifically, a real-time equipment operating parameter sequence refers to the equipment's operating status data from the current moment and the previous period. This data reflects the equipment's immediate operating status. Common real-time equipment operating parameters include equipment speed, temperature, vibration frequency, rotational speed, and pressure. For example, the real-time operating data for equipment A might include: temperature = 80°C, vibration frequency = 50Hz, and rotational speed = 3000rpm. These data constitute a real-time equipment operating parameter sequence. Based on this real-time equipment operating parameter sequence, a device health feature sequence is calculated using the same feature extraction method as in step S1210. These features include equipment failure rate, mean time to failure (MTBF), mean time to failure (MTTR), equipment availability, and equipment utilization. Assuming that the real-time equipment operating data includes temperature and vibration frequency, a feature sequence reflecting the equipment's current health status can be generated by calculating features such as the historical mean and standard deviation of these parameters. By extracting real-time equipment health features, the equipment's immediate operating status can be effectively tracked and provide accurate input for the equipment failure prediction model.

[0156] The health status feature sequence calculated in real time is input into a trained equipment failure prediction model (such as an LSTM or GRU) to obtain the probability (FA) of equipment failure within a preset timeframe. The output of the equipment failure prediction model is the probability of failure within the preset timeframe. For example, if the model predicts a 0.15 probability of equipment failure within the next 24 hours, this means the probability of equipment failure is 15%.

[0157] By collecting and processing equipment operating data in real time, the system can monitor equipment health and predict future failure risks. This predictive capability is crucial for identifying potential equipment problems before they occur. For example, if a piece of equipment's temperature or vibration amplitude has increased gradually over the past few hours, the failure prediction model can issue an early alert and recommend preventive maintenance, thus avoiding sudden equipment downtime and reducing production interruptions and repair costs.

[0158] The real-time coal transportation volume data series refers to coal transportation data from the current moment and the previous period. This data reflects the actual load of the transportation system, including coal orders, actual loading volume, and the number of transport vehicles. For example, real-time transportation volume data may include: coal orders = 5,000 tons, actual loading volume = 4,800 tons, and the number of transport vehicles = 120. Based on the real-time coal transportation volume data series, historical transportation demand characteristics are extracted using the same indicator calculation method as in step S1220, including daily average transportation volume, monthly transportation volume change rate, annual transportation volume growth rate, and transportation volume seasonality index. For example, based on the transportation volume data from the past seven days, indicators such as daily average transportation volume and monthly transportation volume change rate are calculated. These indicators reflect the basic patterns and fluctuation trends of transportation demand. By extracting real-time transportation demand indicators, the system can accurately grasp the fluctuation trends of transportation demand and adjust transportation plans in a timely manner to avoid resource waste or transportation bottlenecks.

[0159] The real-time extracted transport demand indicator sequence is input into a trained transport demand forecasting model (such as SVR or GBDT) to obtain the predicted transport volume TR for a preset time period. The output of the transport demand forecasting model is the predicted value of coal transportation volume for the preset time period. For example, the model predicts that the transportation volume for the next day will be 5,200 tons and the transportation volume for the next month will be 160,000 tons. By monitoring and forecasting transport demand in real time, the system can dynamically adjust transportation plans and resource allocation. For example, during seasons when transport demand rises sharply, the forecasting model can provide advance notification of the increasing trend in transport volume, helping companies to allocate transport vehicles and equipment in advance and avoid a shortage of transport resources. Furthermore, when demand decreases, it can also promptly reduce the investment in transport resources and optimize costs.

[0160] The core of step S1400 is to acquire and process equipment and transportation data in real time. Combining this with trained prediction models, predictions are made within a preset timeframe, providing scientific decision-making support for the coal transportation system. Specifically, equipment failure rate predictions enable early identification of equipment failure risks and preventative measures, while transportation volume predictions provide an accurate basis for the allocation and scheduling of transportation resources. Overall, these prediction models provide strong data support for the intelligent management of the coal transportation system, helping the system improve operational efficiency, reduce risks, and respond flexibly to changing market conditions.

[0161] Step S2000: obtaining a network topology of the coal transportation system, constructing a redundant path multi-objective optimization model based on the first prediction result and the second prediction result, as well as the network topology of the coal transportation system; and obtaining a redundant path set according to the redundant path multi-objective optimization model.

[0162] Furthermore, step S2000 includes:

[0163] Step S2100: Acquire the network topology of the coal transportation system and construct a redundant path multi-objective optimization model based on the equipment failure rate FA and the predicted transportation volume TR within a preset time, as well as the network topology of the coal transportation system; the network topology of the coal transportation system includes a path set and a node set;

[0164] Furthermore, if Figure 4 As shown, step S2100 includes:

[0165] Step S2110 , using the equipment failure rate FA and the predicted transport volume TR within a preset time as constraints for the redundant path multi-objective optimization model;

[0166] Step S2120 , defining decision variables of the redundant path multi-objective optimization model, including path selection variables and traffic distribution ratio;

[0167] The path selection variable can be represented by a binary variable, which takes the value of 1 if selected and 0 otherwise; the traffic distribution ratio can be represented by a continuous variable with a value range of [0,1], indicating the proportion of the transport volume allocated to each path to the total transport volume;

[0168] Step S2130 , determining the objective function of the redundant path multi-objective optimization model, including minimizing coal transportation costs, minimizing equipment failure risks, and maximizing transportation efficiency;

[0169] Step S2140 , constructing a redundant path multi-objective optimization model based on the path set and node set in the network topology structure of the coal transportation system, and the decision variables and objective function of the redundant path multi-objective optimization model.

[0170] The redundant path multi-objective optimization model includes:

[0171] Let G = (V, E) represent the network topology of the coal transportation system, where V is the node set and E is the path set. Define the following decision variables:

[0172] x ij : Path selection variable, a binary variable, indicating whether to select the path from node i to node j. If selected, then x ij =1, otherwise x ij =0; i and j are node indices, i∈V, j∈V.

[0173] f ij : Traffic allocation ratio is a continuous variable that represents the traffic allocation ratio allocated to path (i, j) (the proportion of transport volume to the total transport volume), and its value range is [0,1].

[0174] Objective function 1: Minimize coal transportation costs:

[0175] min∑ (i,j)∈E c ij ·x ij ·f ij ;

[0176] Objective function 2: Minimize the risk of equipment failure:

[0177] min ∑ (i,j)∈E p ij ·x ij ·L ij ·f ij ;

[0178] Objective function 3: Maximize transportation efficiency:

[0179]

[0180] Constraints:

[0181] Constraint 1: Flow conservation:

[0182]

[0183] Constraint 2: Transport volume allocation:

[0184]

[0185] Constraint 3: Satisfy forecasted transportation demand:

[0186]

[0187] Constraint 4: Allocation ratio range:

[0188]

[0189] Constraint 5: Decision variable values:

[0190]

[0191] c ij : The cost of transporting a unit of coal on path (i, j) can be estimated based on factors such as path length, transportation time, and energy consumption; (i, j) represents a path from node i to node j.

[0192] x ji : Path selection variable, indicating whether to select the path from node j to node i. If selected, then x ji =1, otherwise x ji =0;

[0193] p ij: The probability of equipment failure on path (i, j) can be obtained based on the equipment failure rate FA within the preset time output by the equipment failure prediction model.

[0194] L ij : If the equipment on path (i, j) fails, the expected economic loss can be estimated based on historical data or expert experience.

[0195] T i,j : The estimated transportation time of path (i, j) can be calculated based on the path length and average transportation speed.

[0196] Q: The average load of coal transport vehicles can be obtained based on vehicle type and historical data.

[0197] D: The predicted total coal transportation demand, which is obtained based on the predicted transportation volume TR within the preset time output by the transportation demand prediction model.

[0198] s: The source point of the coal transportation network, that is, the production or distribution center of coal.

[0199] t: The meeting point of the coal transportation network, that is, the destination or consumption place of coal.

[0200] j:(i,j)∈E: represents the set of the end nodes j of all paths (i,j) starting from node i, including the end nodes of all paths starting from node i.

[0201] i:(i,j)∈E: represents the set of all paths (i,j) starting from node i, that is, all paths from i to other nodes.

[0202] Constraint 1 (flow conservation) ensures that the inflow is equal to the outflow for each node in the network except the source and sink; for the source, the outflow is 1, and for the sink, the inflow is 1. Constraint 2 (transportation volume allocation) ensures that the sum of the flow of all paths is 1. Constraint 3 (meeting the predicted transportation demand) ensures that the total transportation volume of the network can meet the total predicted coal transportation demand. Constraint 4 (allocation ratio range) limits the flow allocation ratio of each path to the range of [0,1]. Constraint 5 (decision variable value) limits the path selection variable x ij A variable that is either 0 or 1.

[0203] This redundant path multi-objective optimization model, through objective function 1 (minimizing coal transportation costs) and objective function 2 (minimizing equipment failure risk), improves system reliability while controlling costs and reducing economic losses caused by equipment failure. Objective function 3 (maximizing transportation efficiency) optimizes flow distribution along redundant paths, reducing coal in-transit time and improving the overall efficiency of the transportation system. Constraints 1 (flow conservation) and 2 (transport volume distribution) ensure logistical balance within the coal transportation network, avoiding node congestion or idle paths. Specifically, constraint 1 ensures that the inflow to every node, except the source and sink, equals the outflow, ensuring flow balance across the network. Constraint 2 ensures that the sum of the flow distribution across all paths is 1, preventing excessive or insufficient flow within the network. Constraint 3 (meeting predicted transportation demand) ensures that the coal transportation network can meet changes in transportation demand within a preset timeframe, thereby improving the system's adaptability and flexibility. By rationally setting 0-1 and continuous decision variables, path selection and flow distribution are integrated into a single mathematical model, achieving an integrated optimization solution for redundant path planning. This model can more efficiently select the optimal route and traffic allocation plan, further improving the overall performance and adaptability of the transportation system.

[0204] The goal of step S2100 is to construct a redundant path multi-objective optimization model based on the coal transportation system's network topology (including a set of paths and nodes), combined with the equipment failure rate FA within a preset timeframe and the predicted transportation volume TR. This model aims to achieve the following goals: optimize coal transportation reliability, reduce transportation costs, improve system efficiency, and effectively mitigate potential risks associated with equipment failures.

[0205] When building a redundant path multi-objective optimization model, it's important to first introduce the equipment failure rate (FA) and the predicted transport volume (TR) as constraints. These factors influence the model's path selection and traffic allocation decisions, ensuring that the optimization results not only meet transport demand but also improve system stability and reliability.

[0206] The equipment failure rate (FA) refers to the probability of equipment failure within a preset timeframe and reflects the reliability of equipment operation. Routes with higher equipment failure rates may require lower flow allocations to minimize transport disruptions caused by equipment failures. Paths with high failure rates should avoid or reduce flow allocations to ensure stable coal transportation. If the probability of equipment failure on a particular route is high, the optimization model should favor alternative routes with higher equipment reliability. Assume that the equipment failure rate on route (i, j) is 0.2, meaning that within the preset timeframe, the probability of failure on this route is 20%. If the transport demand on this route is excessive, frequent equipment failures may occur, impacting overall transport efficiency. The optimization model will account for this in the constraints and appropriately reduce the flow allocation on this route.

[0207] The predicted transport volume (TR) refers to the total volume of transport tasks that the coal transportation system needs to complete within a preset timeframe, typically expressed in tons. The transport volume forecast not only reflects the system's load demand but is also closely related to the flow distribution along each route. The predicted transport volume reflects the scale of the transport tasks facing the system. To ensure that each route can meet transport demand, the model must rationally allocate transport volume while ensuring reliability. Excessive transport volume concentrated on a single route can lead to overload on that route, impacting overall transport efficiency and equipment health. For example, if the predicted transport volume is 10,000 tons and the equipment failure rate on a particular route in the system is high, the system should rationally adjust flow distribution based on the failure rate to avoid concentrating too many transport tasks on routes with high failure rates.

[0208] When building a redundant path multi-objective optimization model, it is necessary to define decision variables. These variables will determine the optimal solution for path selection and traffic distribution. The two main decision variables are path selection variables and traffic distribution ratios.

[0209] Path selection variable x ij , is a binary variable that indicates whether to select the path from node i to node j. If selected, then x ij =1, otherwise x ij = 0; The path selection variable determines whether a path in the coal transportation system is selected for transportation. If the path selection probability is high, the flow distribution ratio of the path will be large, and vice versa. Suppose at a certain moment, there are two paths to choose from between node i and node j: path 1 and path 2. If the equipment failure rate of path 1 is low, then x ij The value of path 1 may be 1, and the value of path 2 may be 0. The optimization model will select the most appropriate path based on the equipment failure rate and transportation demand.

[0210] Traffic distribution ratio f ijIs a continuous variable that represents the proportion of the transport volume allocated to path (i, j) to the total transport volume, and its value range is [0,1]. The flow distribution ratio directly affects the amount of coal transported on each path. The flow distribution ratio determines the distribution of each path in the total transport volume. When multiple paths are available, the system will adjust the flow distribution based on factors such as the reliability of the path, the transport task, and the probability of failure. If the total transport volume of the system is 10,000 tons, and the flow distribution ratio f of path (i, j) is ij If the value is 0.3, the transport volume allocated to this route is 3,000 tons. By optimizing the traffic distribution ratio, the load of each route can be balanced to avoid overloading of some routes.

[0211] The objective function of the redundant path multi-objective optimization model includes minimizing coal transportation costs, minimizing the risk of equipment failure, and maximizing transportation efficiency. By balancing these objectives, the model is able to find the optimal path selection and flow allocation solution.

[0212] Coal transportation costs include direct and indirect costs arising from factors such as route selection, route usage time, and maintenance costs. One optimization goal is to reduce overall transportation costs through appropriate route selection and traffic distribution. Optimizing route selection and traffic distribution can avoid overuse of high-cost routes, thereby reducing unnecessary overhead. For example, selecting routes with high reliability and low transportation costs can reduce maintenance and fuel consumption. Equipment failure risk is related to the failure rate of equipment along a route. The goal is to adjust traffic distribution to avoid frequent equipment failures and ensure equipment health. By rationally allocating transportation tasks and avoiding overuse of routes with high failure rates, the incidence of equipment failures can be reduced, improving equipment utilization and system stability. Transportation efficiency refers to the amount of transportation tasks completed per unit time. One optimization goal is to utilize each route as efficiently as possible, ensuring that the maximum amount of transportation is completed in the shortest time. Optimizing traffic distribution and route selection can improve the overall operational efficiency of the coal transportation system, reduce transportation time and resource waste, and enhance the overall system's responsiveness and service level.

[0213] The redundant path multi-objective optimization model, by considering equipment failure rates and optimizing traffic distribution ratios, can effectively avoid transport disruptions caused by equipment failures and improve the overall reliability and stability of the system. By rationally allocating transport tasks, overuse of high-cost routes is avoided, reducing transport waste and lowering overall transport costs. By maximizing transport efficiency, the system's transport capacity can be maximized while ensuring safety and reliability, improving overall operational efficiency and enhancing the system's responsiveness under high loads.

[0214] Step S2200, obtaining a redundant path set according to a redundant path multi-objective optimization model;

[0215] Furthermore, if Figure 5 As shown, step S2200 includes:

[0216] Step S2210, solving the redundant path multi-objective optimization model to obtain a Pareto efficient solution set; each solution in the Pareto efficient solution set corresponds to a redundant path solution;

[0217] Specifically, the purpose of step S2210 is to solve the redundant path multi-objective optimization model and obtain a Pareto-efficient solution set. The Pareto-efficient solution set includes all solutions that cannot improve one objective without degrading other objectives and represents the optimal combination of decision variables. In multi-objective optimization, there is typically no single optimal solution; rather, multiple solutions exist, and the trade-offs and balances between them represent different optimization objectives.

[0218] Choosing a suitable multi-objective optimization algorithm is the key to finding a Pareto efficient solution set. Common multi-objective optimization algorithms include:

[0219] Weighted Sum Method: This method assigns different weights to each objective to solve the weighted sum maximization or minimization problem. This method solves the problem by converting multiple objective functions into a weighted sum.

[0220] Constraint method: optimize one objective function as the main objective, and use other objective functions as constraints. Gradually adjust the constraint values ​​of other objectives to optimize the main objective.

[0221] Genetic algorithm: Genetic algorithm generates possible solutions from generation to generation by simulating the natural selection process, and uses fitness function to evaluate the quality of the solutions. It generates new solutions through crossover and mutation, thereby optimizing multiple objectives.

[0222] In practical applications, genetic algorithms are often used to solve complex multi-objective optimization problems, finding near-optimal solutions within a large solution space. Through the iterative process of genetic algorithms, the system can find multiple different solutions, which together constitute a Pareto-efficient solution set.

[0223] A Pareto-efficient solution is one in which one objective cannot be further improved without degrading the others in a multi-objective optimization problem. In other words, if there is a solution A where it is impossible to increase or decrease the value of one objective without degrading the values ​​of the others, then that solution is considered Pareto-efficient. The set of Pareto-efficient solutions encompasses all such solutions, representing the optimal balance between all optimization objectives.

[0224] For example, in redundant path multi-objective optimization, route selection and traffic allocation involve multiple objectives: minimizing coal transportation costs, minimizing the risk of equipment failure, and maximizing transportation efficiency. Each solution in the Pareto efficient solution set represents a balance between these three objectives, making it impossible to optimize one objective without affecting the others.

[0225] Step S2220, selecting a main path from the Pareto efficient solution set;

[0226] Furthermore, step S2220 includes:

[0227] Step S2221, calculate the normalized value y of each solution in the Pareto efficient solution set on each target l,g , the objectives include coal transportation cost, equipment failure risk and transportation efficiency, y l,g represents the normalized value of the lth solution in the Pareto efficient solution set on the gth objective; 1≤g≤m, m is the total number of objectives; l is the index of the solution in the Pareto efficient solution set;

[0228] Step S2222, set the target weight vector w=[w1,w2,…,w m ],w m is the weight of the mth target; the weighted summation method is used, based on the target weight vector w and the normalized value y of each solution on each target l,g , calculate the comprehensive evaluation index for each solution; among them, w1+w2+…+w m =1;

[0229] Step S2223: The solution with the largest comprehensive evaluation index is selected as the optimal solution, and the redundant path solution corresponding to the optimal solution is selected as the main path solution;

[0230] Specifically, since the result of a multi-objective optimization problem is a set of multiple solutions, the system needs to select an optimal solution, namely the main path solution, through further evaluation. The main path is the path solution that best balances multiple objectives.

[0231] Before selecting the main path, each solution in the Pareto efficient solution set must first be normalized. Normalization is the process of converting target values ​​of different dimensions into the same standard range to make each target value comparable. The specific steps are as follows:

[0232] Objectives include coal transportation costs, equipment failure risk, and transportation efficiency:

[0233] Coal transportation cost: reflects the economic cost of coal transportation, and the goal is usually to minimize the coal transportation cost.

[0234] Equipment failure risk: reflects the probability or risk of equipment failure, and the goal is usually to minimize the risk of equipment failure.

[0235] Transportation efficiency: refers to the amount of coal transported per unit time. The goal is to maximize transportation efficiency.

[0236] Normalization typically uses linear normalization, mapping target values ​​to the interval [0, 1]. During normalization, transportation efficiency is maximized, while coal transportation costs and equipment failure risk are minimized. Therefore, during the normalization process, the normalized value of transportation efficiency is proportional to the original value, while the normalized values ​​of coal transportation costs and equipment failure risk are inversely proportional to the original values. This results in consistent ranges for each target value, enabling fair comparison and weighting.

[0237] The weighted summation method assigns different weights to each objective, calculates a comprehensive evaluation metric for each solution, and then selects the optimal solution. The weights reflect the importance the decision maker places on each objective. For the three objectives of coal transportation cost, equipment failure risk, and transportation efficiency, the decision maker can set their weights based on the actual situation. For example, if the decision maker prioritizes reducing transportation costs, they can assign a higher weight to transportation costs. Suppose there are three objectives: transportation cost w1 = 0.4, equipment failure risk w2 = 0.3, and transportation efficiency w3 = 0.3. The sum of these weights is 1.

[0238] For each solution in the Pareto efficient solution set, after calculating its normalized value on each target, the weighted summation method is used to obtain the comprehensive evaluation index z of each solution. l :

[0239] z l =w1×y l,1 +w2×y l,2 +...+w m ×y l,m ;

[0240] Among them, y l,m represents the normalized value of the lth solution on the mth target, z l Represents the comprehensive evaluation index of the lth solution.

[0241] In step S2223, the comprehensive evaluation index of each solution is calculated and the solution with the maximum comprehensive evaluation index is selected. This solution represents the optimal balance between coal transportation cost, equipment failure risk, and transportation efficiency. After the optimal solution is selected, its corresponding redundant path solution is determined as the main path solution;

[0242] After calculating the comprehensive evaluation index for each solution, the solution with the largest comprehensive evaluation index is compared and selected as the optimal solution. This means that although some solutions may perform well on a single objective, their overall performance may be poor. Ultimately, a weighted summation method is used to select the solution with the best overall performance.

[0243] For example, suppose solution A has the following raw values ​​for the three objectives: transportation cost of 1 million yuan, equipment failure risk of 0.1, and transportation efficiency of 5,000 tons / hour; while solution B has the following raw values: transportation cost of 2 million yuan, equipment failure risk of 0.05, and transportation efficiency of 2,000 tons / hour. After normalization, the normalized values ​​of A might be (0.5, 0.5, 1.0), and the normalized values ​​of B might be (1.0, 1.0, 0.4).

[0244] If the weights of the three objectives are all 1 / 3, then the comprehensive evaluation index of solution A is:

[0245] z A =(1 / 3)×0.5+(1 / 3)×0.5+(1 / 3)×1.0=0.67;

[0246] The comprehensive evaluation index of solution B is:

[0247] z B =(1 / 3)×1.0+(1 / 3)×1.0+(1 / 3)×0.4=0.80;

[0248] It can be seen that while Solution A excels in transportation efficiency, Solution B has greater advantages in terms of coal transportation costs and equipment failure risk. Therefore, Solution B has a higher comprehensive evaluation index, indicating that Solution B achieves a better balance between the three objectives and is the more preferred option.

[0249] Therefore, in step S2223, the solution with the largest comprehensive evaluation index should be selected as the optimal solution and used as the decision-making solution for the main path. This ensures an optimal balance between transportation costs, equipment failure risk, and transportation efficiency, thereby achieving overall optimization of redundant path planning.

[0250] By using a weighted summation method to evaluate the overall performance of each solution, we can ensure that the final primary path solution strikes a good balance between multiple objectives. This method fully considers the weight and importance of different objectives, thereby achieving the system's optimal combination of transportation costs, equipment failure risk, and transportation efficiency, ensuring overall optimization of redundant path planning.

[0251] Step S2224: According to the main path plan, determine the node sequence that the main path passes through to form the physical topology structure of the main path.

[0252] Furthermore, if Figure 6 As shown, step S2224 includes:

[0253] Step S22241, initialize the node sequence N of the main path main is an empty collection;

[0254] Step S22242: Select the starting point s' of the main path as the current node and add it to N main middle;

[0255] Step S22243: For the current node s', find the path selection variable x that satisfies the main path solution. s't' =1 next node t';

[0256] Step S22244, add node t' to N main and set node t' as the new current node;

[0257] Step S22245, repeating steps S22243 and S22244 until the end of the main path is reached;

[0258] Step S22246: Set the node sequence N of the main path main The physical topology that serves as the primary path.

[0259] Specifically, the purpose of step S2224 is to gradually determine the node sequence passed by the main path according to the path selection rules through a given main path scheme, and finally form the physical topology structure of the path. This process mainly relies on the path selection algorithm in graph theory to help determine the optimal or agreed path in a complex network. The physical topology structure of the main path shows the specific node arrangement of the path, which is crucial for many engineering applications (such as communication networks, transportation systems, coal transportation paths, etc.).

[0260] First, define an empty set N main , used to store the node sequence that the main path passes through. Set N main As the path selection process progresses, it gradually fills up, eventually forming a complete node sequence. Initially, it is empty, indicating that no nodes on the main path have yet been determined. Initializing it to an empty set provides a clean starting point for path construction, avoiding interference from existing data or errors, and ensuring smooth progress in subsequent steps.

[0261] In the process of building a path, a starting point (s') is first selected. In network topology, the starting point is the starting node of the main path and is usually the initial condition for path selection. This node is selected and added to the N mainThe starting point indicates that the path begins at this node and will continue from this node in subsequent steps until it reaches its destination. Determining the starting point provides a clear starting location for path building, making subsequent path building more orderly and logical. For example, in a coal transportation system, the starting point might be the loading area of ​​a coal mine. The starting point of the path selection indicates the beginning of coal transportation route planning.

[0262] The key to step S22243 is the path selection variable x s't' This variable is a decision variable used to indicate whether there is a valid path between the current node s' and the candidate node t'. Specifically, x s't' The value of 1 indicates that there is an optional path from node s' to node t', and x s't' 0 means no path is available. By judging the path selection variable, we can decide which path to take from the current node s' to the next node t'. Using the path selection variable, we can ensure that the path construction follows the given path planning rules. For example, in a complex logistics system, the path selection variable can ensure that coal is accurately transported from one warehouse to another without deviating from the planned route. Assume that the current node is warehouse A, and the candidate nodes include warehouses B, C, and D. According to the path selection rule, if x AB =1 and x AC =0,x AD =0, then the next node will be B.

[0263] Once the next node t' that meets the path selection criteria is found, it is added to N main , indicating that node t' is part of the main path. At the same time, t' is updated to the current node so that the subsequent nodes can be continued to be found in subsequent steps. This process is iterative, and each time a new node is determined, the value of the current node is updated and it continues to move forward until the end node is reached. This step ensures that the construction of the path is continuous, and each step is pushed from one node to the next node, eventually forming a complete path from the starting point to the end point. For example, in the process of coal transportation, this step helps ensure the continuity of the transportation path so that each coal transportation can be carried out on the correct path. For example, if warehouse B is selected as the next node in step S22243, then warehouse B will be added to the node sequence N main and becomes the new current node. Then continue to search for the next feasible node from warehouse B.

[0264] Step S22245 is the core part of the path construction. It gradually adds each node on the main path to N by repeatedly executing steps S22243 and S22244. mainThe algorithm iterates until it reaches the destination node. Each time, it selects the next node that meets the path selection criteria and adds it to the path sequence until no new nodes can be selected. This iterative process ensures that path selection is gradual, and each selection is based on the results of the previous step, ensuring that the final path conforms to the predetermined plan. It can also dynamically adapt to different path selection situations in complex networks. For example, suppose a path starts from warehouse A, passes through nodes B, C, D, and finally reaches destination Z. Each time, based on the path selection rules, it starts from the current node and selects the next node in sequence until it reaches Z.

[0265] In step S22246, the node sequence N of the main path main has been completed and contains all the nodes that the path passes through. This node sequence represents the physical topology of the main path. The topology structure represents the connection relationship between each node in the network and reflects the actual form of the path. In practical applications such as coal transportation, this physical topology structure can be used for subsequent tasks such as path planning, resource scheduling, and system optimization. By obtaining the physical topology structure, support can be provided for subsequent decision-making, such as optimizing resource allocation, improving transportation efficiency, and reducing costs. Path planning is not just a theoretical issue. The actual physical topology structure can directly affect the efficiency and effectiveness of actual operations. For example, assuming that the final path selection is the transportation path from the coal mine to the warehouse, N main The node sequence can be used to construct the topological structure of the entire transportation network, thereby optimizing the logistics system of coal transportation.

[0266] Step S2224 completes the main path determination process step by step through a series of precise operations, from initializing the node sequence to determining the physical topology. s't' In this application, each path selection step is rigorously calculated and planned. The beneficial effect of this method is that it can accurately construct a feasible and optimal path based on a given path planning solution, providing a solid foundation for subsequent path optimization, resource allocation, network management, etc.

[0267] Step S2230: Using the primary route solution, calculate the transportation risk and determine whether a backup route is needed. If a backup route is not needed, form a redundant route set with the primary route. If a backup route is needed, select a backup route from the remaining Pareto efficient solution set after removing the primary route solution.

[0268] Furthermore, if Figure 7 As shown, step S2230 includes:

[0269] Step S2231, select variable x according to the path in the main path solution i'j', determine the path set E that the main path passes through main ;x i'j' Indicates whether to select the path (i', j') from node i' to node j'. If so, then x i'j' =1, otherwise x i'j' =0;

[0270] Step S2232: for each path (i', j')∈E on the main path main , according to the flow distribution ratio f of path (i', j') i'j' , allocate the predicted transport volume TR to the path (i', j'), and obtain the transport volume TR allocated to the path (i', j') i'j' ;

[0271] Step S2233: for each path (i', j')∈E on the main path main , combined with the equipment failure probability p of path (i', j') i'j' Economic losses caused by failure L i'j' , and the transport volume TR allocated to the path (i', j') i'j' , calculate the transportation risk R of path (i',j') i'j' ;

[0272] Step S2234: Sum the transport risks of all paths on the main path to obtain the total transport risk R of the main path. main ;

[0273] Step S2235: The total transport risk R of the main path main Compared with the preset risk threshold R th Compare; if R main >R th , then a backup path needs to be added; otherwise, no backup path is needed.

[0274] Specifically, the core task of step S2230 is to calculate the transportation risk based on the primary route and determine whether to add an alternate route. This process involves a detailed assessment of the transportation risk of the primary route to ensure that coal transportation is carried out under control. If the calculated risk exceeds a preset threshold, an alternate route is added to mitigate the risk. Otherwise, the risk of the primary route is within an acceptable range, and no alternate route is needed.

[0275] In step S2231, first select variable x according to the path in the main path solution i'j' , determine the path set E that the main path passes through main . Path selection variable x i'j' is a binary variable indicating whether a path is selected as part of the main path.

[0276] x i'j' Is a binary variable used to determine whether a path belongs to the main path. i'j' = 1, the path (i', j') is selected as part of the main path; if x i'j' =0, the path (i', j') is not selected as the main path.

[0277] For example, suppose there are two paths connecting node i' and node j', path 1 and path 2. In path selection, if x i'j' = 1 and x of path 2 i'j' = 0, the main path plan will select path 1. The path selection variable helps determine which paths are used in the main path. i'j' , which can clearly define the path set E that the main path passes through main , that is, the set of all paths selected as main paths.

[0278] In step S2232, for each path (i', j')∈E on the main path main , according to the flow distribution ratio f of path (i', j') i'j' , allocate the predicted total transport volume TR to the route. Traffic allocation ratio f i'j' Indicates the proportion of the transport volume allocated to each path in the total transport volume. Calculate the transport volume TR allocated to path (i', j') i'j' The formula is: TR i'j' =f i'j' ×TR;

[0279] For example, suppose the predicted total transport volume TR = 10,000 tons, and the flow distribution ratio of path (i', j') is f i'j' =0.3, then the transport volume allocated to path (i', j') is:

[0280] TR i'j' =0.3×10000=3000 tons;

[0281] This calculation ensures that the transport volume allocated on each route matches the total transport volume demand.

[0282] In step S2233, the device failure probability p of the path (i', j') is combined i'j' , Economic losses caused by equipment failure i'j' , and the transport volume TR allocated to the path (i', j') i'j' , calculate the transportation risk R of path (i',j') i'j' . Transportation Risk R i'j'It is a metric that comprehensively evaluates the possible equipment failures that may occur on a path and their economic impact.

[0283] Equipment failure probability p i'j' It represents the probability of a device failure on the path (i', j') within a certain period of time in the future. This value is usually estimated through historical data, device status monitoring, etc. The higher the device failure probability, the greater the failure risk of the path. For example: If the device failure probability p on the path (i', j') is i'j' = 0.1, then the probability of equipment failure in the future on this path is 10%. The economic loss caused by equipment failure is L i'j' It reflects the economic impact of equipment failure on path (i', j'), including repair costs, profit loss caused by transportation delays, etc.

[0284] The transportation risk R of path (i', j') i'j' It is the comprehensive result of equipment failure probability, transportation volume and economic losses caused by failure. Its calculation formula is:

[0285] R i'j' =p i'j' ×TR i'j' ×L i'j' ;

[0286] This formula represents the failure risk of the transportation task on the path (i', j'), which comprehensively considers the equipment failure probability, transportation volume and economic losses.

[0287] In step S2234, the transportation risk R of all paths on the main path is calculated. i'j' Sum up and get the total transportation risk R of the main path main The total transport risk of the main path is the accumulation of the transport risks on all main paths, which represents the failure risk of the entire main path. Risk threshold R th It is a preset standard that represents the maximum acceptable value of transportation risk. The threshold can be set based on historical data, risk preferences, or the system's risk management strategy. For example, the risk threshold can be set to 80% of the maximum monthly transportation risk in the past year. For example: If the maximum monthly transportation risk in the past year is RMB 40,000,000, the risk threshold R can be set to 80%. th Set to 32,000,000 yuan.

[0288] If R main >R th , it means that the transportation risk of the main path exceeds the acceptable range, and it is necessary to add a backup path to disperse the risk and ensure the stable operation of the system.

[0289] If R main ≤R th, the transportation risk of the main route is controllable and no backup route is needed.

[0290] By calculating and assessing transportation risks along the primary route, the system can identify and address potential risks during transportation. If the risk on the primary route exceeds acceptable limits, adding a backup route effectively mitigates the risk and avoids transportation disruptions due to equipment failure or other reasons. Furthermore, if the risk is manageable, adding a backup route is avoided, saving resources and improving efficiency. This approach enhances the risk management capabilities of the coal transportation system, ensuring the smooth completion of transportation tasks.

[0291] Step S2240 , selecting a backup path from the remaining Pareto efficient solutions after removing the primary path solution;

[0292] Furthermore, if Figure 8 As shown, step S2240 includes:

[0293] Step S2241: Eliminate the optimal solution corresponding to the main path from the Pareto efficient solution set to form a set of alternative paths;

[0294] Step S2242: Calculate the transportation risk R of each alternative path in the alternative path set. b , selecting the first N1 solutions with the lowest transportation risk in the alternative path set to form a first alternative path set, wherein the first alternative path set includes N1 first alternative paths;

[0295] Step S2243: Calculate the similarity SI between the first backup path and the primary path k , SI k Indicates the similarity between the kth first backup path and the primary path; 1≤k≤N1;

[0296] Step S2244, based on the similarity SI k The first backup paths are sorted in ascending order, and the first N2 first backup paths are selected as final backup paths; N2≤N1.

[0297] Step S2250: Combine the primary path and the backup path to form a redundant path set.

[0298] Specifically, step S2240 aims to select a suitable backup route from a set of optimized routes that satisfy the Pareto efficiency criteria. The backup route must minimize transportation risk and have a low degree of similarity to the primary route to disperse risk and improve the system's robustness and ability to respond to emergencies.

[0299] In multi-objective optimization, the Pareto-efficient solution set represents the optimal compromise between all optimization objectives. During this process, the optimal solution corresponding to the primary path (i.e., the primary path itself) is first removed from these Pareto-efficient solutions, leaving the remaining candidate paths to form a set of alternative paths. This ensures that the selected backup paths do not overlap with the primary path, effectively providing redundancy and dispersing risk. By eliminating the primary path, the diversity of the backup path set is ensured, thereby improving the robustness of the system. This diversity of backup paths can help cope with various emergencies, such as when the primary path is blocked by external factors such as equipment failure and weather.

[0300] In the set of alternative paths, the paths need to be screened based on the transportation risk. The same method as step S2230 is used to calculate the transportation risk of the alternative paths. By calculating the transportation risk of each alternative path, the top N1 paths with the lowest risk are selected as the first set of alternative paths. The transportation risk of these paths is low, which means that they are relatively stable and reliable, and can ensure the safety of transportation. Selecting the path with the lowest transportation risk can minimize the unexpected situations that may occur during the path selection process. Compared with other paths, these paths can better ensure the safety, stability and efficiency of transportation. For example, if the main path cannot be used due to some factors, selecting a path with low transportation risk can greatly reduce the probability of accidents and ensure the smooth progress of the transportation task.

[0301] The calculation of similarity in step S2243 is to measure the degree of overlap between the backup path and the main path, and the node overlap index is usually used to measure the similarity of the paths. Node overlap refers to the ratio of the number of nodes shared between the first backup path and the main path to the total number of nodes in the path. The lower the overlap of the paths, the higher the degree of difference between the first backup path and the main path, which is conducive to risk dispersion. The purpose of calculating the similarity is to further select backup paths that are more different from the main path. By selecting paths with lower similarity, the risks that may arise during transportation can be effectively dispersed. For example, if the main path cannot be used due to traffic congestion, selecting a backup path with low node overlap with the main path can avoid failures under the influence of the same problem and enhance the resilience of the transportation system. For example: If the main path passes through nodes A, B, C, and D, and a backup path passes through nodes B, C, E, and F, the node overlap is This means that the backup path has 33% node overlap with the primary path. If the backup path has a low overlap, it means that the backup path is geographically different from the primary path, and therefore can provide an effective alternative when the primary path is blocked.

[0302] After calculating the similarity between each first backup path and the primary path, the backup paths are sorted from lowest to highest similarity. The first N2 backup paths with the lowest similarity are selected as the final backup paths. These paths have low node overlap, effectively avoiding the impact of primary path problems and reducing transportation risks. By selecting paths with low node overlap as backup paths, risk is dispersed and the system's resilience is enhanced. Selecting paths with the lowest similarity means that backup paths provide more effective alternatives when the primary path is blocked, improving the flexibility and resilience of the coal transportation system.

[0303] Once the primary path and optimal backup path have been selected, they can be combined to form a redundant path set. A redundant path set is a complete set of paths that includes the primary path and all backup paths. Each path should be assigned a unique identification number and sorted from low to high based on transportation risk, with lower-risk paths prioritized. This redundant path set provides multiple options for coal transportation, ensuring that if an incident occurs on any path, other paths remain available. By sorting paths by risk, the safest and most stable paths are prioritized in the event of an anomaly, significantly improving system stability and emergency response capabilities. Assume that the primary path has a lower transportation risk, the first backup path has a slightly higher risk, and the second backup path has an even higher risk. During actual transportation, if a problem occurs on the primary path, the backup path with the lowest risk will be prioritized to ensure uninterrupted transportation.

[0304] Step S3000: Perform intelligent flow control based on the redundant path set.

[0305] Furthermore, if Figure 9 As shown, step S3000 includes:

[0306] Step S3100: collecting coal flow rates of the primary route and the backup route in real time;

[0307] Step S3200, calculating the ratio of the coal flow rate of the main path to the coal flow rate of the backup path to obtain the flow balance;

[0308] Step S3300: determine whether the flow balance exceeds the balance threshold. If it exceeds the balance threshold, adjust the coal flow distribution.

[0309] Specifically, step S3000 aims to dynamically adjust the flow rates of the primary and backup paths in the coal transportation system through intelligent flow control based on a set of redundant paths. This ensures balanced flow distribution and improves system stability and operational efficiency. The key to intelligent flow control lies in real-time flow monitoring and flow adjustment based on preset flow balance thresholds, ensuring that the load on the primary and backup paths remains within a reasonable range.

[0310] First, the system needs to collect real-time coal flow along both the primary and backup routes. Coal flow here refers to the amount of coal transported per unit time, typically measured in tons per hour or other appropriate units. The primary route is the primary transportation route, while the backup route is used to share the transport load. Backup routes are particularly important when traffic on the primary route is excessive. To collect real-time flow data, flow monitoring equipment or sensors are typically deployed. These devices can accurately measure the amount of coal passing through each route. For example, sensors can monitor vehicle flow on rail or road transport, and combined with GPS and other positioning systems, accurate flow data can be calculated. Real-time flow monitoring provides a timely understanding of the load on both the primary and backup routes. If the primary route shows signs of overload, prompt action can be taken to redirect some traffic to the backup route, avoiding delays or system overload. For example, if traffic on the primary route suddenly increases, timely flow data allows the backup route to respond more quickly, share the load, and avoid congestion or failures.

[0311] The goal of step S3200 is to calculate the flow balance, that is, the ratio of the main path flow to the backup path flow, and determine the load balance between them. The flow balance is an indicator to measure the rationality of the path load distribution. If the flow of the main path is much larger than the flow of the backup path, the flow balance will be higher, indicating that the main path is overloaded and the backup path is not fully utilized. The calculation of the flow balance provides a quantitative basis for subsequent flow adjustments. By calculating the balance in real time, it is possible to detect the imbalance of path loads in a timely manner and ensure reasonable flow distribution. For example, when the flow balance is high, the system can automatically adjust the flow based on this information, directing part of the flow from the main path to the backup path to avoid overloading the main path, thereby improving transportation efficiency and ensuring smooth operation of the system.

[0312] Step S3300 compares the calculated flow balance with the preset balance threshold. If the flow balance exceeds the set balance threshold, it means that the load on the main path is too heavy, while the load on the backup path is light. At this time, the system needs to adjust the distribution of coal flow. If the flow balance exceeds the balance threshold, it means that the load on the main path is too heavy. The system must adjust the flow and allocate more flow to the backup path to reduce the burden on the main path. If the flow balance is within a reasonable range, it means that the flow distribution of the main path and the backup path is relatively balanced and no adjustment is required. By setting the balance threshold and making timely judgments, the system can automatically respond when the load is uneven, avoid overloading a single path, and thus improve the stability of the transportation system. Assuming that during the peak transportation period, the main path traffic is overloaded, the system can automatically identify and guide the backup path to share part of the traffic, avoiding the risk of overloading the main path and ensuring transportation efficiency.

[0313] When the flow balance exceeds the threshold, the coal flow of the main path and the backup path needs to be dynamically adjusted to make the load between them more balanced. The adjustment formula can be made according to the following method:

[0314]

[0315] Among them, F main is the coal flow of the main path, is the coal flow of the main path after adjustment, F backup is the coal flow of the alternative path, is the adjusted coal flow rate on the backup route; Δf is the flow adjustment ratio, a parameter that is dynamically adjusted based on actual demand. For example, when the primary route is overloaded, Δf can be set to 0.1, which means that the primary route flow rate is reduced by 10% and the backup route flow rate is increased by 10%. This adjusted flow distribution will achieve a more balanced flow rate between the primary and backup routes, thereby improving the stability of the coal transportation system. This adjustment formula provides a dynamic flow distribution mechanism. The flow adjustment ratio Δf allows for flexible response to flow fluctuations in different situations. The adjusted flow rate will achieve a more balanced flow rate between the primary and backup routes, thereby improving system stability. For example, if the primary route is congested, this formula can be used to shift traffic from the primary route to the backup route, reducing the burden on the primary route and ensuring uninterrupted coal transportation.

[0316] Once traffic adjustment is complete, the system will continue to monitor traffic changes on the primary and backup paths in real time. If the adjusted traffic distribution still does not reach an acceptable balance, the system will enter a cyclic adjustment phase, continuously optimizing traffic distribution until the ideal balance is achieved. This process ensures the continuity and stability of traffic adjustment. Continuous monitoring and adjustment ensure that the system is always in optimal operating condition, preventing overload or resource waste on any single path. Through cyclic adjustment, the system can adaptively adjust traffic distribution to ensure that the load on the primary and backup paths always remains within a reasonable range, improving overall transportation efficiency and system reliability. For example, when multiple peak periods occur alternately, the system can adjust traffic flow based on real-time data at each moment to avoid transportation delays caused by uneven path loads.

[0317] Step S3000 implements intelligent flow control based on a set of redundant paths. This ensures the stability and efficiency of the coal transportation system through real-time monitoring, calculation of flow balance, and adjustment of flow distribution when necessary. Through a rational flow adjustment formula and cyclic optimization mechanism, the system dynamically responds to changes in transport load, ensuring that the system provides stable and efficient service under varying conditions. The beneficial effects of real-time monitoring and intelligent adjustment not only improve transportation efficiency but also reduce the risks and failures caused by uneven flow, ensuring the smooth completion of coal transportation tasks.

[0318] Example 2

[0319] Based on Example 1, this embodiment provides a redundant path design system for improving the stability of the coal transportation system. Figure 10 Shown, including:

[0320] Model building module: obtaining historical operating data of the coal transmission system, building a first data set based on the historical operating data; building and training an equipment failure prediction model and a transportation demand prediction model based on the first data set;

[0321] Prediction module: Uses the trained equipment failure prediction model and transportation demand prediction model to predict the equipment health status and transportation demand within a preset time, and obtains the first prediction result and the second prediction result;

[0322] Multi-objective optimization module: obtains the network topology of the coal transportation system, constructs a redundant path multi-objective optimization model based on the first prediction result and the second prediction result, as well as the network topology of the coal transportation system; obtains a redundant path set based on the redundant path multi-objective optimization model;

[0323] Control module: performs intelligent flow control based on a set of redundant paths.

[0324] In the model construction module, the historical operation data includes historical equipment operating parameters and historical coal transportation volume data; the historical equipment operating parameters and historical coal transportation volume data are sorted by time to form a historical equipment operating parameter sequence and a historical coal transportation volume data sequence; and constructing the first data set includes:

[0325] Step S1210 , statistically analyzing the historical equipment operating condition parameter sequence, extracting historical health status features, and forming a historical health status feature sequence;

[0326] Step S1220, performing time series analysis on the coal transportation volume data series, extracting historical transportation demand indicators, and forming a historical transportation demand indicator series;

[0327] Step S1230: Combine the historical health status feature sequence and the historical transportation demand index sequence to form a first data set.

[0328] In the model building module, the building and training of the equipment failure prediction model and the transportation demand prediction model includes:

[0329] Step S1310 , using a time series prediction algorithm to build an equipment failure prediction model based on historical health status feature sequences;

[0330] Step S1320: Using a regression prediction algorithm, a transportation demand prediction model is constructed based on the historical transportation demand indicator sequence.

[0331] In the prediction module, obtaining the first prediction result and the second prediction result includes:

[0332] Step S1410: obtaining a real-time equipment operating condition parameter sequence, obtaining a real-time health status feature sequence based on the real-time equipment operating condition parameter sequence, and inputting the real-time health status feature sequence into a trained equipment failure prediction model to obtain a first prediction result;

[0333] Step S1420: Acquire a real-time coal transportation volume data sequence, obtain a real-time transportation demand index sequence based on the real-time coal transportation volume data sequence, input the real-time transportation demand index sequence into a trained transportation demand prediction model, and obtain a second prediction result.

[0334] The first prediction result includes the equipment failure rate FA within a preset time, and the second prediction result includes the predicted transportation volume TR within a preset time.

[0335] In the multi-objective optimization module, the construction of the redundant path multi-objective optimization model includes:

[0336] Step S2100: Acquire the network topology of the coal transportation system and construct a redundant path multi-objective optimization model based on the equipment failure rate FA and the predicted transportation volume TR within a preset time, as well as the network topology of the coal transportation system; the network topology of the coal transportation system includes a path set and a node set;

[0337] The step S2100 includes:

[0338] Step S2110 , using the equipment failure rate FA and the predicted transport volume TR within a preset time as constraints for the redundant path multi-objective optimization model;

[0339] Step S2120 , defining decision variables of the redundant path multi-objective optimization model, including path selection variables and traffic distribution ratio;

[0340] Step S2130 , determining the objective function of the redundant path multi-objective optimization model, including minimizing coal transportation costs, minimizing equipment failure risks, and maximizing transportation efficiency;

[0341] Step S2140 , constructing a redundant path multi-objective optimization model based on the path set and node set in the network topology structure of the coal transportation system, and the decision variables and objective function of the redundant path multi-objective optimization model.

[0342] In the multi-objective optimization module, obtaining the redundant path set according to the redundant path multi-objective optimization model includes:

[0343] Step S2210, solving the redundant path multi-objective optimization model to obtain a Pareto efficient solution set; each solution in the Pareto efficient solution set corresponds to a redundant path solution;

[0344] Step S2220, selecting a main path from the Pareto efficient solution set;

[0345] Step S2230: Using the primary route solution, calculate the transportation risk and determine whether a backup route is needed. If a backup route is not needed, form a redundant route set with the primary route. If a backup route is needed, select a backup route from the remaining Pareto efficient solution set after removing the primary route solution.

[0346] Step S2240 , selecting a backup path from the remaining Pareto efficient solutions after removing the primary path solution;

[0347] Step S2250: Combine the primary path and the backup path to form a redundant path set.

[0348] The step S2220 includes:

[0349] Step S2221, calculate the normalized value y of each solution in the Pareto efficient solution set on each targetl,g , the objectives include coal transportation cost, equipment failure risk and transportation efficiency, y l,g represents the normalized value of the lth solution in the Pareto efficient solution set on the gth objective; 1≤g≤m, m is the total number of objectives; l is the index of the solution in the Pareto efficient solution set;

[0350] Step S2222, set the target weight vector w=[w1,w2,…,w m ],w m is the weight of the mth target; the weighted summation method is used, based on the target weight vector w and the normalized value y of each solution on each target l,g , calculate the comprehensive evaluation index for each solution; among them, w1+w2+…+w m =1;

[0351] Step S2223: The solution with the largest comprehensive evaluation index is selected as the optimal solution, and the redundant path solution corresponding to the optimal solution is selected as the main path solution;

[0352] Step S2224: According to the main path plan, determine the node sequence that the main path passes through to form the physical topology structure of the main path.

[0353] The step S2224 includes:

[0354] Step S22241, initialize the node sequence N of the main path main is an empty collection;

[0355] Step S22242: Select the starting point s' of the main path as the current node and add it to N main middle;

[0356] Step S22243: For the current node s', find the path selection variable x that satisfies the main path solution. s't' =1 next node t';

[0357] Step S22244, add node t' to N main and set node t' as the new current node;

[0358] Step S22245, repeating steps S22243 and S22244 until the end of the main path is reached;

[0359] Step S22246: Set the node sequence N of the main path main The physical topology that serves as the primary path.

[0360] The step S2230 includes:

[0361] Step S2231, select variable x according to the path in the main path solutioni'j' , determine the path set E that the main path passes through main ;x i'j' Indicates whether to select the path (i', j') from node i' to node j'. If so, then x i'j' =1, otherwise x i'j' =0;

[0362] Step S2232: for each path (i', j')∈E on the main path main , according to the flow distribution ratio f of path (i', j') i'j' , allocate the predicted transport volume TR to the path (i', j'), and obtain the transport volume TR allocated to the path (i', j') i'j' ;

[0363] Step S2233: for each path (i', j')∈E on the main path main , combined with the equipment failure probability p of path (i', j') i'j' Economic losses caused by failure L i'j' , and the transport volume TR allocated to the path (i', j') i'j' , calculate the transportation risk R of path (i',j') i'j' ;

[0364] Step S2234: Sum the transport risks of all paths on the main path to obtain the total transport risk R of the main path. main ;

[0365] Step S2235: The total transport risk R of the main path main Compared with the preset risk threshold R th Compare; if R main >R th , then a backup path needs to be added; otherwise, no backup path is needed.

[0366] The step S2240 includes:

[0367] Step S2241: Eliminate the optimal solution corresponding to the main path from the Pareto efficient solution set to form a set of alternative paths;

[0368] Step S2242: Calculate the transportation risk R of each alternative path in the alternative path set. b , selecting the first N1 solutions with the lowest transportation risk in the alternative path set to form a first alternative path set, wherein the first alternative path set includes N1 first alternative paths;

[0369] Step S2243: Calculate the similarity SI between the first backup path and the primary path k , SI kIndicates the similarity between the kth first backup path and the primary path; 1≤k≤N1;

[0370] Step S2244, based on the similarity SI k The first backup paths are sorted in ascending order, and the first N2 first backup paths are selected as final backup paths; N2≤N1.

[0371] In the control module, the intelligent flow control based on the redundant path set includes:

[0372] Step S3100: collecting coal flow rates of the primary route and the backup route in real time;

[0373] Step S3200, calculating the ratio of the coal flow rate of the main path to the coal flow rate of the backup path to obtain the flow balance;

[0374] Step S3300: determine whether the flow balance exceeds the balance threshold. If it exceeds the balance threshold, adjust the coal flow distribution.

[0375] Example 3

[0376] This embodiment discloses an electronic device that may include one or more processors and one or more memories. The memories may store computer-readable code that, when executed by the one or more processors, may execute the redundant path design method for improving the stability of a coal transportation system.

[0377] The method or system according to the embodiment of the present application can also be implemented with the help of the architecture of an electronic device. The electronic device may include a bus, one or more CPUs, a read-only memory (ROM), a random access memory (RAM), a communication port connected to a network, an input / output component, a hard disk, etc. The storage device in the electronic device, such as a ROM or a hard disk, can store the redundant path design method for improving the stability of the coal transmission system provided by the present application. The redundant path design method for improving the stability of the coal transmission system may, for example, include: obtaining historical operating data of the coal transmission system, and constructing a first data set based on the historical operating data; constructing and training an equipment failure prediction model and a transportation demand prediction model based on the first data set; using the trained equipment failure prediction model and transportation demand prediction model, predicting the equipment health status and transportation demand within a preset time, and obtaining a first prediction result and a second prediction result; obtaining the network topology of the coal transportation system, and constructing a redundant path multi-objective optimization model based on the first prediction result and the second prediction result, as well as the network topology of the coal transportation system; obtaining a redundant path set based on the redundant path multi-objective optimization model; and performing intelligent flow control based on the redundant path set.

[0378] Furthermore, the electronic device may further include a user interface. Of course, the architecture disclosed in the present invention is only exemplary, and when implementing different devices, one or more components in the electronic device disclosed in the present invention may be omitted according to actual needs.

[0379] Example 4

[0380] This embodiment discloses a computer-readable storage medium having computer-readable instructions stored thereon. When the computer-readable instructions are executed by a processor, the redundant path design method for improving the stability of a coal transportation system according to an embodiment of the present application can be executed. The storage medium includes, but is not limited to, volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory (RAM) and cache memory. Non-volatile memory may include, for example, read-only memory (ROM), a hard disk, and flash memory.

[0381] In addition, according to the embodiment of the present application, the process described with reference to the flowchart above can be implemented as a computer software program. For example, the present application provides a non-transitory machine-readable storage medium, which stores machine-readable instructions, and the machine-readable instructions can be executed by a processor to execute instructions corresponding to the method steps provided in the present application, such as: obtaining historical operating data of the coal transportation system, and constructing a first data set based on the historical operating data; constructing and training an equipment failure prediction model and a transportation demand prediction model based on the first data set; using the trained equipment failure prediction model and transportation demand prediction model, predicting the equipment health status and transportation demand within a preset time, and obtaining a first prediction result and a second prediction result; obtaining the network topology of the coal transportation system, and constructing a redundant path multi-objective optimization model based on the first prediction result and the second prediction result, as well as the network topology of the coal transportation system; obtaining a redundant path set based on the redundant path multi-objective optimization model; and performing intelligent flow control based on the redundant path set. When the computer program is executed by a central processing unit (CPU), the above functions defined in the method of the present application are executed.

[0382] The methods, systems, and devices of the present application may be implemented in many ways. For example, the methods, systems, and devices of the present application may be implemented by software, hardware, firmware, or any combination of software, hardware, and firmware. The above order of steps for the method is for illustration only, and the steps of the method of the present application are not limited to the order specifically described above unless otherwise specified. In addition, in some embodiments, the present application may also be implemented as programs recorded in a recording medium, which include machine-readable instructions for implementing the methods according to the present application. Therefore, the present application also covers recording media that store programs for executing the methods according to the present application.

[0383] In addition, the parts of the above technical solutions provided in the embodiments of the present application that are consistent with the implementation principles of the corresponding technical solutions in the prior art are not described in detail to avoid excessive redundancy.

[0384] The above-described specific embodiments further illustrate the objectives, technical solutions, and beneficial effects of the present invention. It should be understood that the above description is merely a specific embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.

Claims

1. A redundant path design method for improving the stability of a coal transmission system, characterized in that: The method comprises: Acquire historical operating data of the coal transportation system and construct a first data set based on the historical operating data; construct and train an equipment failure prediction model and a transportation demand prediction model based on the first data set; use the trained equipment failure prediction model and transportation demand prediction model to predict the equipment health status and transportation demand within a preset time period to obtain a first prediction result and a second prediction result; Obtaining a network topology of the coal transportation system, and constructing a redundant path multi-objective optimization model based on the first prediction result and the second prediction result, as well as the network topology of the coal transportation system; obtaining a redundant path set according to the redundant path multi-objective optimization model; and performing intelligent flow control based on the redundant path set; The construction of the redundant path multi-objective optimization model includes: Obtaining a network topology of a coal transportation system, the network topology of the coal transportation system including a set of paths and a set of nodes; using an equipment failure rate FA and a predicted transportation volume TR within a preset time as constraints of a redundant path multi-objective optimization model; defining decision variables of the redundant path multi-objective optimization model, the decision variables including a path selection variable and a flow distribution ratio; determining an objective function of the redundant path multi-objective optimization model, the objective function including minimizing coal transportation costs, minimizing equipment failure risks, and maximizing transportation efficiency; and constructing a redundant path multi-objective optimization model based on the set of paths and the set of nodes in the network topology of the coal transportation system, as well as the decision variables and objective function of the redundant path multi-objective optimization model; The obtaining of the redundant path set according to the redundant path multi-objective optimization model includes: Solve the redundant path multi-objective optimization model to obtain a Pareto efficient solution set; each solution in the Pareto efficient solution set corresponds to a redundant path solution; select a primary path from the Pareto efficient solution set; use the primary path solution to calculate the transportation risk and determine whether a backup path is needed; if a backup path is not needed, the primary path forms a redundant path set; if a backup path is needed, the backup path is selected from the remaining Pareto efficient solution set after eliminating the primary path solution; and the primary path and the backup path are combined to form a redundant path set.

2. The redundant path design method for improving the stability of the coal transportation system according to claim 1, characterized in that: The historical operation data includes historical equipment operating parameters and historical coal transportation volume data; the historical equipment operating parameters and historical coal transportation volume data are sorted by time to form a historical equipment operating parameter sequence and a historical coal transportation volume data sequence; The constructing the first data set includes: Perform statistical analysis on the historical equipment operating condition parameter sequence, extract historical health status characteristics, and form a historical health status feature sequence; Conduct time series analysis on the coal transportation volume data series, extract historical transportation demand indicators, and form a historical transportation demand indicator series; The historical health status feature sequence and the historical transportation demand indicator sequence are combined to form a first data set.

3. The redundant path design method for improving the stability of the coal transportation system according to claim 2, characterized in that: The construction and training of the equipment failure prediction model and the transportation demand prediction model includes: Using a time series prediction algorithm, we build an equipment failure prediction model based on the historical health status feature sequence. Using a regression prediction algorithm, we build a transportation demand prediction model based on the historical transportation demand indicator sequence. The first prediction result includes the equipment failure rate FA within a preset time, and the second prediction result includes the predicted transportation volume TR within a preset time.

4. The redundant path design method for improving the stability of the coal transportation system according to claim 3 is characterized in that: The selecting of the main path from the Pareto efficient solution set includes: Calculate the normalized value y of each solution in the Pareto efficient solution set on each objective l,g , the objectives include coal transportation cost, equipment failure risk and transportation efficiency, y l,g represents the normalized value of the lth solution in the Pareto efficient solution set on the gth objective; 1≤g≤m, m is the total number of objectives; l is the index of the solution in the Pareto efficient solution set; Set the target weight vector w=[w1,w2,…,w m ],w m is the weight of the mth target; the weighted summation method is used, based on the target weight vector w and the normalized value y of each solution on each target l,g , calculate the comprehensive evaluation index for each solution; among them, w1+w2+…+w m =1; The solution with the largest comprehensive evaluation index is taken as the optimal solution, and the redundant path solution corresponding to the optimal solution is selected as the main path solution; According to the main path plan, the node sequence that the main path passes through is determined to form the physical topology structure of the main path.

5. The redundant path design method for improving the stability of the coal transportation system according to claim 4, characterized in that: The physical topology structure forming the main path includes: Step S22241, initialize the node sequence N of the main path main is an empty collection; Step S22242: Select the starting point s' of the main path as the current node and add it to N main middle; Step S22243: For the current node s', find the path selection variable x that satisfies the main path solution. s't' =1 next node t'; Step S22244, add node t' to N main and set node t' as the new current node; Step S22245, repeating steps S22243 and S22244 until the end of the main path is reached; Step S22246: Set the node sequence N of the main path main The physical topology that serves as the primary path.

6. The redundant path design method for improving the stability of the coal transportation system according to claim 5, characterized in that: Determining whether a backup path is needed includes: Select variable x according to the path in the main path scheme i'j' , determine the path set E that the main path passes through main ;x i'j' Indicates whether to select the path (i', j') from node i' to node j'. If so, then x i'j' =1, otherwise x i'j' =0; For each path (i', j')∈E on the main path main , according to the flow distribution ratio f of path (i', j') i'j' , allocate the predicted transport volume TR to the path (i', j'), and obtain the transport volume TR allocated to the path (i', j') i'j' ; For each path (i', j')∈E on the main path main , combined with the equipment failure probability p of path (i', j') i'j' Economic losses caused by failure L i'j' , and the transport volume TR allocated to the path (i', j') i'j' , calculate the transportation risk R of path (i',j') i'j' ; Sum the transportation risks of all paths on the main path to obtain the total transportation risk R of the main path main ; The total transportation risk R of the main path main Compared with the preset risk threshold R th Compare; if R main >R th , then a backup path is required; otherwise, no backup path is required.

7. The redundant path design method for improving the stability of a coal transportation system according to claim 6, characterized in that: The step of selecting a backup path from the remaining Pareto efficient solution set after removing the primary path solution includes: Eliminate the optimal solution corresponding to the main path from the Pareto efficient solution set to form a set of alternative paths; Calculate the transportation risk R of each alternative path in the alternative path set b , selecting the first N1 solutions with the lowest transportation risk in the alternative path set to form a first alternative path set, wherein the first alternative path set includes N1 first alternative paths; Calculate the similarity SI between the first backup path and the main path k , SI k Indicates the similarity between the kth first backup path and the primary path; 1≤k≤N1; According to the similarity SI k The first backup paths are sorted in ascending order, and the first N2 first backup paths are selected as final backup paths; N2≤N1.

8. A redundant path design system for improving the stability of a coal transportation system, which is used to implement the redundant path design method for improving the stability of a coal transportation system according to any one of claims 1 to 7, characterized in that: The system comprises: Model building module: obtaining historical operating data of the coal transmission system, building a first data set based on the historical operating data; building and training an equipment failure prediction model and a transportation demand prediction model based on the first data set; Prediction module: Uses the trained equipment failure prediction model and transportation demand prediction model to predict the equipment health status and transportation demand within a preset time, and obtains the first prediction result and the second prediction result; Multi-objective optimization module: obtains the network topology of the coal transportation system, constructs a redundant path multi-objective optimization model based on the first prediction result and the second prediction result, as well as the network topology of the coal transportation system; obtains a redundant path set based on the redundant path multi-objective optimization model; Control module: intelligent flow control based on redundant path sets; The construction of the redundant path multi-objective optimization model includes: Obtaining a network topology of a coal transportation system, the network topology of the coal transportation system including a set of paths and a set of nodes; using an equipment failure rate FA and a predicted transportation volume TR within a preset time as constraints of a redundant path multi-objective optimization model; defining decision variables of the redundant path multi-objective optimization model, the decision variables including a path selection variable and a flow distribution ratio; determining an objective function of the redundant path multi-objective optimization model, the objective function including minimizing coal transportation costs, minimizing equipment failure risks, and maximizing transportation efficiency; and constructing a redundant path multi-objective optimization model based on the set of paths and the set of nodes in the network topology of the coal transportation system, as well as the decision variables and objective function of the redundant path multi-objective optimization model; The obtaining of the redundant path set according to the redundant path multi-objective optimization model includes: Solve the redundant path multi-objective optimization model to obtain a Pareto efficient solution set; each solution in the Pareto efficient solution set corresponds to a redundant path solution; select a primary path from the Pareto efficient solution set; use the primary path solution to calculate the transportation risk and determine whether a backup path is needed; if a backup path is not needed, the primary path forms a redundant path set; if a backup path is needed, the backup path is selected from the remaining Pareto efficient solution set after eliminating the primary path solution; and the primary path and the backup path are combined to form a redundant path set.

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