Intelligent control method and system for coal transportation production plan

Abstract

The invention is applicable to the technical field of coal transportation, and particularly relates to an intelligent control method and system for a coal transportation production plan, a data acquisition module is used for acquiring coal inventory data and transportation data, and a data processing and analysis module is used for performing data preprocessing on the coal inventory data and the transportation data; and the real-time monitoring and adjusting module is used for carrying out statistical analysis and prediction analysis on the coal demand information to obtain a coal demand prediction result and generating a transportation scheduling scheme according to the coal demand prediction result, and the real-time monitoring and adjusting module is used for tracking the transportation tool and dynamically adjusting the transportation plan. According to the method, the speed and accuracy of scheduling decision making are remarkably improved, and errors caused by human factors are reduced; through real-time data analysis and machine learning, highly automatic and intelligent transportation scheduling is realized; resource use is optimized, the transportation cost is reduced, and the overall supply chain efficiency is improved; the coping capacity for uncertain factors is enhanced, and the reliability and customer satisfaction of coal transportation are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of coal transportation, and in particular relates to an intelligent control method and system for coal transportation production planning. Background Art

[0002] As global energy demand continues to increase, coal, as an important energy source, has a stable supply that is crucial to economic and social development. Effective coal transportation is a key factor in ensuring efficient operation of power generation and industrial production. Traditional coal transportation scheduling relies on manual operations and simple algorithms, and cannot flexibly respond to complex situations such as market changes, traffic conditions, and weather factors, resulting in suboptimal utilization of transportation resources.

[0003] The production planning method used in the existing technology cannot respond to various emergencies in the transportation process in real time, lacks the ability to process and analyze large amounts of data, and cannot fully utilize historical data and real-time data for decision support. Summary of the invention

[0004] The purpose of the present invention is to provide an intelligent control method for coal transportation production planning, aiming to solve the problems that the production planning method used in the prior art cannot respond to various emergencies in the transportation process in real time, lacks the ability to process and analyze large amounts of data, and cannot make full use of historical data and real-time data for decision support.

[0005] The present invention is implemented as follows: an intelligent control system for coal transportation production planning, the system includes a data acquisition module, a data processing and analysis module, an intelligent decision-making module and a real-time monitoring and adjustment module, the data acquisition module is used to collect coal inventory data and transportation data, the data processing and analysis module is used to pre-process the coal inventory data and transportation data, and perform statistical analysis and predictive analysis on them to obtain coal demand forecast results, the intelligent decision-making module generates a transportation scheduling plan based on the coal demand forecast results, and the real-time monitoring and adjustment module is used to track transportation tools and dynamically adjust the transportation plan.

[0006] Preferably, the coal inventory data includes at least coal supply data, real-time inventory data and quantitative warehouse real-time inventory data, and the transportation data includes at least real-time loading progress data, meteorological data, forklift coal supply, belt conveyor supply, coal feeder coal supply and buffer warehouse water level.

[0007] Another object of the present invention is to provide an intelligent control method for coal transportation production planning, which is applied to the intelligent control system of coal transportation production planning as described above, and the method comprises:

[0008] Data collection is performed through the data collection module to obtain coal inventory data and transportation data;

[0009] The coal inventory data and transportation data are cleaned through the data processing and analysis module, and statistical analysis and pattern recognition are performed on the cleaned data to identify the central trend and outliers of the cleaned data. The data is analyzed through the neural network model to generate coal demand forecast results;

[0010] The coal demand forecast results are processed through the intelligent decision-making module to generate the optimal transportation scheduling plan;

[0011] The transportation tools are tracked through the real-time monitoring and adjustment module, and the transportation plan is adjusted according to the changes in the external environment.

[0012] Preferably, the step of performing data cleaning on the coal inventory data and transportation data includes performing noise removal, error correction and redundancy elimination processing on the coal inventory data and transportation data, and merging data from different sources to obtain a consistent data set.

[0013] Preferably, the step of performing statistical analysis and pattern recognition on the cleaned data includes performing descriptive analysis by statistical analysis methods to identify the central tendency and outliers of the cleaned data, wherein the central tendency is characterized by the mean, median or mode.

[0014] Preferably, in the step of performing data analysis through a neural network model, the neural network model includes an input layer and a hidden layer, the input layer is used to input historical coal demand and supply data, the number of hidden layers is multiple, and each hidden layer contains a preset number of neurons.

[0015] Preferably, in the step of processing the coal demand forecast results through the intelligent decision-making module to generate the optimal transportation scheduling plan, the intelligent decision-making module includes using a machine learning algorithm to process the coal demand forecast results, the machine learning algorithm is a deep learning algorithm or a reinforcement learning algorithm, and the machine learning algorithm is trained by data cleansing. During the training process, the parameters of the model are continuously adjusted so that the model can accurately predict the transportation results and optimize the scheduling plan, a comprehensive reward function is constructed according to the goals to be achieved, and the trained machine learning algorithm is evaluated by constructing a test data set, and the machine learning algorithm is optimized and adjusted according to the evaluation results.

[0016] Preferably, when the machine learning algorithm is a deep learning algorithm, a long short-term memory network in RNN is used to predict the transportation time and cost. The long short-term memory network is expressed as:

[0017] i t =σ(W xi x t +W hih t-1 +b i )

[0018] f t =σ(W xf x t +W hf h t-1 +b f )

[0019] o t =σ(W xo x t +W ho h t-1 +b o )

[0020]

[0021] h t =o t *tanh(C t )

[0022] Among them, x t is the input, h t-1 is the hidden state at the previous moment, i t 、f t and t They are input gate, forget gate and output gate respectively, C t is the cell state, W is the weight, and b is the bias.

[0023] Preferably, when the machine learning algorithm is a reinforcement learning algorithm, a deep Q network is used for decision optimization. The deep Q network selects the optimal action by estimating the Q values ​​of different actions. The Q value update formula in the deep Q network is:

[0024] Q(s,a)←Q(s,a)+α[r+γmax a′ Q(s′,a′;θ - )-Q(s,a;θ)]

[0025] Among them, Q(s,a;θ) is the state-action value function under the current network parameter θ; Q(s′,a′;θ - ) is the target network parameter θ - The state-action value function is obtained; α is the learning rate, which is used to control the degree of each update, γ is the discount factor, and the value range of γ is [0,1], max a′ Q(s′,a′;θ - ) represents the maximum value of the Q value of selecting the best action a′ according to the target network in the next state s′.

[0026] Preferably, the method further comprises constructing a user interaction interface, wherein the user interaction interface displays the transportation plan, real-time data and historical data analysis results through charts and dashboards.

[0027] The invention provides an intelligent control system for coal transportation production planning, which has the following beneficial effects:

[0028] Significantly improve the speed and accuracy of scheduling decisions and reduce errors caused by human factors;

[0029] Realize highly automated and intelligent transportation scheduling through real-time data analysis and machine learning;

[0030] Optimize resource utilization, reduce transportation costs, and improve overall supply chain efficiency;

[0031] Enhance the ability to cope with uncertainties and improve the reliability of coal transportation and customer satisfaction. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 An interface diagram for formulating a production plan provided by an embodiment of the present invention;

[0033] Figure 2 An interface diagram of the daily plan details provided by an embodiment of the present invention;

[0034] Figure 3 An interface diagram of the details of the plan star provided by an embodiment of the present invention;

[0035] Figure 4 An interface diagram summarizing the execution status of a plan provided in an embodiment of the present invention;

[0036] Figure 5 A first interface diagram of a plan execution statistical report provided in an embodiment of the present invention;

[0037] Figure 6 A second interface diagram of a plan execution statistical report provided in an embodiment of the present invention;

[0038] Figure 7 An interface diagram of real-time loading progress provided by an embodiment of the present invention;

[0039] Figure 8 An interface diagram for collecting loading data provided by an embodiment of the present invention;

[0040] Fig. 9 An interface diagram of loading efficiency provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0041] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0042] It is understood that the terms "first", "second", etc. used in this application may be used herein to describe various elements, but unless otherwise specified, these elements are not limited by these terms. These terms are only used to distinguish a first element from another element. For example, without departing from the scope of this application, a first xx script may be referred to as a second xx script, and similarly, a second xx script may be referred to as a first xx script.

[0043] like Figure 1 , Figure 2 , Figure 3 , Figure 4 , Figure 5 , Figure 6 , Figure 7 , Figure 8 and Fig. 9 As shown, an intelligent control system for coal transportation production plan is provided, the system includes a data acquisition module, a data processing and analysis module, an intelligent decision-making module and a real-time monitoring and adjustment module, the data acquisition module is used to collect coal inventory data and transportation data, the data processing and analysis module is used to pre-process the coal inventory data and transportation data, and perform statistical analysis and predictive analysis on them to obtain coal demand forecast results, the intelligent decision-making module generates a transportation scheduling plan according to the coal demand forecast results, and the real-time monitoring and adjustment module is used to track the transportation tools and dynamically adjust the transportation plan.

[0044] In an embodiment of the present invention, the data acquisition module is used to implement data acquisition: by installing sensors, data acquisition programs and other strategies, real-time data such as shovels, inventory, coal feeders, belts, buffer bins, quantitative bins and plans are collected.

[0045] The data processing and analysis module builds a data warehouse and uses big data processing frameworks such as Hadoop and Spark to store and analyze data. It applies statistical analysis methods to evaluate historical transportation efficiency and, based on this, predicts future demand and optimizes routes.

[0046] The intelligent decision-making module builds a prediction model based on deep learning, inputs current and historical data, and outputs the optimal scheduling solution. The model is updated regularly to adapt to the changing external environment and internal strategies.

[0047] The real-time monitoring and adjustment module establishes a central monitoring system to monitor the status of all transportation tools through real-time data flow. When deviations occur, the system automatically proposes adjustment suggestions or directly implements adjustments.

[0048] In the embodiment of the present invention, a friendly web interface is constructed so that operators can easily access system functions, view real-time data, manually adjust plans, and analyze historical trends and current performance through visualization tools.

[0049] The present invention also provides an intelligent control method for coal transportation production plan, which is applied to the intelligent control system of coal transportation production plan as described above, and the method comprises:

[0050] Data collection is performed through the data collection module to obtain coal inventory data and transportation data.

[0051] In this step, the data acquisition module is used to collect the following data:

[0052] Coal supply data: including key information such as coal type, quality, and source. This data is essential to ensure supply chain stability and cost control.

[0053] Real-time inventory data: Monitor coal storage levels to replenish inventory in a timely manner and avoid stockouts or surpluses.

[0054] Real-time loading progress data: Track the loading process of coal from warehouse to transport vehicles to ensure loading efficiency and accuracy.

[0055] Ride scheduling data: Forecast and manage transportation demand, optimize vehicle scheduling and route planning.

[0056] Meteorological data: Analyze the impact of weather conditions on coal transportation and storage, such as temperature, humidity, wind speed and other factors.

[0057] Shovel coal feeding quantity: Monitor the coal delivery quantity of the shovel in each operation to ensure the accuracy and efficiency of loading.

[0058] Belt throughput: Record the flow of coal through the conveyor belt system to assess conveying efficiency and possible bottlenecks.

[0059] Coal feeder coal quantity: Control the output of the coal feeder to ensure the stability and efficiency of the combustion process.

[0060] Buffer tank water level: Monitor the water level in the buffer tank to maintain the proper level and prevent overflow or dryness.

[0061] Real-time inventory in quantitative warehouse: Real-time monitoring of coal inventory in quantitative warehouse to accurately control loading quantity.

[0062] Among them, coal inventory data at least includes coal supply data, real-time inventory data and real-time inventory data of quantitative warehouses, and transportation data at least includes real-time loading progress data, meteorological data, forklift coal supply, belt conveyor supply, coal feeder coal supply and buffer warehouse water level.

[0063] The coal inventory data and transportation data are cleaned through the data processing and analysis module, and statistical analysis and pattern recognition are performed on the cleaned data to identify the centralized trends and outliers in the cleaned data. The data is analyzed through the neural network model to generate coal demand forecast results.

[0064] In this step, perform the following steps:

[0065] Data cleaning and integration: Use big data processing technology to pre-process the collected data, including removing noise, correcting errors and eliminating redundancy. And use data integration technology to merge data from different sources to form a consistent data set, laying the foundation for subsequent analysis.

[0066] Statistical analysis and pattern recognition provide the basis for transport routes and timetables.

[0067] In the process of statistical analysis, descriptive analysis is a method used to summarize and describe the characteristics of a data set. In order to identify the central tendency and outliers of the data, the following statistical analysis methods can be used:

[0068] Central tendency can be characterized by the mean, median, and mode.

[0069] Predictive analysis: Combining time series analysis and forecasting models, using neural network algorithms, and using historical data to predict future coal demand and supply trends. This helps companies adjust supply chain strategies in advance and balance supply and demand.

[0070] Model architecture of the neural network model:

[0071] Input layer: Input historical coal demand and supply data, which may include relevant characteristics such as time, price, output, consumption, etc.

[0072] Hidden layer: You can set multiple hidden layers, each of which contains a certain number of neurons. Common activation functions include ReLU function: f(x) = max(0, x), Sigmoid function:

[0073] Formula (taking a simple multi-layer perceptron as an example):

[0074] For a single neuron, the input is x1,x2,…,xn, the weight is w1,w2,…,wn, the offset is b, and the activation function is f, then the input is:

[0075] The coal demand forecast results are processed through the intelligent decision-making module to generate the optimal transportation scheduling plan.

[0076] In this step, the intelligent decision-making module performs the following steps:

[0077] Transport scheduling optimization: Using machine learning algorithms, deep learning and reinforcement learning, we can automatically generate the optimal transport scheduling plan based on real-time and historical data analysis results. These algorithms can take into account cost, time and environmental factors to achieve multi-objective optimization.

[0078] Perform data collection and preprocessing:

[0079] It is necessary to collect a large amount of real-time and historical transportation data, including the origin and destination of the goods, transportation time, transportation cost, vehicle information, road conditions, weather, etc. These data are cleaned, preprocessed and feature engineered to convert them into a format suitable for processing by machine learning algorithms.

[0080] Algorithm selection:

[0081] Deep learning algorithm: Use Long Short-Term Memory (LSTM) in RNN to predict shipping time and cost.

[0082] LSTM formula:

[0083] i t =σ(W xi x t +W hi h t-1 +b i )

[0084] f t =σ(W xf x t +W h fh t-1 +b f )

[0085] o t =σ(W xo x t +W ho h t-1 +b o )

[0086]

[0087] h t =o t *tanh(C t )

[0088] Among them, x t is the input, ht-1 is the hidden state at the previous moment, i t 、f t and t They are input gate, forget gate and output gate respectively, C t is the cell state, W is the weight, and b is the bias.

[0089] Reinforcement learning algorithm: Use Deep Q Network (DQN) for decision optimization. DQN selects the best action by estimating the Q value (action value) of different actions.

[0090] The Q value update formula in DQN is:

[0091] Q(s,a)←Q(s,a)+α[r+γmax a′ Q(s′,a′;θ - )-Q(s,a;θ)]

[0092] Among them, Q(s,a;θ) is the state-action value function under the current network parameter θ; Q(s′,a′;θ - ) is the target network parameter θ - The state-action value function is obtained; a is the learning rate, which is used to control the degree of each update, γ is the discount factor, and the value range of γ is [0,1], max a′ Q(s′,a′;θ - ) represents the maximum value of the Q value of selecting the best action ′ according to the target network in the next state s′.

[0093] Perform model training:

[0094] Use the preprocessed data to train the selected algorithm. During the training process, the multi-objective optimization is continuously adjusted:

[0095] In order to consider cost, time and environmental factors at the same time, these objectives can be transformed into a comprehensive reward function. For example, different weights can be assigned to cost, time and environmental impact respectively, and then the total reward can be calculated.

[0096] Model evaluation and optimization:

[0097] Use the test data set to evaluate the trained model and optimize and adjust the model based on the evaluation results.

[0098] Supply chain coordination and optimization: Through advanced operations research methods and heuristic algorithms, it involves the coordination and optimization of procurement, transportation, warehousing, distribution and other links to improve overall efficiency and reduce costs. This includes inventory management optimization, transportation mode selection and warehousing layout planning.

[0099] The transportation tools are tracked through the real-time monitoring and adjustment module, and the transportation plan is adjusted according to the changes in the external environment.

[0100] In this step, the real-time monitoring and adjustment module performs the following steps:

[0101] Transport tool tracking: Using GPS and IoT technology, the location and status of transport tools can be tracked in real time to obtain accurate logistics information. This helps to understand the logistics progress in real time and adjust the transportation plan in time.

[0102] Dynamically adjust the transportation plan: Dynamically adjust the transportation plan according to changes in the external environment, such as traffic congestion, weather conditions, etc. Through real-time data analysis and prediction, risks can be avoided in advance to ensure the smooth progress of the transportation process.

[0103] The embodiment of the present invention also provides a user interaction interface:

[0104] Intuitive operation interface: Provide operators with an intuitive operation interface that displays detailed transportation plans, real-time data, and historical data analysis results in the form of charts, dashboards, etc. The interface design should be concise and clear, so that users can quickly understand and operate.

[0105] Manual adjustment and optimization: Allow operators to manually adjust and optimize transportation plans according to actual needs. Provide convenient adjustment tools and buttons to help users flexibly deal with various complex situations and ensure the smooth implementation of transportation plans.

[0106] It should be understood that, although each step in the flow chart of each embodiment of the present invention is shown in sequence according to the indication of the arrow, these steps are not necessarily performed in sequence according to the order indicated by the arrow. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be performed in other orders. Moreover, at least a portion of the steps in each embodiment may include a plurality of sub-steps or a plurality of stages, and these sub-steps or stages are not necessarily performed at the same time, but can be performed at different times, and the execution order of these sub-steps or stages is not necessarily performed in sequence, but can be performed in turn or alternately with at least a portion of other steps or sub-steps or stages of other steps.

[0107] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0108] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0109] The above-mentioned embodiments only express several implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

[0110] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. An intelligent control system for coal transportation production planning, characterized in that: The system includes a data acquisition module, a data processing and analysis module, an intelligent decision-making module and a real-time monitoring and adjustment module. The data acquisition module is used to collect coal inventory data and transportation data. The data processing and analysis module is used to pre-process the coal inventory data and transportation data, and perform statistical analysis and predictive analysis on the data to obtain coal demand forecast results. The intelligent decision-making module generates a transportation scheduling plan based on the coal demand forecast results. The real-time monitoring and adjustment module is used to track transportation tools and dynamically adjust the transportation plan.

2. The intelligent control system for coal transportation production planning according to claim 1 is characterized in that: The coal inventory data at least includes coal supply data, real-time inventory data and real-time inventory data of quantitative warehouses, and the transportation data at least includes real-time loading progress data, meteorological data, forklift coal supply, belt conveyor supply, coal feeder coal supply and buffer warehouse water level.

3. An intelligent control method for coal transportation production planning, characterized in that: An intelligent control method for coal transportation production planning as claimed in claim 1 or 2, the method comprising: Data collection is performed through the data collection module to obtain coal inventory data and transportation data; The coal inventory data and transportation data are cleaned through the data processing and analysis module, and statistical analysis and pattern recognition are performed on the cleaned data to identify the central trend and outliers of the cleaned data. The data is analyzed through the neural network model to generate coal demand forecast results; The coal demand forecast results are processed through the intelligent decision-making module to generate the optimal transportation scheduling plan; The transportation tools are tracked through the real-time monitoring and adjustment module, and the transportation plan is adjusted according to the changes in the external environment.

4. The intelligent control method for coal transportation production plan according to claim 3 is characterized in that: The step of data cleaning the coal inventory data and transportation data includes performing noise removal, error correction and redundancy elimination on the coal inventory data and transportation data, and merging data from different sources to obtain a consistent data set.

5. The intelligent control method for coal transportation production plan according to claim 3 is characterized in that: The step of performing statistical analysis and pattern recognition on the cleaned data includes performing descriptive analysis by statistical analysis methods to identify the central tendency and outliers of the cleaned data, wherein the central tendency is characterized by the mean, median or mode.

6. The intelligent control method for coal transportation production plan according to claim 3 is characterized in that: In the step of performing data analysis through a neural network model, the neural network model includes an input layer and a hidden layer, the input layer is used to input historical coal demand and supply data, the number of hidden layers is multiple, and each hidden layer contains a preset number of neurons.

7. The intelligent control method for coal transportation production plan according to claim 3 is characterized in that: In the step of processing the coal demand forecast results through the intelligent decision-making module to generate the optimal transportation scheduling plan, the intelligent decision-making module includes using a machine learning algorithm to process the coal demand forecast results, the machine learning algorithm is a deep learning algorithm or a reinforcement learning algorithm, and the machine learning algorithm is trained by data cleansing. During the training process, the parameters of the model are continuously adjusted so that the model can accurately predict the transportation results and optimize the scheduling plan. A comprehensive reward function is constructed according to the goals to be achieved, and the trained machine learning algorithm is evaluated by constructing a test data set, and the machine learning algorithm is optimized and adjusted according to the evaluation results.

8. The intelligent control method for coal transportation production plan according to claim 7 is characterized in that: When the machine learning algorithm is a deep learning algorithm, the long short-term memory network in the RNN is used to predict the transportation time and cost. The long short-term memory network is expressed as: i t =σ(W xi x t +W hi h t-1 +b i ) f t =σ(W xf x t +W hf h t-1 +b f ) o t =σ(W xo x t +W ho h t-1 +b o ) h t =o t *tanh(C t ) Among them, x t is the input, h t-1 is the hidden state at the previous moment, i t 、f t and t They are input gate, forget gate and output gate respectively, C t is the cell state, W is the weight, and b is the bias.

9. The intelligent control method for coal transportation production plan according to claim 7, characterized in that: When the machine learning algorithm is a reinforcement learning algorithm, a deep Q network is used to optimize decision making. The deep Q network selects the optimal action by estimating the Q values ​​of different actions. The Q-value update formula in the deep Q network is: Q(s,a)←Q(s,a)+α[r+γmax a′ Q(s′,a′;θ - )-Q(s,a;θ)] Among them, Q(s,a;θ) is the state-action value function under the current network parameter θ; Q(s′,a′;θ - ) is the target network parameter θ - The state-action value function is obtained; α is the learning rate, which is used to control the degree of each update, γ is the discount factor, and the value range of γ is [0,1], max a′ Q(s′,a′;θ - ) represents the maximum value of the Q value of selecting the best action a′ according to the target network in the next state s′.

10. The intelligent control method for coal transportation production plan according to claim 3, characterized in that: The method also includes building a user interaction interface, which displays the transportation plan, real-time data and historical data analysis results through charts and dashboards.

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