Coal transportation and sales management system based on deep learning

Abstract

The invention relates to the technical field of coal transportation and sales management, and discloses a coal transportation and sales management system based on deep learning. The system comprises a multi-source data acquisition and processing module which collects and preprocesses coal quality, a transportation state and market sales data; the coal demand prediction module predicts a market demand trend based on the preprocessed data; the inventory optimization scheduling module optimizes the inventory level according to the demand trend; the transportation task dynamic allocation module allocates transportation tasks in combination with the optimized inventory; the abnormity monitoring and analyzing module monitors transportation abnormity; the constraint evaluation and optimization module evaluates transportation resource constraints and optimizes task allocation; the real-time response adjustment module dynamically adjusts the transportation task according to the abnormality and constraint evaluation result; the life cycle prediction module predicts the aging trend of inventory equipment; the energy efficiency evaluation module evaluates the transportation energy efficiency; and the maintenance decision optimization module optimizes a maintenance strategy in combination with the aging trend and the energy efficiency evaluation result. The system realizes intelligent management of the whole coal transportation and sale process.

Description

Technical Field

[0001] The present invention relates to the technical field of coal transportation and marketing management, and specifically to a coal transportation and marketing management system based on deep learning. Background Art

[0002] Throughout the development of the coal industry, transportation and marketing management, as a crucial link between production and the market, has a direct impact on both operational profitability and the overall development of the industry. Traditional coal transportation and marketing management models, which rely heavily on manual decision-making based on experience, suffer from numerous drawbacks that are difficult to overcome. From a data collection perspective, data on coal quality, transportation status, and market sales is often scattered across different processes and systems, lacking a unified collection and processing mechanism. Inconsistent data formats and collection frequencies make it difficult to ensure data integrity and accuracy, making it impossible to provide a reliable basis for subsequent decision-making. Traditional demand forecasting methods rely on simple trend analysis based on historical sales data, ignoring various factors such as market dynamics, policy changes, and seasonality. This forecasting approach often results in significant errors, making it difficult for companies to rationally arrange production and inventory based on market demand, leading to supply shortages and inventory overstocking. In inventory management, due to the lack of scientific demand forecasting as a guide, inventory levels are set with a high degree of blindness. Excessive inventory increases storage costs and capital occupancy, while too low inventory can lead to an inability to meet market demand in a timely manner, affecting the company's reputation and sales performance. Traditionally, transportation tasks are allocated according to fixed routes and modes, making it difficult to dynamically adjust them based on real-time inventory levels, traffic conditions, and market demand. This not only wastes transportation resources but also potentially prolongs transportation times, impacting the timely supply of coal. During transportation, the ability to monitor and handle abnormal situations is weak, often requiring only post-problem remediation, rather than early warning and timely resolution. Furthermore, insufficient assessment of transportation resource constraints makes it difficult to achieve optimal task allocation, further reducing transportation efficiency.

[0003] Traditional equipment maintenance methods rely on scheduled maintenance, but lack accurate prediction of equipment aging trends and assessment of energy efficiency levels. This can lead to untimely maintenance, impacting normal equipment operation, or excessive maintenance, increasing maintenance costs. Summary of the Invention

[0004] The purpose of the present invention is to provide a coal transportation and marketing management system based on deep learning to solve the problems raised in the above background technology.

[0005] In order to achieve the above object, the present application provides a coal transportation and marketing management system based on deep learning, which comprises: A multi-source data acquisition and processing module is used to collect coal quality data, transportation state data and market sales data, and perform data preprocessing; A coal demand prediction module is used to predict the coal market demand trend based on the preprocessed data; An inventory optimization and scheduling module is used to optimize the coal inventory level according to the coal market demand trend; A transportation task dynamic allocation module is used to allocate transportation tasks based on the optimized coal inventory level; An abnormality monitoring and analysis module is used to monitor and analyze abnormal conditions in the transportation task execution process; A constraint evaluation and optimization module is used to evaluate transportation resource constraints and optimize task allocation; A real-time response adjustment module is used to dynamically adjust transportation tasks according to abnormal conditions and constraint evaluation results; A life cycle prediction module is used to predict the aging trend of coal inventory equipment; An energy efficiency evaluation module is used to evaluate the energy efficiency level of the coal transportation process; A maintenance decision optimization module is used to optimize maintenance strategies in combination with the aging trend and energy efficiency level.

[0006] Preferably, the specific steps of data preprocessing performed by the multi-source data acquisition and processing module are as follows: Receive coal quality data, transportation state data and market sales data collected by different sensors, group data of the same type and synchronize sampling time steps through time windows, and then perform unit standardization processing; Sort each group of data and calculate the first quartile and the third quartile, determine the upper and lower boundaries of the data based on the interquartile range, and mark the abnormal values that exceed the boundaries; Statistical missing values in each group of data and fill them in through linear interpolation, then replace the abnormal values through mean filling, reverify the data quality and normalize the data to a specific range.

[0007] Preferably, the specific steps of predicting the coal market demand trend by the coal demand prediction module are as follows: A deep learning model is established based on the preprocessed data, which includes an input layer, a feature extraction layer and an output layer; The input layer receives preprocessed data and historical demand data; The feature extraction layer extracts the time and space features of the data through deep learning algorithms; The output layer outputs the coal market demand trend prediction results based on the extracted features.

[0008] Preferably, the specific steps of optimizing the coal inventory level by the inventory optimization scheduling module are as follows: Build an inventory optimization model based on the coal market demand trend forecast results; The historical inventory data is divided into a training set and a test set. The training set is input into the inventory optimization model and the inventory optimization value is output through forward propagation. Compare the optimized inventory value with the historical actual inventory value to calculate the model error value. If the error value is higher than the preset threshold, adjust the model parameters through back propagation; After training, the model is trained using the test set to verify the convergence of the error value and output the optimized coal inventory level.

[0009] Preferably, the specific steps of allocating transport tasks by the transport task dynamic allocation module are as follows: Establish a task allocation model based on the optimized coal inventory level; Analyze the availability of transportation resources to generate an action space and set a reward function based on resource costs and benefits; Generate a child node tree with the current transportation status as the root node, and select nodes layer by layer through the confidence bound selection strategy; Expand the simulation of task allocation evolution by unused transport operations, calculate the strategy benefits, and then backtrack to update the node's cumulative rewards and visit counts; After iterative optimization, the node path with the highest cumulative reward is selected to build a complete transportation task allocation plan.

[0010] Preferably, the specific steps of the abnormal monitoring and analysis module for monitoring and analyzing abnormal situations are as follows: Receive transportation task allocation plans and real-time transportation status data; Extract transportation process features through deep learning algorithms and establish anomaly detection models; Based on the extracted features, the abnormal probability and type are output, and an early warning signal is generated according to the abnormal probability and type.

[0011] Preferably, the specific steps of the constraint evaluation and optimization module evaluating transportation resource constraints and optimizing task allocation are as follows: Obtain early warning signals and transportation task allocation plans from the abnormal monitoring and analysis module; Analyze resource supply cycles and equipment hours, and mark resource conflict tasks; Recalculate the task execution interval to adjust the task allocation and output the optimized constraint evaluation parameter set.

[0012] Preferably, the specific steps of the life cycle prediction module for predicting the aging trend of coal inventory equipment are as follows: Build equipment aging models based on real-time transportation status data and historical equipment data; The equipment operation data is divided into a training set and a test set. The training set is input into the equipment aging model to output the aging prediction value. Compare the aging prediction value with the historical actual aging value to calculate the error value. If the error value is higher than the preset threshold, adjust the model parameters; After the test set input verifies convergence, the device aging trend residual is output; Correct the remaining life of the equipment based on the aging trend residual and output the correction result.

[0013] Preferably, the specific steps of optimizing the maintenance strategy by the maintenance decision optimization module are as follows: Obtain equipment aging trend residuals and correction results from the life cycle prediction module; Obtain energy efficiency level assessment results from the energy efficiency assessment module; Construct a maintenance decision model based on equipment aging trend residuals and energy efficiency levels; The deep learning algorithm is used to simulate the evolution of the health status of different maintenance operations and output the optimized maintenance strategy.

[0014] Preferably, the specific steps of the real-time response adjustment module dynamically adjusting the transportation task are as follows: Obtain early warning signals from the abnormal monitoring and analysis module; obtain constraint evaluation parameter sets from the constraint evaluation and optimization module; Monitor environmental changes and equipment operating status, and calculate task time deviations; Count equipment working hours and remaining materials, adjust task allocation plans; output dynamically adjusted transportation task correction results.

[0015] Compared with the prior art, the present invention has the following beneficial effects: Through the multi-source data acquisition and processing module, various types of data such as coal quality, transportation status and market sales can be comprehensively collected, and effective pre-processing can be carried out to eliminate noise and redundancy in the data, making the data more standardized and reliable, and providing a solid data foundation for the subsequent operation of the system. The coal demand forecasting module leverages deep learning algorithms to comprehensively consider multiple influencing factors and accurately grasp market demand trends. By deeply mining and analyzing pre-processed data, it can capture underlying patterns and connections within the data, resulting in demand forecasts that are more realistic and help companies gain better insight into market changes. The Inventory Optimization and Scheduling module adjusts inventory levels based on accurate demand forecasts. This avoids the over- or under-stocking issues inherent in traditional inventory management, achieving a dynamic inventory balance and reducing storage costs and capital tie-up while ensuring timely fulfillment of market demand. The transportation task dynamic allocation module allocates transportation tasks flexibly based on the optimized inventory level, combined with real-time traffic conditions, transportation resources, and other information. It can select the optimal transportation route and transportation method according to the actual situation, improve the utilization rate of transportation resources, shorten the transportation time, and ensure the timely supply of coal. The abnormality monitoring and analysis module can monitor various abnormal situations in the transportation task execution process in real time, such as transportation delay, equipment failure, etc. Through rapid analysis and identification of abnormal data, problems can be found in time and warning can be issued, which facilitates relevant personnel to take measures to handle, reducing the impact of abnormal situations on the transportation process. The constraint evaluation and optimization module comprehensively evaluates the constraints of transportation resources, considering factors such as the number of transportation vehicles, load, and restrictions on transportation routes, making task allocation more reasonable. Through optimization of task allocation scheme, the efficiency of transportation resources can be fully utilized, avoiding resource waste and improving overall transportation efficiency.

[0016] The real-time response adjustment module can dynamically adjust the transportation task according to the abnormality monitoring and analysis results and the constraint evaluation results. When abnormal situations or resource constraints change, the system can quickly respond, re-plan the transportation route and allocate tasks to ensure smooth transportation process. The life cycle prediction module can predict the aging trend of coal inventory equipment, and through the analysis of equipment operation data, it can know in advance the possible faults and aging of the equipment, providing direction for equipment maintenance. The energy efficiency evaluation module evaluates the energy efficiency level of the coal transportation process, understands the energy consumption in the transportation process, and provides a reference for energy saving and consumption reduction. The maintenance decision optimization module formulates reasonable maintenance strategies based on the aging trend of the equipment and the energy efficiency evaluation results. It can arrange maintenance time and content according to the actual condition of the equipment, avoid blind maintenance, prolong the service life of the equipment, reduce maintenance cost, and ensure the normal operation of the equipment and the stability of transportation energy efficiency. BRIEF DESCRIPTION OF DRAWINGS

[0017] Figure 1 The timing diagram of the deep learning-based coal transportation and marketing management system described in the present invention; Figure 2 The flowchart for the coal demand prediction module; Figure 3 The flowchart for the transportation task dynamic allocation module; Figure 4 The flowchart for the abnormality monitoring and analysis module; Figure 5 The flowchart for the maintenance decision optimization module. DETAILED DESCRIPTION

[0018] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative effort belong to the scope of protection of the present application.

[0019] Please refer to Figure 1 The present application provides a coal transportation and marketing management system based on deep learning, which comprises: The intelligent management of the whole process of coal transportation and marketing is realized through multi-module cooperation. The system architecture includes a multi-source data acquisition and processing module, a coal demand prediction module, an inventory optimization and scheduling module, a transportation task dynamic allocation module, an abnormality monitoring and analysis module, a constraint evaluation and optimization module, a real-time response adjustment module, a life cycle prediction module, an energy efficiency evaluation module and a maintenance decision optimization module. Each module is connected through data flow and control flow to form a closed-loop intelligent decision system. During system operation, coal quality, transportation status and market sales data are first collected, preprocessed and input into the demand prediction model to generate market demand trend prediction results. Based on the prediction results, inventory optimization and transportation task allocation are performed, and abnormal conditions are monitored and resource constraints are evaluated in real time during execution to dynamically adjust the transportation plan. At the same time, the aging trend and energy efficiency level of the equipment are continuously tracked to optimize the maintenance strategy.

[0020] Example 1: Please refer to Figure 2 The system implementation of the multi-source data acquisition and processing module includes a distributed data acquisition architecture. The module deploys several data acquisition terminals, which are connected to laser-induced breakdown spectroscopy analyzers on the coal mine production line to obtain coal quality parameters, vehicle-mounted OBD devices on transport vehicles to collect real-time position and speed data, and market transaction database interfaces to obtain sales records. Different sources of data are transmitted to the data processing center through a dedicated message middleware, and the original data stream is temporarily stored using distributed caching technology. The data processing center includes a time synchronization engine, which establishes a unified time reference axis and generates timestamps based on data, with a fixed interval of 15 minutes. Within each time window, data from the calorific value detection unit, GPS positioning system and transaction platform are grouped and classified, and are aligned to the starting time node of the window through interpolation algorithm. The unit converter performs standardization processing on the synchronized data in the window: the calorific value of coal is uniformly converted to megajoules per kilogram, the transportation mileage is converted to kilometers according to the metric conversion standard, and the total daily sales volume is uniformly converted to metric tons.

[0021] An improved quantile analysis method is used in the abnormal data identification link. The data sequence that has been classified within each time window is independently sorted in ascending order, and the first quartile value and the third quartile value are determined by applying an accurate quartile calculation method. Based on these two values, the interquartile range value is further calculated, and then the valid data range is set to the interval consisting of the first quartile value minus 1.5 times the interquartile range to the third quartile value plus 1.5 times the interquartile range. Any record point in the data sequence that exceeds this interval is automatically marked as an abnormal state, and an abnormality report containing the original value and time location is generated. The data repair unit performs a two-step processing flow: first, all missing value positions are scanned, and a continuous function curve is constructed using the cubic spline interpolation algorithm for precise filling; second, the marked abnormal value is replaced by a dynamic sliding window mean method, and the average value of several valid data points before and after the data point is calculated to cover the abnormal value. After the repair, each set of data is transformed through the normalization processing layer. The maximum and minimum values ​​of each data dimension are used to calculate the linear mapping relationship. All values ​​are converted to a closed interval from zero to one. The conversion process retains the distribution characteristics of the original data.

[0022] The coal demand forecasting module implements a deep learning architecture that integrates temporal and spatial features. This module receives preprocessed data streams and restructures the data at the input interface layer. A three-dimensional data tensor representation is constructed, where the first dimension indexes consecutive time points, the second dimension corresponds to the spatial location of each mining area, and the third dimension contains feature channels such as coal quality parameters, transportation volume indicators, and market prices. The feature extraction layer consists of a two-channel parallel processing network. The temporal feature channel utilizes a bidirectional long-short-term memory network structure with 64 hidden units and a two-layer stack to process the temporal data evolution pattern of each mining area. The spatial feature channel utilizes a graph convolutional network architecture, establishing an adjacency matrix based on the geographical coordinates of the mining areas and constructing a mining area network topology map using two-hop node connections. Graph convolution operations model spatial dependencies within the topology. The feature fusion step concatenates the temporal and spatial feature vectors before inputting them into the fully connected network layer.

[0023] The output layer design generates market demand forecasts based on feature fusion vectors. The number of output units in the fully connected layer corresponds to the future time span, and a modified sinusoidal activation mechanism is used for the final output transformation, generating a probability distribution of daily market demand intensity over the next seven days. During the model training execution phase, an adaptive learning rate adjustment algorithm is configured, with the initial learning rate parameter set to 0.001 and a training sample size of 128 data sets per batch. The validation set monitoring mechanism operates independently during training, automatically triggering a training termination command when the validation set data loss function value does not show a downward trend for five consecutive training cycles. The forecasting system periodically receives the latest preprocessed data and updates the output forecast results in real time.

[0024] Strict quality control is implemented throughout the data processing process. After data cleaning is completed for each time window, a data integrity verification report is generated, documenting the number of missing values ​​filled and the percentage of outliers corrected. A reversible mapping table is established during the data conversion process to store the correspondence between raw and normalized values. An input data caching mechanism is implemented during the prediction model's operation, completely preserving snapshots of input data used for each prediction task and supporting retrospective analysis of historical prediction results. The system is deployed on a redundant server cluster, enabling real-time hot backup and automatic failover of the data processing process.

[0025] Example 2: See Figure 3 The inventory optimization and scheduling module adopts a two-layer recursive neural network architecture to realize the dynamic optimization function of inventory. This module receives the output data from the demand forecasting module and accesses the historical inventory database records at the same time. The first-layer network structure adopts a gated recurrent unit design to process a 60-day historical inventory level change sequence. The input of each time step contains the inventory quantity of the day, the in-and-out records, and the demand forecast deviation value. The number of hidden layer units in the network is set to 128, and the layer normalization technology is used to stabilize the training process. The second-layer optimization network adopts an attention mechanism structure, and the input is composed of the feature vector output by the first-layer network and the current market demand forecast result. The attention calculation layer contains 8 independent attention heads, each head calculates the association weight of the inventory of different mining areas. The output of the optimization network includes three key indicators: the recommended inventory quantity of each mining area, the safety stock coefficient, and the economic procurement batch parameter. The inventory optimization objective function is defined as: in: represents the actual inventory of the i-th mining area, is the optimized inventory output by the model, is the unit cost of the jth mode of transport, Corresponding transport capacity utilization, : The total number of mining areas (referring to the total number of independent coal mines or coal storage bases managed by the system), : The total number of available transportation modes (referring to the number of different coal transportation methods supported by the system). and The weights for inventory balance and transportation efficiency are controlled, with default settings of 0.7 and 0.3, respectively. Gradient clipping is used during training to limit parameter updates, with a maximum gradient norm set to 1.0. Model validation uses a rolling window cross-validation strategy with a fixed window length of 30 days and a sliding step size of 7 days to ensure evaluation coverage across different seasonal cycles.

[0026] The dynamic transport task allocation module implements an intelligent decision-making system based on Monte Carlo tree search. This module inputs inventory optimization results and real-time transport resource status data to construct a multidimensional state space description. The state vector contains 48 dimensional features, including inventory levels at each mining site, transport vehicle location coordinates, road network traffic conditions, and loading and unloading equipment availability. The action space is designed as a three-tiered decision-making structure: the top tier selects transport task priority strategies, the middle tier determines vehicle scheduling plans, and the bottom tier refines load capacity and route planning. The reward function is calculated using a multi-objective weighted approach, considering three dimensions: transport cost parameters, timeliness indicators, and customer satisfaction scores, and assigning fixed weights of 0.6, 0.3, and 0.1, respectively.

[0027] In the implementation of the tree search algorithm, the state node expansion adopts a progressive strategy. The initial root node corresponds to the current state of the transportation system, and each child node represents a possible transportation operation choice. The node selection strategy adopts an improved upper confidence interval algorithm, and the exploration coefficient is set to Each iteration performs a complete simulation: starting from the current node, traversing to the leaf nodes according to the selection strategy; expanding a new legal transport operation as a child node; calculating the expected reward of this operation path through random simulation; and backpropagating to update the number of visits and accumulated reward values ​​for each node along the path. The system is configured with a standard operation cycle of 1000 iterations. In the final output stage, a greedy strategy is used to select the node path with the highest accumulated reward value.

[0028] The task allocation plan generation process includes conflict detection and resolution mechanisms. The system maintains a data structure of transportation resource utilization timelines and automatically detects resource conflicts between newly assigned tasks and existing ones. A tiered conflict resolution strategy is implemented: high-priority tasks maintain their original time windows, medium-priority tasks are allowed to move within a ±2-hour window, and low-priority tasks can be reassigned transportation resources. The final output plan includes a detailed task allocation list, resource utilization schedule, and a set of alternative plans. The plan is expressed in a standardized JSON format, including complete fields such as task ID, transport vehicle number, load capacity, route coordinate sequence, and estimated start and end times.

[0029] The system implementation utilizes a microservices architecture. The inventory optimization service runs on GPU-accelerated compute nodes, with processing cycles set to automatically trigger every hour. The transportation allocation service is deployed as a distributed computing cluster, with each compute node handling transportation tasks for a specific geographic area. The data persistence layer uses a time-series database to store historical optimization records, and a relational database to manage transportation plan data. The front-end visualization interface displays both inventory level heat maps and transportation task Gantt charts, supporting manual intervention and adjustments. All service modules communicate asynchronously via message queues to ensure real-time system responsiveness.

[0030] The monitoring and maintenance subsystem continuously tracks the module's operational status. Performance metrics collected include optimization calculation time, solution generation time, and resource utilization. An exception handling mechanism monitors input data quality and automatically triggers data repair processes when abnormal data is detected. The version management service records model parameter update history and supports rapid rollback to stable versions. The system is equipped with an automated test suite to verify the correctness of core algorithm logic before each deployment. A logging system stores detailed intermediate calculation results from each decision cycle for post-analysis and auditing.

[0031] A continuous improvement mechanism is implemented for algorithm optimization. The online learning component collects data on deviations between actual execution results and predicted results, and regularly updates model parameters. A reinforcement learning environment simulator generates data for various transportation scenarios for offline training to enhance algorithm robustness. A multi-objective optimization strategy dynamically adjusts weight coefficients based on business needs, adapting to operational priorities at different times. A parallel computing framework accelerates the solution process for large-scale transportation networks, employing task sharding and result aggregation strategies. A cache mechanism stores solutions for common transportation scenarios, improving system responsiveness.

[0032] Example 3: See Figure 4 The anomaly monitoring and analysis module implements a real-time anomaly detection system based on deep learning. This module accesses the task allocation plan output by the dynamic allocation module of transportation tasks and synchronously receives the real-time transportation status data stream from the IoT terminal. The data preprocessing unit first performs a data fusion operation to align the expected transportation route, time nodes, and loading indicators in the task plan with the actual GPS positioning coordinates, on-board weighing sensor data, timestamp information, etc. The aligned data stream generates data snapshots at intervals of 5 seconds. Each snapshot contains a 36-dimensional feature vector, covering monitoring indicators such as vehicle position offset, speed fluctuation value, load change rate, and fuel consumption anomaly.

[0033] The anomaly detection model uses a deep convolutional autoencoder architecture. The encoder consists of a five-layer fully connected neural network, with 36, 24, 16, 10, and 8 neurons in each layer, respectively. Each layer uses a linear unit activation function with leakage correction. The bottleneck layer compresses features to one-quarter of their original dimensions, while the decoder reconstructs the input data using a mirror-symmetric structure. The feature extraction process implements hierarchical dimensionality reduction, and the output of each network layer undergoes batch normalization. The reconstruction error is calculated using a weighted Euclidean distance metric, giving greater weight to time-sensitive features.

[0034] The dynamic threshold adjustment mechanism implements the anomaly determination algorithm. The basic formula for threshold calculation is defined as: The characters in the formula have the following meanings: Indicates the total number of normal state samples used in model training (this parameter is determined based on the amount of historical data during the system initialization phase and remains unchanged during subsequent operations). represents the original feature vector of the i-th training sample, Represents the model's reconstructed output vector for the sample, Refers to the number of feature space dimensions (the value is a constant 36), Corresponding to the monitoring value of the j-th dimension feature in the real-time data stream, represents the mean value of the feature in the training set, is the feature stability adjustment factor (the default setting is 0.05).

[0035] The model training phase performs unsupervised learning based on historical normal transportation data. The learning objective is to minimize both reconstruction error and feature distribution variance. During real-time monitoring, each data snapshot is input into the model and the reconstruction result is output. An abnormal event is flagged when the calculated result exceeds 1.8 times the current threshold. The threshold itself implements a dynamic update strategy, recalculating the baseline value every 24 hours based on the latest 6,000 normal data samples.

[0036] The early warning generation subsystem implements a three-level classification mechanism. The first level analyzes the distribution patterns of abnormal features and identifies clusters of feature combinations related to equipment failures; the second level detects sequence patterns of features related to transportation delays; and the third level screens for groups of quality deviation features. Each classification level is equipped with an independent support vector machine classifier, trained on a specific type of abnormal data set. The classifier outputs a probability distribution of abnormality types and generates a formal early warning signal when the probability of the main category exceeds 0.75. The early warning message uses a standardized coding format and includes the abnormality number, occurrence time, geographic location coordinates, severity level, impact range, and recommended initial response measures.

[0037] The Constraint Assessment and Optimization module implements closed-loop optimization of resource constraints. This module receives warning signals from the Anomaly Monitoring module and simultaneously obtains a comprehensive overview of the current transport task allocation plan. The Resource Modeling Unit maintains a dynamic resource pool database, recording 83 constraints, including vehicle maintenance cycles, driver work hour limits, and the operating status of loading and unloading equipment. The constraint conflict detection algorithm utilizes a multidimensional spatiotemporal scanning method to construct a bidirectional mapping table between transport tasks and resource occupancy. The core logic of conflict identification is to detect the application requirements of multiple tasks for the same key resource within the same time period. Setting conflict sensitivity parameters controls the granularity of system detection.

[0038] Resource conflict resolution implements a hierarchical response mechanism. The system categorizes transport tasks into three priority levels: high-priority tasks guarantee unadjustable delivery deadlines; medium-priority tasks are allowed to fluctuate within a two-hour window before or after the original schedule; and low-priority tasks are subject to reallocation of transport resources. The optimization algorithm implements a heuristic search process, establishing a task adjustment decision tree structure, with each node representing a local adjustment plan for the task sequence. The search strategy prioritizes maintaining high-priority tasks unchanged and selecting the branch path that minimizes the adjustment cost. The evaluation function calculates the combined impact of each adjustment on the total transport cost and timeliness, ultimately selecting the solution that satisfies all constraints and achieves the optimal evaluation value.

[0039] The optimization output module generates a structured parameter set. Core parameters include the adjusted task start time matrix, vehicle-task reallocation mapping table, and resource usage status diagram. Data is expressed using a dual-track mechanism using a binary protocol and JSON format to meet the interface requirements of different system components. A time series database records the decision parameters of each optimization adjustment, forming a historical log of the evolution of constraints.

[0040] The system deployment architecture utilizes an edge-cloud collaborative model. A lightweight version of the anomaly detection model is deployed on edge computing nodes to perform preliminary anomaly screening. Full data analysis is performed on the cloud computing platform, enabling cross-validation of multi-dimensional data. Real-time data stream processing utilizes a pipeline architecture, with parallel processing channels configured to manage data spikes. A historical data warehouse maintains a complete 90-day record of the transportation process, enabling retrospective analysis of anomaly patterns.

[0041] The operation and maintenance mechanism includes a multi-dimensional monitoring system. The system health dashboard displays 12 operational indicators in real time, including data processing latency, model computation load ratio, and the number of accumulated warning messages. The anomaly detection model implements a quarterly update strategy, performing incremental training based on newly added sample data. The constraint rule library supports online editing, allowing operators to dynamically adjust resource constraint boundary conditions. All configuration changes are versioned, fully recording the operation time, modification content, and responsible individual.

[0042] The system is equipped with security protection mechanisms. End-to-end encryption is used during data transmission to prevent the leakage of transport status information. User access is controlled through a role-based permission control model, with three levels of permissions: data query, parameter configuration, and system management. Operation logs fully record all warning generation events and constraint adjustment operations, forming an auditable operation chain. The disaster recovery system fully backs up critical data daily, retaining the last 30 days of backup copies to ensure the continuity and stability of system operations.

[0043] Example 4: See Figure 5The life cycle prediction module builds an equipment health assessment system based on multi-source data fusion. This module accesses the real-time operation data stream and historical maintenance record database of the transportation equipment, and the data collection frequency is set to once per minute. The equipment operation data contains 28 monitoring dimensions, covering mechanical parameters such as engine speed, hydraulic system pressure, and drive shaft temperature, as well as operating condition indicators such as ambient temperature, humidity, and dust concentration. The data preprocessing unit performs feature engineering operations, performs sliding window statistical calculations on the original signal, and generates an enhanced data set containing statistical features such as mean, variance, and peak factor. The maintenance record data is structured to extract key information such as maintenance type, replacement parts, and labor time consumption, and establish a time alignment relationship with the operation data.

[0044] The equipment aging model uses a deep residual network architecture, with the network input layer designed as a hybrid structure that adapts to multimodal data. Continuous numerical monitoring features are processed by a fully connected layer, and discrete features such as maintenance events are converted into dense vectors by an embedding layer. The main network consists of a sequence of 15 residual blocks, each of which consists of two 256-dimensional fully connected layers, activated by batch normalization and linear units with leakage correction. The skip connection structure allows the original feature information to be passed directly to the deep network. The output layer generates a monthly decay rate forecast for the equipment health index, mapped to a value between 0 and 1, with 1 indicating a completely healthy state and 0 indicating complete failure.

[0045] The model training process adopts a phased strategy. In the first phase, network parameters are pre-trained using historical data. Data is grouped by device serial number to ensure that data from the same device appears only in the training or test set. In the second phase, online learning is implemented, incrementally updating model parameters using the latest collected device operating data. The training loss function comprehensively considers the requirements of prediction accuracy and trend smoothness, using a dynamic weighting method to balance short-term fluctuations and long-term decay patterns. Model validation uses a holdout method, retaining the last three months of device status data as an independent test set.

[0046] The remaining useful life prediction unit integrates a Weibull distribution model. This unit receives the health index sequence output by the aging model and fits the device performance degradation curve. The prediction process includes uncertainty quantification, generating a probability distribution function for the remaining useful life. The system regularly calibrates the prediction results. When the deviation between the actual detected device status and the predicted value exceeds a set threshold, the model parameter adjustment process is triggered. The calibration mechanism uses a sliding window method, recalculating the shape and scale parameters of the Weibull distribution using only the most recent six months of observational data.

[0047] The maintenance decision optimization module implements a reinforcement learning-based policy generation system. The state space is defined as a joint feature representation of equipment health, maintenance resource inventory, and production plan requirements. The action space encompasses seven standard maintenance operations: routine inspection, lubrication maintenance, component replacement, system calibration, deep cleaning, specialized overhaul, and shutdown overhaul. The decision network utilizes a dual deep Q-network architecture, with the primary network generating action value estimates and the target network providing a stable learning objective. The experience replay buffer stores the most recent 10,000 state-action-reward transition samples, and the sampling strategy prioritizes experience segments with high learning value.

[0048] The system runtime maintenance strategy optimization process consists of four phases. The first phase collects current equipment status data and feeds it into an aging model to obtain a health status assessment. The second phase simulates the potential impact of different maintenance operations on equipment status and generates a prediction of the state evolution trajectory. The third phase evaluates the comprehensive cost of each maintenance plan, taking into account factors such as direct maintenance costs, downtime losses, and production delay costs. The fourth phase selects the Pareto-optimal solution and outputs an analysis report that includes specific maintenance measures, execution time windows, and expected results.

[0049] The energy efficiency assessment module monitors energy consumption throughout the entire transport process. Connecting to the transport equipment's fuel flow meter, electricity meter, and air pressure sensor, it creates a comprehensive view of energy consumption. Evaluation metrics include 12 core parameters, including ton-kilometer energy consumption, equipment idle rate, and energy conversion efficiency. The data collection system implements timestamp synchronization to ensure accurate correlation between energy consumption data and transport tasks. Evaluation results are transmitted in a standardized format to the maintenance decision module, serving as key input for strategy optimization.

[0050] The system implementation utilizes a distributed deployment architecture. The aging prediction model runs on GPU-accelerated computing nodes, processing data streams from equipment across mining sites. The maintenance decision service is deployed as a multi-instance cluster, with each instance responsible for generating policies for a specific type of equipment. The data storage layer utilizes a time-series database to store equipment monitoring data, a graph database to manage equipment topology, and a relational database to record maintenance history. The front-end interface provides a visual dashboard of equipment health status, supporting 3D equipment model display and interactive prediction queries. See Table 1.

[0051] Table 1: Equipment monitoring characteristics table.

[0052] Feature Number Feature Name Collection method Data range unit F01 Engine speed CAN bus 800-2200 rpm F02 Hydraulic system pressure pressure sensor 0-35 MPa F03 Drive shaft temperature Infrared temperature measurement 20-120 ℃ F04 Brake pad wear Laser ranging 0-15 mm F05 Instantaneous fuel flow flow meter 0-50 L / h F06 Vibration acceleration accelerometer 0-10 <![CDATA[m / s 2 ]]> The operation and maintenance monitoring system implements a full-link tracking mechanism. The data collection process records the device serial number, collection time, and data quality mark for each monitoring point. The model inference process log contains detailed information such as a snapshot of input features, prediction results, and computation time. The maintenance decision audit trail records the time when the policy was generated, the factors considered, and the final basis for the selection. The system sets automatic alarm rules and generates diagnostic reports when data anomalies, prediction deviations, or decision conflicts are detected. The version control system manages iterative updates of model parameters and decision rules, supporting rapid rollback to historical stable versions.

[0053] The system's security mechanism incorporates multi-layered protection measures. Data transmission is encrypted using a national secret algorithm, and stored data implements field-level access control. The user rights management system divides operational permissions based on roles and responsibilities, implementing the principle of least privilege. Operational logs fully record all critical operations, including sensitive activities such as data modifications, model updates, and decision overrides. The system deploys an intrusion detection module to monitor abnormal access patterns and data leakage risks in real time. The disaster recovery system implements a remote active-active architecture to ensure service continuity in extreme situations.

[0054] Example 5: The real-time response adjustment module builds a dynamic task scheduling system to achieve closed-loop control of the transportation process. This module continuously receives warning signals from the abnormal monitoring and analysis module and the parameter set of the constraint evaluation and optimization module, while monitoring external environmental changes and equipment operating status. The data fusion center integrates multi-source information streams, including real-time meteorological data, road control notifications, vehicle OBD diagnostic information, and loading and unloading platform operation status. The data update frequency is set to once every 10 seconds to ensure the timeliness of system perception. The environmental change monitoring unit analyzes the impact of meteorological factors such as wind speed, precipitation, and temperature on the transportation route, and the equipment status analysis unit calculates the deviation of key indicators such as engine load rate and tire wear.

[0055] The mission time deviation calculation engine implements a multi-dimensional assessment. For each active transport mission, the system compares the planned timeline with the actual progress and quantifies the time deviation. This deviation analysis considers the segmented nature of routes, dividing the entire transport route into logical segments and independently assessing delays in each segment. Weighting factors are incorporated into the calculation process to assign greater importance to critical path nodes. The resource availability analysis unit compiles real-time information such as the number of available vehicles, remaining driver work hours, and the standby status of loading and unloading equipment to generate a resource supply capacity summary.

[0056] The dynamic adjustment algorithm adopts a hierarchical decision-making mechanism. The first layer handles slight deviation cases, implementing local fine-tuning strategies, including vehicle speed optimization, route order adjustment, and other non-structural changes. The second layer deals with moderate deviations, performing task reassignment operations, replacing transport vehicles or adjusting execution teams while maintaining the original task objectives. The third layer solves severe deviation scenarios, triggering a global task reconstruction process to recalculate the optimal transportation solution. The decision-making process introduces a fuzzy logic controller to handle reasoning problems under conditions of uncertainty and incomplete information.

[0057] The task allocation scheme update implements an incremental adjustment method. The system maintains a complete mirror of the current task execution state, and any adjustment operation is first simulated in a virtual environment. The simulator predicts the evolution trajectory of the adjusted task and evaluates its impact on the overall transportation network. The scheme generator outputs a differentiated set of adjustment recommendations, including emergency changes that must be implemented and optional optimization measures that can be implemented. Each adjustment preserves a complete version history, supporting quick rollback to the previous stable state. Adjustment instructions are issued to each execution terminal through a standardized message format, including explicit execution time windows and operation step instructions.

[0058] The system implements an event-driven architecture at the system implementation level. Core components include event listeners, state evaluators, decision engines, and instruction distributors. Event listeners filter and categorize incoming data streams, identifying important events that require system response. State evaluators maintain real-time models of the transportation network, calculating state indicators for each node. Decision engines implement multi-threaded processing, evaluating different levels of adjustment schemes in parallel. Instruction distributors are responsible for converting final decisions into executable commands and transmitting them to vehicle-mounted terminals and site control systems through a dedicated communication protocol.

[0059] The exception handling mechanism implements a hierarchical response strategy. First-level exceptions are handled autonomously by local terminals, such as temporary vehicle detours for simple scenarios. Second-level exceptions trigger regional dispatch center intervention, coordinating multi-vehicle collaborative adjustments. Third-level exceptions are reported to the system core decision layer, starting the global optimization process. Each exception type is associated with a pre-set response template to speed up the decision-making process. The system records complete exception handling logs, including exception occurrence time, impact range, response measures, and actual effects.

[0060] The communication guarantee system establishes a reliable message transmission channel. Vehicle-mounted terminals maintain persistent connections with the central system, using a heartbeat mechanism to detect communication status. Data transmission implements both compression and encryption, optimizing bandwidth utilization and ensuring information security. The message confirmation mechanism ensures that each instruction is correctly received and executed by the terminal. Offline processing capabilities allow vehicle-mounted terminals to continue executing pre-set tasks when the network is interrupted, and synchronize state updates after connection is restored.

[0061] The version management service manages the dynamic evolution of the system. Each algorithm adjustment or parameter update generates a new version identifier, which is associated with the adjustment decision. The configuration management center records the modification history of all operating parameters and supports parameter-level rollbacks. The performance monitoring dashboard displays key metrics such as system response latency, decision accuracy, and resource utilization in real time. The capacity planning unit predicts system load trends and automatically allocates computing resources to meet peak demand.

[0062] The system maintenance mechanism implements a preventive maintenance strategy. A log analyzer continuously monitors system health and identifies potential problem patterns. A resource defragmenter regularly optimizes memory and storage usage. Database maintenance tasks perform index rebuilds and statistics updates during low-load periods. The backup system implements a differential backup strategy, with daily full backups of core data and hourly incremental backups of operation logs. Disaster recovery drills are conducted quarterly to verify the system's resilience in extreme situations.

[0063] The user interface design emphasizes contextual awareness. The dispatcher console dynamically adjusts information presentation based on the current system status, automatically highlighting critical decision-making factors in critical situations. Visualization tools support multiple perspectives of the transportation network, including geographic, timeline, and resource topology views. The operation guidance system provides context-sensitive function suggestions based on user role and task type. An audit trail interface fully records all manual interventions, including detailed information such as the time of operation, the person performing the operation, and the content of the modification.

[0064] System integration utilizes standardized interface specifications. Data exchange with external weather services adheres to industry-standard formats, and communication protocols with onboard terminals are compatible with interface definitions from major equipment manufacturers. Data transfer between internal modules utilizes a unified encoding scheme to ensure lossless information transmission. The adapter layer handles the special interface requirements of legacy systems, enabling a smooth transition between legacy and new systems. The message-based middleware implements flow control and priority management to ensure the quality of critical data transmission.

[0065] Our testing and verification system covers all functional scenarios. Unit testing verifies the correctness of each algorithm component, while integration testing checks the interaction logic between modules. Load testing simulates system performance in high-concurrency scenarios, and endurance testing verifies stability during long-term operation. Fault injection testing assesses the system's fault tolerance under abnormal conditions. Regression testing ensures that system updates do not introduce new issues. Our test case library is continuously expanding to cover various edge cases encountered in actual operations.

[0066] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0067] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A coal transportation and marketing management system based on deep learning, characterized in that: include: Multi-source data acquisition and processing module, used to collect coal quality data, transportation status data and market sales data, and perform data preprocessing; Coal demand forecasting module, which predicts coal market demand trends based on preprocessed data; Inventory optimization and scheduling module, which optimizes coal inventory levels based on coal market demand trends; Dynamic allocation module for transportation tasks, allocating transportation tasks based on optimized coal inventory levels; Abnormal monitoring and analysis module, monitoring and analyzing abnormal situations during the execution of transportation tasks; Constraint evaluation and optimization module, which evaluates transportation resource constraints and optimizes task allocation; Real-time response adjustment module, dynamically adjusting transportation tasks based on abnormal situations and constraint assessment results; Life cycle prediction module, used to predict the aging trend of coal inventory equipment; Energy efficiency assessment module, used to evaluate the energy efficiency level of the coal transportation process; Maintenance decision optimization module, which optimizes maintenance strategies based on aging trends and energy efficiency levels; The specific steps of data preprocessing by the multi-source data acquisition and processing module are as follows: Receive coal quality data, transportation status data, and market sales data collected by different sensors, group the same type of data, synchronize the sampling time step through the time window, and then perform unit standardization; Sort each group of data and calculate the first and third quartiles, determine the upper and lower boundaries of the data based on the interquartile range, and mark outliers that exceed the boundaries; Count the missing values ​​in each group of data and fill them through linear interpolation, then replace the outliers by mean filling, re-verify the data quality and normalize the data to a specific range; The specific steps of the coal demand forecasting module for forecasting coal market demand trends are as follows: Building a deep learning model based on the preprocessed data, which includes an input layer, a feature extraction layer, and an output layer; The input layer receives the preprocessed data sets and historical demand data; The feature extraction layer extracts the temporal and spatial features of the data through deep learning algorithms; The output layer outputs the coal market demand trend forecast results based on the extracted features.

2. The coal transportation and marketing management system based on deep learning according to claim 1 is characterized in that: The specific steps of optimizing coal inventory levels by the inventory optimization scheduling module are as follows: Build an inventory optimization model based on the coal market demand trend forecast results; The historical inventory data is divided into a training set and a test set. The training set is input into the inventory optimization model and the inventory optimization value is output through forward propagation. Compare the optimized inventory value with the historical actual inventory value to calculate the model error value. If the error value is higher than the preset threshold, adjust the model parameters through back propagation; After training, the model is trained using the test set to verify the convergence of the error value and output the optimized coal inventory level.

3. The coal transportation and marketing management system based on deep learning according to claim 2 is characterized in that: The specific steps of allocating transport tasks by the transport task dynamic allocation module are as follows: Establish a task allocation model based on the optimized coal inventory level; Analyze the availability of transportation resources to generate an action space and set a reward function based on resource costs and benefits; Generate a child node tree with the current transportation status as the root node, and select nodes layer by layer through the confidence bound selection strategy; Expand the simulation of task allocation evolution by unused transport operations, calculate the strategy benefits, and then backtrack to update the node's cumulative rewards and visit counts; After iterative optimization, the node path with the highest cumulative reward is selected to build a complete transportation task allocation plan.

4. The coal transportation and marketing management system based on deep learning according to claim 3 is characterized in that: The specific steps of the abnormal monitoring and analysis module for monitoring and analyzing abnormal situations are as follows: Receive transportation task allocation plans and real-time transportation status data; Extract transportation process features through deep learning algorithms and establish anomaly detection models; Based on the extracted features, the abnormal probability and type are output, and an early warning signal is generated according to the abnormal probability and type.

5. The coal transportation and marketing management system based on deep learning according to claim 4 is characterized in that: The specific steps of the constraint evaluation and optimization module to evaluate transportation resource constraints and optimize task allocation are as follows: Obtain early warning signals and transportation task allocation plans from the abnormal monitoring and analysis module; Analyze resource supply cycles and equipment hours, and mark resource conflict tasks; Recalculate the task execution interval to adjust the task allocation and output the optimized constraint evaluation parameter set.

6. The coal transportation and marketing management system based on deep learning according to claim 5 is characterized in that: The specific steps of the life cycle prediction module for predicting the aging trend of coal inventory equipment are as follows: Build equipment aging models based on real-time transportation status data and historical equipment data; The equipment operation data is divided into a training set and a test set. The training set is input into the equipment aging model to output the aging prediction value. Compare the aging prediction value with the historical actual aging value to calculate the error value. If the error value is higher than the preset threshold, adjust the model parameters; After the test set input verifies convergence, the device aging trend residual is output; Correct the remaining life of the equipment based on the aging trend residual and output the correction result.

7. The coal transportation and marketing management system based on deep learning according to claim 6 is characterized in that: The specific steps of optimizing the maintenance strategy by the maintenance decision optimization module are as follows: Obtain equipment aging trend residuals and correction results from the life cycle prediction module; Obtain energy efficiency level assessment results from the energy efficiency assessment module; Construct a maintenance decision model based on equipment aging trend residuals and energy efficiency levels; The deep learning algorithm is used to simulate the evolution of the health status of different maintenance operations and output the optimized maintenance strategy.

8. The coal transportation and marketing management system based on deep learning according to claim 7 is characterized in that: The specific steps of the real-time response adjustment module dynamically adjusting the transportation task are as follows: Obtain early warning signals from the abnormal monitoring and analysis module; obtain constraint evaluation parameter sets from the constraint evaluation and optimization module; Monitor environmental changes and equipment operating status, and calculate task time deviations; Count equipment working hours and material remaining, and adjust task allocation plans; Output the dynamically adjusted transportation task correction results.

Images