Intelligent port railway freight spraying dust suppression method and system

Abstract

The invention discloses a harbor railway freight spraying dust suppression method and system based on intellectualization, and the method comprises the following steps: arranging monitoring equipment at a harbor railway, and obtaining the position information of a train; based on the train position information and the cargo loading characteristics, a spraying parameter optimization model is constructed and trained; the target goods are sprayed based on the optimal spraying parameters output by the trained spraying parameter optimization model; the spraying coverage effect under different spraying head layout is evaluated, and the optimal spraying head layout is obtained; and under the optimal nozzle layout, in combination with train operation and meteorological parameters, the spraying opportunity and the spraying amount are adjusted in real time, and spraying dust suppression of port railway freight is completed. According to the invention, the precision, efficiency and environmental protection effect of port railway freight spraying operation can be obviously improved, and intelligent and automatic dust suppression operation is realized.

Description

Technical Field

[0001] The present invention belongs to the technical field of spray dust suppression, and in particular relates to an intelligent-based spray dust suppression method and system for port railway freight. Background Art

[0002] The port railway freight spray dust suppression system faces some technical difficulties in its actual application. First, the port railway lines are complex, and freight trains often cross multiple lines when running inside the port. How to accurately identify the specific line and location of the train is a major challenge. Secondly, the loading volume and stacking form of different goods vary greatly. It is necessary to dynamically adjust the spray parameters such as spray angle, atomization particle size, water outlet pressure, etc. according to the characteristics and loading conditions of the goods to achieve precise spraying and improve the dust suppression effect. In addition, the port environment is harsh, with strong winds and sand, and the equipment is prone to failure. How to improve the reliability and stability of the equipment is also a technical problem. Furthermore, improper layout of the spray device can easily lead to waste of water resources, and the layout of the nozzles needs to be optimized.

[0003] In response to the above technical difficulties, it is urgent to propose an intelligent port railway freight spray dust suppression method and system. Summary of the Invention

[0004] In order to solve the above technical problems, the present invention proposes an intelligent port railway freight spray dust suppression method and system to solve the problems existing in the above-mentioned prior art.

[0005] To achieve the above objectives, the present invention provides an intelligent port railway freight spray dust suppression method, comprising the following steps:

[0006] Deploy monitoring equipment on the port railway to obtain train location information;

[0007] Based on the train location information and the characteristics of the loaded cargo, a spray parameter optimization model is constructed and trained;

[0008] The target cargo is sprayed based on the optimal spray parameters output by the trained spray parameter optimization model;

[0009] Evaluate the spray coverage effect under different nozzle layouts to obtain the optimal nozzle layout;

[0010] Under the optimal nozzle layout, combined with train operation and meteorological parameters, the spraying timing and spraying amount are adjusted in real time to complete the spraying dust suppression for port railway freight.

[0011] Optionally, monitoring equipment is deployed on the port railway to obtain train location information, including:

[0012] Based on monitoring equipment, video images of the port are acquired in real time and preprocessed, and features are extracted from the preprocessed video images to obtain train car features; the extracted train car features are compared with a pre-established car feature library to identify the model and number of the train car; based on the model and number of the train car, train scheduling information is obtained; the identified train car information is correlated with the acquired train scheduling information and analyzed to determine the specific route of the train, and the train position information is obtained based on the train's travel speed, travel time and the layout of the monitoring equipment.

[0013] Optionally, the process of constructing and training a spray parameter optimization model based on the train location information and the characteristics of the loaded cargo includes:

[0014] The train's location information and environmental condition data are obtained, and the loaded cargo characteristic parameters are retrieved from a cargo loading information database; a training sample set is constructed based on the cargo characteristic parameters, the train's location information, and the environmental condition data; a spray parameter optimization model is constructed based on a support vector machine, and the spray parameter optimization model is trained based on the training sample set to obtain a trained spray parameter optimization model.

[0015] Optionally, the optimal spray parameters output by the trained spray parameter optimization model include: optimal spray angle, optimal atomized particle size, and optimal water outlet pressure.

[0016] Optionally, the spray coverage effect under different nozzle arrangements is evaluated, and the process of obtaining the optimal nozzle layout includes:

[0017] Based on fluid dynamics simulation technology, the spray coverage effects under different nozzle layouts are simulated and evaluated. Based on the evaluation results, the particle swarm optimization algorithm is used to optimize the nozzle layout position and number to obtain the nozzle layout plan with the best spray coverage effect.

[0018] Optionally, under the optimal nozzle layout, the process of adjusting the spraying timing and spraying amount in real time in combination with train operation and meteorological parameters includes:

[0019] A water mist coverage effect prediction model is constructed, and the train running speed parameters and wind direction and speed parameters are obtained. The train running speed parameters and wind direction and speed parameters are input into the water mist coverage effect prediction model to predict the optimal spraying time and spraying amount under the current environment; if the predicted optimal spraying time coincides with the current moment, the spraying device is controlled to spray according to the predicted optimal spraying amount; if the predicted optimal spraying time does not coincide with the current moment, spraying is temporarily not performed, and the prediction for the next moment is continued.

[0020] Optionally, a vibration sensor and a temperature sensor are installed on the spray device to collect the operating parameters of the spray device in real time; the operating parameters are analyzed based on time series analysis and anomaly detection algorithm, and a fault warning is issued when the operating parameters exceed the corresponding preset thresholds.

[0021] The present invention also provides an intelligent port railway freight spray dust suppression system for implementing an intelligent port railway freight spray dust suppression method, comprising: a train position monitoring module, a spray parameter optimization module, a nozzle layout optimization module, a spray timing adjustment module, and a spray device detection module;

[0022] The train location monitoring module is used to deploy monitoring equipment on the port railway to obtain train location information;

[0023] The spray parameter optimization module is used to build and train a spray parameter optimization model based on the train position information and the characteristics of the loaded cargo, and spray the target cargo based on the optimal spray parameters output by the trained spray parameter optimization model;

[0024] The nozzle layout optimization module is used to evaluate the spray coverage effect under different nozzle layouts to obtain the optimal nozzle layout;

[0025] The spray timing adjustment module is used to adjust the spray timing and spray amount in real time under the optimal nozzle layout in combination with train operation and meteorological parameters;

[0026] The spray device detection module is used to analyze the operating parameters of the spray device based on time series analysis and anomaly detection algorithm, and issue a fault warning when the operating parameters exceed the corresponding preset thresholds.

[0027] The present invention also provides an electronic device, comprising: a memory and a processor; the memory is used to store a program; the processor is used to execute the program to implement each step of the intelligent port railway freight spray dust suppression method.

[0028] The present invention also provides a readable storage medium on which a computer program is stored. When the computer program is executed by a processor, each step of the intelligent port railway freight spray dust suppression method is implemented.

[0029] Compared with the prior art, the present invention has the following advantages and technical effects:

[0030] This invention deploys video surveillance equipment at key locations, employs computer vision technology to identify train positions and carriage features, and establishes a spray parameter optimization model based on a cargo loading information database. This enables precise spraying and improves dust suppression. The invention utilizes computational fluid dynamics simulation technology to optimize nozzle layout and uses sensors to obtain real-time train operation and meteorological parameters, enabling dynamic adjustment of spray strategies to avoid water waste. The invention also uses vibration and temperature sensors to monitor equipment operating status, providing early warning of failure risks and improving equipment reliability and stability.

[0031] The present invention can significantly improve the accuracy, efficiency and environmental protection effect of port railway freight spraying operations, and realize intelligent and automated dust suppression operations. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] The accompanying drawings, which constitute part of this application, are intended to provide a further understanding of this application. The exemplary embodiments and descriptions of this application are intended to explain this application and do not constitute an improper limitation on this application. In the accompanying drawings:

[0033] Figure 1 is a flow chart of a method according to an embodiment of the present invention;

[0034] Figure 2 Schematic diagram of the system structure of an embodiment of the present invention. DETAILED DESCRIPTION

[0035] It should be noted that, in the absence of conflict, the embodiments and features of the embodiments in this application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0036] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0037] Example 1

[0038] like Figure 1 As shown, this embodiment provides an intelligent port railway freight spray dust suppression method, including the following steps:

[0039] Deploy monitoring equipment on the port railway to obtain train location information;

[0040] Based on the train location information and the characteristics of the loaded cargo, a spray parameter optimization model is constructed and trained;

[0041] The target cargo is sprayed based on the optimal spray parameters output by the trained spray parameter optimization model;

[0042] Evaluate the spray coverage effect under different nozzle layouts to obtain the optimal nozzle layout;

[0043] Under the optimal nozzle layout, combined with train operation and meteorological parameters, the spraying timing and spraying amount are adjusted in real time to complete the spraying dust suppression for port railway freight.

[0044] It is feasible to deploy video surveillance equipment at key locations along the port railway line, use computer vision technology to analyze video images, identify train car features, and combine train dispatch information to accurately determine the specific line and location of the train. The process includes:

[0045] Video surveillance equipment is deployed at key locations along the port's railway lines to acquire real-time video image data. Computer vision technology is used to pre-process the acquired video images, including operations such as image denoising, enhancement, and normalization, to obtain high-quality video images. Deep learning algorithms, such as convolutional neural networks (CNN) or recurrent neural networks (RNN), are used to extract features from the pre-processed video images to acquire key features of the train carriages. The extracted carriage features are compared with a pre-established carriage feature library to identify the specific model and number of the train carriages. Train dispatch information, including the train number, departure station, terminal station, and current route, is acquired in real time from the train dispatching system. The identified train carriage information is correlated with the acquired train dispatching information to determine the specific route the train is currently on. The precise position coordinates of the train are calculated based on the train's speed and travel time on the railway line, combined with the deployment location of the video surveillance equipment, to achieve real-time tracking and monitoring of the train's position.

[0046] As a feasible approach, video surveillance equipment can be deployed along busy railway lines in ports to monitor train locations and status in real time. For example, high-definition cameras can be installed at key locations such as major railroad junctions, marshalling yard entrances, and freight yard loading and unloading areas to ensure clear capture of every passing train car. These cameras need to have night vision capabilities and a certain degree of weather resistance to ensure all-weather monitoring. The acquired real-time video image data is the foundation for subsequent analysis. This acquired video image data is often affected by environmental noise, such as lighting changes, rain and snow, and camera shake, which can degrade image quality. Therefore, image preprocessing is necessary, including image denoising, enhancement, and normalization. For example, Gaussian filtering can be used to remove image noise, histogram equalization can be used to enhance image contrast, and pixel values can be normalized to a specific range, such as 0 to 1, to facilitate processing by deep learning algorithms. High-quality video images improve the accuracy of feature extraction. Deep learning algorithms, particularly convolutional neural networks (CNNs), are well-suited for processing image data. The preprocessed video images are fed into a CNN, which automatically extracts key train car features through multiple layers of convolution and pooling. For example, car color, logo, serial number, dimensions, and number of axles can all serve as features. To improve recognition accuracy, transfer learning can be employed, leveraging models pre-trained on large datasets such as ImageNet for fine-tuning, accelerating training and enhancing model generalization. Recurrent neural networks (RNNs) can be used to analyze temporal information in video images, such as train speed and direction. The extracted car features are then compared against a pre-established car feature library. This library contains information on all types of train cars at the port, such as model, serial number, dimensions, and load capacity. For example, if a car's feature vector is [red, K801, 15 meters] and the feature library contains a record [red, K801, 15 meters], the car model can be identified as K801. Similarity calculation methods, such as cosine similarity or Euclidean distance, can be used for feature comparison. The train dispatching system contains a wealth of train operation information, such as train number, departure station, destination, estimated arrival time, and current route. This information can be accessed in real time through an interface. For example, train K1234, departing from station A to station B, is currently operating on route 1. This information is crucial for determining the train's location. By correlating the identified car information with the acquired train dispatch information, the train's current route can be determined. For example, if car K801 is identified as belonging to train K1234, and K1234 is currently operating on route 1, it can be determined that car K801 is currently on route 1. To more accurately track the train's location, calculations can be made based on the placement of video surveillance equipment, the train's speed, and travel time.For example, if carriage K801 passed camera A at the start of Line 1 at 10:00 a.m. at a speed of 20 m / s, its position at 10:01 a.m. can be estimated to be 20 x 60 = 1200 meters from camera A. Data from multiple cameras can be cross-correlated, improving positioning accuracy. Real-time tracking and monitoring of train positions helps improve the efficiency and safety of port rail transportation.

[0047] It is feasible to establish a spray parameter optimization model based on the identified train location information and the cargo characteristic parameters in the cargo loading information database, such as cargo type, volume, and weight, using the support vector machine algorithm. The model takes cargo characteristics, train location, and environmental conditions as inputs and outputs the optimal spray angle, atomized particle size, and water outlet pressure. The process of achieving precise spraying includes:

[0048] The real-time position information and environmental condition data of the train are obtained, and characteristic parameters such as the type, volume and weight of the cargo to be sprayed are read from the cargo loading information database; based on the characteristic parameters of the cargo, the train position information and the environmental condition data, a training sample set of the support vector machine algorithm is constructed, and the algorithm model is trained to obtain a spray parameter optimization model; the characteristic parameters of the cargo to be sprayed, the real-time position of the train and the current environmental conditions are used as input and substituted into the trained spray parameter optimization model to obtain the optimal spray angle, atomized particle size and water outlet pressure parameters in this case; according to the optimal spray parameters output by the optimization model, the nozzle angle of the spray device is controlled The system uses the density, atomization particle size and water outlet pressure to precisely spray the target cargo on the train. During the spraying process, the system obtains real-time data on train position changes and environmental conditions, and substitutes them into the optimization model to dynamically adjust the spray parameters to ensure the accuracy of the spraying effect. After the spraying is completed, the feedback data on the cargo spraying effect is obtained and added to the training sample set of the support vector machine algorithm. The optimization model is retrained to adapt it to more cargo characteristics and environmental conditions. The optimization model is regularly evaluated and improved. According to the new cargo characteristics, environmental conditions and spraying effect data, the spray parameter control strategy is continuously improved and optimized to improve the intelligence level of cargo spraying.

[0049] As a feasible approach, meteorological sensors can be deployed throughout the port to obtain environmental data, such as temperature, humidity, and wind speed. For example, temperature and humidity sensors, wind speed sensors, and rainfall sensors can be installed at various locations across the port to collect real-time environmental data and transmit it to a central control system via wireless networks. The cargo loading information database stores various cargo characteristics, such as coal type, volume, weight, flammability, and moisture content; iron ore type, particle size, grade, and moisture content; and container cargo type, size, weight, and hazard level. These parameters are used to develop appropriate spraying strategies based on the characteristics of each cargo. For example, for flammable and explosive cargo, a lower water discharge pressure and smaller atomized particle size are required to prevent static electricity accumulation and hazards. For dusty cargo, a higher water discharge pressure and larger atomized particle size are required to effectively suppress dust. Constructing the training sample set for the support vector machine algorithm requires collecting a large amount of historical spraying data, including cargo characteristics, environmental conditions, spraying parameters, and spraying effectiveness data. For example, data on spraying various cargoes under different environmental conditions over the past year is collected, including cargo type, volume, weight, temperature, humidity, wind speed, spray angle, atomized particle size, water outlet pressure, as well as dust concentration and temperature changes after spraying. This data is cleaned and organized into a structured dataset, which serves as training samples for the support vector machine algorithm. The process of training the spray parameter optimization model involves using the support vector machine algorithm to learn the mapping relationship between cargo characteristics, environmental conditions, and optimal spray parameters from the training sample set. For example, through training, the model can learn that for dust-prone cargo like coal in a hot and dry environment, a larger spray angle, larger atomized particle size, and higher water outlet pressure should be used. By inputting the characteristic parameters of the cargo to be sprayed, the train's real-time location, and the current environmental conditions into the trained spray parameter optimization model, the optimal spray parameters for that situation can be determined. For example, a train carrying coal is located on Port Line 1 in an ambient temperature of 30 degrees Celsius, a humidity of 60%, and a wind speed of 3 meters per second. This data was fed into the optimization model, which output the optimal spray angle of 45 degrees, atomized particle size of 50 microns, and water outlet pressure of 3 MPa. Based on the optimal spray parameters output by the optimization model, the spray device's nozzle angle, atomized particle size, and water outlet pressure were controlled. For example, the spray device's motor was controlled to rotate the nozzle to 45 degrees; the spray pump speed was controlled to adjust the water outlet pressure to 3 MPa; and the nozzle aperture was controlled to adjust the atomized particle size to 50 microns. During the spraying process, real-time data on train position changes and environmental conditions was collected and fed into the optimization model to dynamically adjust the spray parameters.For example, while a train is traveling, the wind speed gradually increases to 5 m / s. The new wind speed data is input into the optimization model, which recalculates the optimal spray parameters and sends them to the spray device, dynamically adjusting the spray angle, atomized particle size, and water outlet pressure to ensure accurate spraying. After spraying is complete, feedback data on the cargo spraying effect, such as dust concentration and temperature changes, is needed. This data is incorporated into the training sample set of the support vector machine algorithm and the optimization model is retrained. This adapts the model to a wider range of cargo characteristics and environmental conditions, improving its generalization and prediction accuracy. Regular evaluation and improvement of the optimization model allows continuous refinement and optimization of the spray parameter control strategy based on new cargo characteristics, environmental conditions, and spraying effect data. For example, over time, new cargo types may emerge or environmental conditions may change significantly. In these cases, it is necessary to collect new data, retrain the optimization model, or adjust model parameters to adapt to the new conditions and enhance the intelligence of cargo spraying.

[0050] Furthermore, based on the optimal spray parameters output by the optimization model, the nozzle angle, atomization particle size, and water outlet pressure of the spray device are controlled to implement precise spraying of target cargo on the train. The process includes:

[0051] Based on cargo attributes and environmental conditions, a spray optimization model is established. Using machine learning algorithms such as genetic algorithms and particle swarm optimization, an optimal combination of spray parameters, including nozzle angle, atomized particle size, and water outlet pressure, is determined. This optimal combination of spray parameters, derived from the optimization model, is transmitted to the spray control unit. Based on the received parameters, the control unit adjusts the nozzle angle motor, atomizer, and water pump to adjust the nozzle angle, atomized particle size, and water outlet pressure to the values corresponding to the optimal parameter combination. Image recognition technology is used to identify and locate cargo on the train, capturing information such as its size and location. This information is then transmitted to the spray control unit. Based on the received target cargo information and the set optimal spray parameters, the control unit controls the nozzles to align with the target cargo and initiates the spraying process. The nozzle angle is adjusted to ensure coverage of the target cargo. During the spraying process, the optimized combination of atomized particle size and water outlet pressure ensures uniform distribution of the water mist across the surface of the target cargo, enhancing the spraying effect. At the same time, a pressure sensor monitors the outlet water pressure and dynamically adjusts the pump's operating status based on pressure fluctuations to ensure that the outlet water pressure remains within the optimal parameter range. After the spraying is completed, image recognition technology is used to detect the water mist coverage of the cargo surface. The spraying effect is calculated based on the coverage rate and transmitted to the optimization model as feedback for iterative optimization. Based on the feedback data from multiple spraying cycles, the optimization model uses a machine learning algorithm to iteratively optimize the model parameters, continuously improving the accuracy of the optimal spraying parameter combination and achieving precise spraying. The optimized model parameters are then updated to the sprinkler control unit to guide subsequent spraying operations.

[0052] It is feasible to use computational fluid dynamics simulation technology to model and analyze the nozzle layout plan to optimize the location and number of nozzles. At the same time, train speed sensors and wind direction and speed sensors are used to obtain real-time train operation and meteorological parameters. The process of dynamically adjusting the spraying timing and spraying amount includes:

[0053] Computational fluid dynamics simulation technology was used to simulate the water mist distribution under different nozzle layouts and obtain coverage evaluation results for each layout. Based on the coverage evaluation results, a particle swarm optimization algorithm was used to optimize the nozzle layout position and number, resulting in the nozzle layout with the best coverage. Particle swarm optimization can be used to find the optimal nozzle layout. The basic principle of the algorithm is to simulate the foraging behavior of bird flocks and find the optimal solution through the interaction between particles.

[0054] Train speed parameters are acquired in real time through train speed sensors, and meteorological wind direction and speed parameters are acquired in real time through wind direction and speed sensors. These parameters are input into a pre-established water mist coverage prediction model to dynamically predict the optimal spraying timing and amount under the current environment. If the predicted optimal spraying timing matches the current moment, the spraying system is controlled to spray according to the predicted optimal amount. If the predicted optimal spraying timing does not match the current moment, spraying is temporarily suspended and the prediction continues for the next moment. Based on the real-time monitoring results of the water mist coverage effect, a reinforcement learning algorithm is used to dynamically optimize the water mist coverage prediction model to improve the accuracy of the water mist coverage effect prediction.

[0055] Furthermore, train speed sensors can measure the train's running speed in real time. For example, speed sensors installed on train wheels can convert the wheel's rotational speed into a train speed signal. Wind direction and speed sensors can obtain meteorological information in real time. For example, ultrasonic anemometers can measure wind speed and direction. Data from these sensors can be transmitted to the control system via a wireless network. Train speed sensors can measure the train's running speed in real time. For example, speed sensors installed on train wheels can convert the wheel's rotational speed into a train speed signal. Wind direction and speed sensors can obtain meteorological information in real time. For example, ultrasonic anemometers can measure wind speed and direction. Data from these sensors can be transmitted to the control system via a wireless network.

[0056] As an additional embodiment, Doppler radar speed measurement technology is used to obtain real-time train speed, combined with cargo loading characteristics data, and a dynamic programming algorithm is used to calculate the optimal spraying timing. When the train slows down or stops, the start and stop of the spray device and the spray intensity are automatically adjusted to avoid water waste. The specific process includes:

[0057] If the train speed changes, the dynamic programming algorithm is triggered to calculate the optimal spraying time according to the cargo loading characteristic data, and the calculation result is sent to the spray control device; after receiving the optimal spraying time, the spray control device determines whether the current train speed is in a deceleration or parking state. If so, the start and stop state and spray intensity parameters of the spray device are automatically adjusted according to the speed change amplitude through the fuzzy control algorithm; during the constant speed operation stage of the train, the spray control device uses the Kalman filter algorithm to smooth the speed data to avoid frequent start and stop of the spray device; the water flow rate of the spray device is collected in real time through the water flow sensor, and the spray intensity parameters are automatically adjusted according to the speed change amplitude through the fuzzy control algorithm. The system calculates the total water consumption of the spraying process based on the spraying time. If the water consumption per unit time is significantly higher than the average value, it is judged as a spraying failure and a warning signal is sent to the fault diagnosis module. After receiving the warning signal, the fault diagnosis module uses the decision tree algorithm to determine the type of spraying failure and sends corresponding control instructions to the train control system based on the failure type. After the train arrives at the station and stops, the spraying control module automatically turns off the spraying device and uploads the accumulated water consumption data to the water resources management system. The water resources management system uses an incremental learning algorithm to perform statistical analysis on historical water consumption data, continuously optimize the spraying control strategy, and maximize water conservation.

[0058] It is feasible to install vibration sensors and temperature sensors on the sprinkler to collect equipment operating parameters in real time. Through time series analysis and anomaly detection algorithms, the equipment operating status is monitored in real time, early warning of equipment failure risks is provided, and preventive maintenance strategies are formulated to improve equipment reliability and stability. The process includes:

[0059] Vibration and temperature sensors are installed at key locations on the sprinkler equipment to collect real-time operating parameters such as vibration and temperature. The collected vibration and temperature data is transmitted to the monitoring system, which processes the data in batches according to preset time intervals to generate a series of time series data. For this time series data, a time series analysis algorithm, such as the ARIMA model, is used to statistically analyze historical data to establish a baseline model for vibration and temperature under normal equipment operating conditions. Anomaly detection algorithms, such as the Isolation Forest or Local Anomaly Factor, are used to compare the real-time data with the baseline model. If the data deviates from the baseline model by more than a preset threshold, it is identified as an anomaly and a fault warning is triggered. When an equipment anomaly is detected, the system automatically generates a fault report and notifies relevant personnel to inspect and maintain the equipment. The system also provides maintenance recommendations based on the severity of the anomaly and the type of fault. Based on the equipment's historical fault data and anomaly detection results, an association rule mining algorithm is used to identify correlations and temporal patterns between different fault types, thereby formulating a preventive maintenance strategy. The preventive maintenance strategy is then entered into the system, with maintenance cycles and triggering rules defined. When the equipment has been running for a certain period of time or an early warning signal appears, the system automatically creates a maintenance work order and arranges professionals to perform preventive maintenance, thereby improving the reliability and stability of the equipment.

[0060] As an additional implementation, high-definition cameras are installed in dust-prone cargo loading and unloading areas. Image difference algorithms are used to analyze dust changes on the cargo surface in real time. By setting a dust coverage percentage threshold, when the dust level exceeds the preset threshold, the sprinkler system is automatically triggered to spray targeted areas, enhancing dust suppression and improving the working environment.

[0061] At least one high-definition camera is installed in a cargo loading and unloading area prone to dust generation. This camera is connected to an image processing device and is used to capture a first image of the cargo surface in real time. After acquiring the first image, the image processing device uses an image difference algorithm to compare and analyze changes in the cargo surface image at different times to determine the change in the dust coverage area on the cargo surface. If the change in dust coverage area exceeds a preset dust coverage area threshold, the dust level on the cargo surface is determined to be excessive, triggering the next step; otherwise, image acquisition continues. Based on the change in dust coverage area, a key spraying area on the cargo surface is determined, and the coordinates of the spraying area are transmitted to the spray control device. Upon receiving the coordinates of the key spraying area, the spray control device activates the spraying device in the corresponding area to perform targeted spraying to suppress dust on the cargo surface. After spraying is complete, the high-definition camera continues to capture a second image of the cargo surface and transmits it to the image processing device. The image processing device analyzes the second image to determine whether the dust level on the cargo surface has fallen below the threshold. If so, dust suppression is complete and targeted spraying continues until the dust level meets the standard.

[0062] As an additional implementation example, an information management platform for port and railway freight spraying operations was constructed, using IoT technology to interconnect equipment from environmental protection, railway, and port departments. The platform integrates data such as train location, cargo information, spraying parameters, and equipment status, and displays them through data analysis and visualization.

[0063] Acquire real-time data of the port railway freight spraying operation site, the real-time data including train location, cargo information, spraying parameters and equipment status; pre-process the real-time data to obtain pre-processed real-time data; use association rule mining algorithm to analyze the association relationship between train location, cargo information and spraying parameters in the pre-processed real-time data; use support vector machine algorithm to build equipment failure prediction model based on equipment status data in the pre-processed real-time data; predict equipment failure risk through the equipment failure prediction model and obtain maintenance plan; acquire historical operation data of port railway freight; use machine learning algorithm to mine the historical operation data to obtain excellent operation mode and experience; Describe excellent operating models and experiences to form a best practice guidance plan; obtain cargo information and weather conditions for new operating tasks; use an intelligent recommendation algorithm to obtain the optimal operating plan based on the cargo information, weather conditions and best practice guidance plan; obtain multi-source heterogeneous data on port railway freight; fuse the multi-source heterogeneous data to obtain fused data; optimize port railway freight scheduling based on the fused data to obtain an optimized capacity deployment plan; use digital twin technology to build a full-process simulation model of port railway freight; simulate operation plans in the full-process simulation model of port railway freight based on the fused data; evaluate the effectiveness of the operation plan and obtain decision-making recommendations.

[0064] like Figure 2 As shown, this embodiment also provides an intelligent port railway freight spray dust suppression system for implementing an intelligent port railway freight spray dust suppression method, including: a train position monitoring module, a spray parameter optimization module, a nozzle layout optimization module, a spray timing adjustment module and a spray device detection module;

[0065] The train location monitoring module is used to deploy monitoring equipment on the port railway to obtain train location information;

[0066] The spray parameter optimization module is used to build and train a spray parameter optimization model based on the train position information and the characteristics of the loaded cargo, and spray the target cargo based on the optimal spray parameters output by the trained spray parameter optimization model;

[0067] The nozzle layout optimization module is used to evaluate the spray coverage effect under different nozzle layouts to obtain the optimal nozzle layout;

[0068] The spray timing adjustment module is used to adjust the spray timing and spray amount in real time under the optimal nozzle layout in combination with train operation and meteorological parameters;

[0069] The spray device detection module is used to analyze the operating parameters of the spray device based on time series analysis and anomaly detection algorithm, and issue a fault warning when the operating parameters exceed the corresponding preset thresholds.

[0070] Example 2

[0071] This embodiment also provides an electronic device, including: a memory and a processor; the memory is used to store a program; the processor is used to execute the program to implement each step of the intelligent port railway freight spray dust suppression method.

[0072] Example 3

[0073] This embodiment also provides a readable storage medium on which a computer program is stored. When the computer program is executed by a processor, each step of the intelligent port railway freight spray dust suppression method is implemented.

[0074] The above are merely preferred embodiments of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.

Claims

1. A method for spraying dust suppression for port railway freight based on intelligence, characterized in that: The following steps are involved: Deploy monitoring equipment on the port railway to obtain train location information; Based on the train location information and the characteristics of the loaded cargo, a spray parameter optimization model is constructed and trained; The target cargo is sprayed based on the optimal spray parameters output by the trained spray parameter optimization model; Evaluate the spray coverage effect under different nozzle layouts to obtain the optimal nozzle layout; Under the optimal nozzle layout, combined with train operation and meteorological parameters, the spraying timing and spraying amount are adjusted in real time to complete the spraying dust suppression for port railway freight.

2. The intelligent port railway freight spray dust suppression method according to claim 1 is characterized in that: The process of deploying monitoring equipment on the port railway to obtain train location information includes: Based on monitoring equipment, video images of the port are acquired in real time and preprocessed, and features are extracted from the preprocessed video images to obtain train car features; the extracted train car features are compared with a pre-established car feature library to identify the model and number of the train car; based on the model and number of the train car, train scheduling information is obtained; the identified train car information is correlated with the acquired train scheduling information and analyzed to determine the specific route of the train, and the train position information is obtained based on the train's travel speed, travel time and the layout of the monitoring equipment.

3. The intelligent port railway freight spray dust suppression method according to claim 1 is characterized in that: Based on the train location information and the characteristics of the loaded cargo, the process of constructing and training a spray parameter optimization model includes: The train's location information and environmental condition data are obtained, and the loaded cargo characteristic parameters are retrieved from a cargo loading information database; a training sample set is constructed based on the cargo characteristic parameters, the train's location information, and the environmental condition data; a spray parameter optimization model is constructed based on a support vector machine, and the spray parameter optimization model is trained based on the training sample set to obtain a trained spray parameter optimization model.

4. The intelligent port railway freight spray dust suppression method according to claim 3 is characterized in that: The optimal spray parameters output by the trained spray parameter optimization model include: optimal spray angle, optimal atomized particle size and optimal water outlet pressure.

5. The intelligent port railway freight spray dust suppression method according to claim 1 is characterized in that: The process of evaluating the spray coverage effect under different nozzle layouts and obtaining the optimal nozzle layout includes: Based on fluid dynamics simulation technology, the spray coverage effects under different nozzle layouts are simulated and evaluated. Based on the evaluation results, the particle swarm optimization algorithm is used to optimize the nozzle layout position and number to obtain the nozzle layout plan with the best spray coverage effect.

6. The intelligent port railway freight spray dust suppression method according to claim 5 is characterized in that: Under the optimal nozzle layout, the process of adjusting the spraying timing and spraying amount in real time in combination with train operation and meteorological parameters includes: A water mist coverage effect prediction model is constructed, and the train running speed parameters and wind direction and speed parameters are obtained. The train running speed parameters and wind direction and speed parameters are input into the water mist coverage effect prediction model to predict the optimal spraying time and spraying amount under the current environment; if the predicted optimal spraying time coincides with the current moment, the spraying device is controlled to spray according to the predicted optimal spraying amount; if the predicted optimal spraying time does not coincide with the current moment, spraying is temporarily not performed, and the prediction for the next moment is continued.

7. The intelligent port railway freight spray dust suppression method according to claim 1 is characterized in that: It also includes installing vibration sensors and temperature sensors on the spray device to collect the operating parameters of the spray device in real time; analyzing the operating parameters based on time series analysis and anomaly detection algorithms, and issuing a fault warning when the operating parameters exceed the corresponding preset thresholds.

8. An intelligent port railway freight spray dust suppression system, characterized in that: The method for implementing the intelligent port railway freight spray dust suppression method according to any one of claims 1 to 7 comprises: a train position monitoring module, a spray parameter optimization module, a nozzle layout optimization module, a spray timing adjustment module, and a spray device detection module; The train location monitoring module is used to deploy monitoring equipment on the port railway to obtain train location information; The spray parameter optimization module is used to build and train a spray parameter optimization model based on the train position information and the characteristics of the loaded cargo, and spray the target cargo based on the optimal spray parameters output by the trained spray parameter optimization model; The nozzle layout optimization module is used to evaluate the spray coverage effect under different nozzle layouts to obtain the optimal nozzle layout; The spray timing adjustment module is used to adjust the spray timing and spray amount in real time under the optimal nozzle layout in combination with train operation and meteorological parameters; The spray device detection module is used to analyze the operating parameters of the spray device based on time series analysis and anomaly detection algorithm, and issue a fault warning when the operating parameters exceed the corresponding preset thresholds.

9. An electronic device, characterized in that: include: memory and processor; The memory is used to store programs; The processor is used to execute the program to implement each step of the intelligent port railway freight spray dust suppression method as described in any one of claims 1 to 8.

10. A readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, each step of the intelligent port railway freight spray dust suppression method according to any one of claims 1 to 8 is implemented.

Images