A method and system for analyzing characteristics of dust pollution in a port area and identifying sensitive factors

Abstract

The present application relates to the technical field of port dust pollution analysis, in particular to a port dust pollution characteristic analysis and sensitive factor identification method and system, the method comprising the following steps: obtaining port multi-source detection information through a port data detection module, processing the port multi-source detection information using a data optimization processing mechanism to obtain a port multi-dimensional detection information database; setting up a port dust characteristic analysis system, combining the port multi-dimensional detection information database to obtain a port dust data characteristic analysis result, a dust movement characteristic analysis result and a dust spatial distribution characteristic analysis result; establishing a port dust pollution sensitive factor prediction system to obtain the action relationship between different sensitive factors; designing an initial management scheme based on the data characteristic, movement characteristic and spatial distribution characteristic analysis results, and adjusting the initial management scheme according to the action relationship to obtain a final management scheme. The present application realizes scientific management of port dust pollution based on dust pollution characteristics and sensitive factors.

Description

Technical Field

[0001] This invention relates to the field of port area dust pollution analysis technology, specifically a method and system for analyzing port area dust pollution characteristics and identifying sensitive factors. Background Technology

[0002] As cargo distribution centers, ports are increasingly facing dust pollution problems during loading, unloading, transportation, and storage. Port dust pollution not only affects the health of port workers but also impacts the surrounding environment and residents' lives. Current methods for analyzing and identifying port dust pollution have limitations in practical application. Port information monitoring methods primarily rely on equipment sampling and transmission, failing to integrate and optimize information from different sources, resulting in low accuracy of port monitoring information. Existing dust pollution analysis methods often focus on analyzing single-attribute factors, lacking in-depth research into the comprehensive mechanisms of multi-factor interactions. Consequently, they struggle to accurately identify and predict port dust pollution, failing to ensure the targeted effectiveness of dust control measures and hindering the effective reduction of port dust pollution levels.

[0003] Therefore, it is necessary to integrate intelligent modules and multi-dimensional analysis mechanisms to effectively monitor and optimize port information, which will help to accurately predict and analyze the characteristics and sensitive factors of dust pollution in the port area, thereby providing technical support for the sustainable development and stable operation of the port. Summary of the Invention

[0004] To address the shortcomings of existing methods and the needs of practical applications, this study aims to achieve efficient monitoring and optimized processing of multi-source information in ports, provide a data foundation for predicting the characteristics and sensitive factors of dust pollution in port areas, and further provide technical support for the sustainable development and stable operation of ports by analyzing monitoring information based on a multi-level analysis system. On the one hand, this invention provides a method for analyzing the characteristics of dust pollution in port areas and identifying sensitive factors. The method includes the following steps: obtaining multi-source detection information of the port area through a port area data detection module; processing the multi-source detection information of the port area using the data optimization processing mechanism in the port area data detection module to obtain a multi-dimensional detection information database of the port area; setting up a port area dust characteristic analysis system; obtaining data characteristic analysis results, dust movement characteristic analysis results, and dust spatial distribution characteristic analysis results of port area dust based on the multi-dimensional detection information database and the port area dust characteristic analysis system; establishing a port area dust pollution sensitive factor prediction system; obtaining the interaction relationship between different sensitive factors using the port area dust pollution sensitive factor prediction system and the port area multi-dimensional detection information database; designing an initial management plan for port area dust pollution based on the data characteristic analysis results, the dust movement characteristic analysis results, and the dust spatial distribution characteristic analysis results; and adjusting the initial management plan according to the interaction relationship to obtain a final management plan for port area dust pollution. This invention can acquire multi-source detection information to form multi-dimensional detection information, providing an accurate and reliable data foundation for subsequent analysis; the port area dust characteristic analysis system can comprehensively and deeply understand the characteristics of port area dust; and the port area dust pollution sensitive factor prediction system helps to identify key factors of port area dust pollution.

[0005] Optionally, the step of processing multi-source detection information in the port area using the data optimization processing mechanism in the port area data detection module to obtain a multi-dimensional detection information database for the port area includes: analyzing the multi-source detection information in the port area based on the data optimization processing mechanism to obtain difference sequence information of the multi-source detection information in the port area; analyzing the degree of information deviation of the difference sequence information based on the data optimization processing mechanism and the difference sequence information; and correcting the multi-source detection information in the port area based on the degree of information deviation to obtain corrected multi-source detection information. This invention corrects multi-source detection information based on the degree of deviation, which can effectively eliminate the influence of abnormal data, improve the accuracy and reliability of the data, and ensure the authenticity and validity of subsequent analysis results.

[0006] Optionally, the step of processing multi-source detection information in the port area using the data optimization processing mechanism in the port area data detection module to obtain a multi-dimensional detection information database for the port area includes: configuring an information state function-observation function and an information iterative optimization model in the data optimization processing mechanism; the data optimization processing mechanism analyzes the corrected multi-source detection information based on the information state function-observation function to obtain state analysis results and observation analysis results of the multi-source detection information; the data optimization processing mechanism combines the state analysis results, the observation analysis results, and the information iterative optimization model to iteratively optimize and integrate the corrected multi-source detection information to obtain a multi-dimensional detection information database for the port area. The information state function-observation function of this invention enables the data optimization processing mechanism to analyze the corrected multi-source detection information from different perspectives, reducing the bias caused by single-perspective analysis; the information iterative optimization model realizes dynamic iterative optimization of multi-source detection information.

[0007] Optionally, configuring the information state function-observation function and the information iterative optimization model in the data optimization processing mechanism includes: obtaining a time-series-multi-source detection information set based on the corrected multi-source detection information; constructing an information state function-observation function based on the time-series-multi-source detection information set; and establishing an information iterative optimization model based on the time-series-multi-source detection information set and the information state function-observation function. This invention enables better analysis of the interactions between various data sources, contributing to a deeper understanding of the complex mechanisms of dust pollution in port areas.

[0008] Optionally, the establishment of the port area dust characteristic analysis system, based on the port area multi-dimensional detection information database and the port area dust characteristic analysis system, to obtain the data characteristic analysis results, dust movement characteristic analysis results, and dust spatial distribution characteristic analysis results of port area dust includes: setting a data characteristic analysis layer, a dust movement characteristic analysis layer, and a dust spatial distribution characteristic analysis layer in the port area dust characteristic analysis system; setting data statistical parameters in the data characteristic analysis layer, including average value, maximum value, minimum value, and standard deviation; setting prediction functions for the dust flight distance of different particle sizes in the dust movement characteristic analysis layer; and setting prediction functions for the dust flight distance of the dust source center and a monitoring area grid division mechanism in the dust spatial distribution characteristic analysis layer. The various analysis layers of this invention are closely interconnected, which can comprehensively present the actual distribution of dust in the port area, thereby ensuring the accuracy of the dust pollution analysis results.

[0009] Optionally, the establishment of the port area dust characteristic analysis system, based on the port area multi-dimensional detection information database and the port area dust characteristic analysis system, to obtain the data characteristic analysis results, dust motion characteristic analysis results, and dust spatial distribution characteristic analysis results of port area dust includes: calculating the average, maximum, minimum, and standard deviation of port area dust in different time series through the data characteristic analysis layer and the port area multi-dimensional detection information database, and obtaining the data characteristic analysis results of port area dust by combining the average, maximum, minimum, and standard deviation; analyzing the flight distance of dust with different particle sizes in different time series using the dust flight distance prediction function, and obtaining the dust motion characteristic analysis results based on the flight distance and the port area multi-dimensional detection information database. This invention utilizes the flight distance prediction function to analyze the flight distance of dust with different particle sizes in different time series, which can accurately quantify the propagation characteristics of dust in the port area, providing a reference for formulating targeted dust control measures.

[0010] Optionally, the establishment of the port area dust characteristic analysis system, based on the port area multi-dimensional detection information database and the port area dust characteristic analysis system, to obtain the data characteristic analysis results, dust movement characteristic analysis results, and dust spatial distribution characteristic analysis results of port area dust includes: dividing the monitoring area according to the monitoring area grid division mechanism and the dust movement characteristic analysis results, and obtaining the monitoring small grid of the monitoring area; introducing a spatial attenuation mechanism and a Gaussian diffusion model; correcting the dust concentration at the center position of the monitoring small grid based on the spatial attenuation mechanism, the Gaussian diffusion model, and the port area multi-dimensional detection information database to obtain the dust concentration information at the center position of different monitoring small grids; and obtaining the dust spatial distribution analysis results based on the dust concentration information at the center position and the port area multi-dimensional detection information database. This invention obtains the dust spatial distribution analysis results based on the dust concentration information at the center position and the port area multi-dimensional detection information database, which can comprehensively utilize various monitoring data information and provide a more comprehensive understanding of the influencing factors and formation mechanisms of port area dust spatial distribution.

[0011] Optionally, establishing a port area dust pollution sensitive factor prediction system and using the port area dust pollution sensitive factor prediction system and the port area multi-dimensional detection information database to obtain the interaction relationship between different sensitive factors includes: setting sensitive factors for port area dust pollution in the port area dust pollution sensitive factor prediction system, wherein the sensitive factors include meteorological conditions, loading and unloading operation methods, and material characteristics of the port area monitoring area; and establishing a sensitive factor contribution evaluation function in the port area dust pollution sensitive factor prediction system. The sensitive factor prediction system and contribution evaluation function of the present invention can improve the accuracy of port area dust pollution prediction results, help to more accurately predict the occurrence time, location, and degree of dust pollution, and provide information basis for the formulation of prevention and control measures.

[0012] Optionally, establishing a port area dust pollution sensitive factor prediction system and using the port area dust pollution sensitive factor prediction system and the port area multi-dimensional detection information database to obtain the interaction relationship between different sensitive factors includes: obtaining data information of different sensitive factors based on the sensitive factors and the port area multi-dimensional detection information; analyzing the contribution degree of different sensitive factors to port area dust pollution using the sensitive factor contribution degree evaluation function; and obtaining the interaction relationship between different sensitive factors based on the different sensitive factor data information and the contribution degree. After analyzing the interaction relationship between different sensitive factors, this invention can rationally allocate prevention and control resources according to the contribution degree and mutual influence of each factor, thereby improving the utilization efficiency of port resources.

[0013] Secondly, to efficiently execute the port area dust pollution characteristic analysis and sensitive factor identification method provided by this invention, this invention also provides a port area dust pollution characteristic analysis and sensitive factor identification system, including a processor, an input device, an output device, and a memory. The processor, input device, output device, and memory are interconnected. The memory stores a computer program, which includes program instructions. The processor is configured to call the program instructions to execute the port area dust pollution characteristic analysis and sensitive factor identification method as described in the first aspect of this invention. The port area dust pollution characteristic analysis and sensitive factor identification system of this invention has a compact structure and stable performance, and can stably execute the port area dust pollution characteristic analysis and sensitive factor identification method provided by this invention, thereby improving the overall applicability and practical application capability of this invention. Attached Figure Description

[0014] Figure 1 This is a flowchart of the port area dust pollution characteristic analysis and sensitive factor identification method of the present invention;

[0015] Figure 2 This is a structural diagram of the port area dust pollution characteristic analysis and sensitive factor identification system of the present invention. Detailed Implementation

[0016] See Figure 1 To achieve accurate acquisition of port monitoring data, effective analysis of port area dust pollution characteristics and sensitive factors, and multi-level identification and analysis of port area dust pollution characteristics and sensitive factors, thus providing technical support for the sustainable development and stable operation of ports, this invention provides a method for port area dust pollution characteristic analysis and sensitive factor identification. The method includes the following steps:

[0017] S1. Obtain multi-source detection information of the port area through the port area data detection module, process the multi-source detection information of the port area using the data optimization processing mechanism in the port area data detection module, and obtain a multi-dimensional detection information database of the port area. The specific implementation steps and contents are as follows.

[0018] The port area data detection module obtains multi-source detection information for the port area.

[0019] In this embodiment, the collection of multi-source detection information in the port area is mainly carried out based on the port area data detection module.

[0020] I. Port Area Meteorological Data Collection. The port area data detection module includes various meteorological monitoring devices, which collect meteorological parameters such as wind speed, wind direction, temperature, humidity, and air pressure in the port area. The data monitoring stations and data collection frequency of the meteorological monitoring equipment can be flexibly adjusted according to the actual monitoring needs of the port area. It can be set to collect data once per minute or once per hour to accurately capture dynamic data and information on the evolution of meteorological conditions.

[0021] II. Port Area Operation Information Acquisition. The port area data detection module needs to establish a close cooperative relationship with the port area operation management department to comprehensively acquire information related to port loading and unloading operations. This mainly covers key elements such as port operation type (e.g., specific categories like coal loading and unloading, ore loading and unloading), port operation time, and port operation intensity (e.g., hourly cargo handling volume). The collection method for port operation information can also combine the operation management system with manually recorded data, which helps ensure the completeness and accuracy of port operation information.

[0022] III. Obtaining Port Area Dust Concentration Monitoring Information. The port area data detection module includes various sensor devices and equipment. Appropriately installing dust concentration monitoring devices and sensors in key areas of the port, such as storage yards, loading and unloading areas, and transport roads, facilitates real-time dynamic monitoring of dust concentration in various working or functional areas of the port. Simultaneously, all devices and sensors in the port area data detection module should support wired or wireless data transmission methods to ensure timely and accurate transmission of port dust concentration monitoring information.

[0023] In this embodiment, data was collected from three dimensions: port meteorological data, port operation information, and port dust concentration monitoring information, comprehensively covering various factors affecting dust pollution in the port area. Meteorological conditions directly influence dust diffusion and propagation, operation information determines the source and amount of dust generation, and dust concentration monitoring information directly reflects the actual situation of dust pollution. By integrating the above multi-dimensional monitoring information, a data foundation can be provided for the analysis of port dust pollution characteristics and the identification of sensitive factors, making the analysis results more comprehensive and accurate. Simultaneously, data from different sources complement and verify each other, improving the reliability and credibility of multi-source detection information in the port area, and helping to more accurately analyze dust pollution characteristics and identify sensitive factors of dust pollution.

[0024] The data optimization and processing mechanism in the port area data detection module is used to process the multi-source detection information of the port area and obtain a multi-dimensional detection information database of the port area.

[0025] When using the port area data monitoring module for data monitoring, the main basis for monitoring data changes at each monitoring point over time is the actual conditions of the port area and the relative spatial relationships between different monitoring equipment sites. However, due to the complexity and dynamism of the actual port environment and the data monitoring environment, the automated monitoring equipment in the port area data monitoring module inevitably produces data monitoring errors. Therefore, multi-source monitoring information from the port area cannot be directly applied to aspects such as port dust pollution characteristic analysis, sensitive factor identification, and port dust pollution source tracing and port management scheme design.

[0026] Therefore, it is necessary to detect and correct outliers in the multi-source detection information of the port area. This can effectively improve the quality of the multi-source detection information of the port area and provide a solid data foundation for subsequent port area data analysis and analysis model establishment. The relevant processing content of the multi-source detection information of the port area is as follows:

[0027] First, the multi-source detection information of the port area is analyzed based on the data optimization processing mechanism to obtain the difference sequence information of the multi-source detection information of the port area.

[0028] In this embodiment, the data optimization processing mechanism uses a statistical method based on the characteristics of normal distribution to detect outliers in the multi-source detection information of the port area. The above method mainly determines whether different time collection points (time series) in the multi-source detection information of the port area belong to outliers based on the degree of deviation between the actual values ​​of each time sample collection point (time series) and the mean and root mean square of all data.

[0029] The time series of multi-source testing information in Hong Kong can be represented as follows:

[0030] ,

[0031] in, This indicates the time sequence of testing information from multiple sources in Hong Kong. Column set, This indicates the time collection points (time series) of multi-source testing information in the port area. express The first in the time series Data sampling (monitoring) points, express The first in the time series The data monitoring value corresponding to each data sampling (monitoring) point.

[0032] When using statistical methods based on normal distribution characteristics to detect outliers in time series data of multi-source detection information in port areas, the first step is to calculate the difference sequence information of the multi-source detection information in port areas. The above difference sequence information needs to satisfy the following relationship:

[0033] ,

[0034] in, This indicates the first value in the multi-source testing information difference sequence of the Hong Kong area. One element, express The first in the time series The data monitoring value corresponding to each data sampling (monitoring) point. express The first in the time series The data monitoring value corresponding to each data sampling (monitoring) point. express The first in the time series The data monitoring value corresponding to each data sampling (monitoring) point.

[0035] The calculation of the difference sequence fully considers the correlation between the current sampling point and the two adjacent sampling points. By quantifying the degree of relative change, it can more accurately capture abnormal fluctuations in the multi-source detection information of the port area. When the value of a certain time series sampling point in the multi-source detection information of the port area deviates significantly from the value range of the other sampling points, the sampling point is determined to be an abnormal data point, and its value is considered an abnormal data value.

[0036] In the port area data detection module, for all sampling points, difference sequence calculations are performed on the multi-source detection information of the port area according to the aforementioned difference sequence information formula. The calculated difference sequence contains... Based on the difference sequence elements, the mean and root mean square of the difference sequence of multi-source detection information in the port area are further calculated, providing key basis for subsequent data analysis and decision-making.

[0037] The mean of the difference sequence of multi-source testing information in the Hong Kong area satisfies the following relationship:

[0038] ,

[0039] in, This indicates the difference sequence of multi-source testing information in Hong Kong. The mean, This indicates the first value in the multi-source testing information difference sequence of the Hong Kong area. One element, This indicates the number of data sampling (monitoring) points.

[0040] The root mean square of the difference sequence of multi-source detection information in the port area, based on the mean analysis results, satisfies the following relationship:

[0041] ,

[0042] in, This indicates the difference sequence of multi-source testing information in Hong Kong. The root mean square, This indicates the difference sequence of multi-source testing information in Hong Kong. The mean, This indicates the first value in the multi-source testing information difference sequence of the Hong Kong area. One element, This indicates the number of data sampling (monitoring) points.

[0043] Then, based on the data optimization processing mechanism and the difference sequence information, the degree of information deviation of the difference sequence information is analyzed.

[0044] In this embodiment, the root mean square of the difference sequence of multi-source detection information in the port area is used as a quantitative reference standard to measure the degree of data deviation. Based on the obtained mean analysis results, by constructing a suitable mathematical model or using relevant statistical methods, the degree of deviation of the difference sequence of multi-source detection information in the port area relative to the mean is accurately calculated, providing a solid theoretical foundation for in-depth research on the inherent laws and anomaly detection of port area data.

[0045] The above deviation analysis results satisfy the following relationship:

[0046] ,

[0047] in, The function representing the degree of deviation analysis, This indicates the first value in the multi-source testing information difference sequence of the Hong Kong area. One element, This indicates the difference sequence of multi-source testing information in Hong Kong. The mean, This indicates the difference sequence of multi-source testing information in Hong Kong. The root mean square, This indicates that the deviation analysis function is 0, and the corresponding... abnormal, The deviation analysis function is set to 1, indicating the corresponding... normal.

[0048] Further analysis of the above deviation analysis results:

[0049] when At that time, the judgment of the first Difference between individual detection information The deviation from the mean is large (exceeding) This indicates that the value is an outlier or a significant deviation.

[0050] when At that time, the judgment of the first Difference between individual detection information The deviation from the mean is small (in Within the range), it is determined that it is within the normal fluctuation range of the data.

[0051] In this embodiment, the data optimization processing mechanism can effectively reduce the deviation of multi-source detection information in the port area, which helps to identify and remove noisy data in the future, and improve the accuracy and consistency of multi-dimensional detection data in the port area. Analyzing the detection data of different detection devices in the port area data detection module can eliminate the limitations of a single data source, reduce data deviation caused by equipment failure or environmental interference, and provide information basis for dust pollution monitoring and scientific operation in the port area.

[0052] Next, the multi-source detection information in the port area is corrected based on the degree of information deviation to obtain the corrected multi-source detection information.

[0053] In this embodiment, the multi-source detection information of the port area is fused and corrected based on the dynamic deviation assessment results. After outlier detection of the multi-source detection information of the port area is completed, the deviation analysis function is used... Based on the output results, an adaptive data correction framework was constructed to dynamically calibrate and optimize the consistency of multi-source detection information in the port area. The specific steps are as follows:

[0054] The first step is to quantify the degree of deviation and classify the anomalies.

[0055] Analysis function based on deviation Judgment No. Data sampling (monitoring) points Deviation state;

[0056] when If it is, then it is judged as an outlier. The time was determined to be a normal fluctuation, which is consistent with the statistical rules of the data.

[0057] Based on the deviation level classification results, an anomaly impact weight matrix is ​​established to quantify the interference intensity of outliers on the overall data distribution.

[0058] The second step is to perform multi-strategy adaptive correction.

[0059] Abnormal data correction: Abnormal values ​​can be replaced with the mean of the nearest time window, the mean of the same period in history, or the results of multi-source cross-validation to further ensure the continuity of port area testing data; corresponding missing values ​​(abnormal values) can also be reconstructed based on time series correlation using Lagrange interpolation, spline interpolation, or Kalman filtering algorithms; and abnormal labels can be added to missing values ​​(abnormal values) to retain the original abnormal records for traceability analysis.

[0060] Normal data retention: Data can be retained accordingly. The data sampling (monitoring) points can be used to reduce random fluctuations through sliding window smoothing or moving average algorithms, further improving the stability of port area detection data.

[0061] The third step is to perform data continuity checks and consistency constraints.

[0062] The embodiment employs time-dimensional constraints to ensure that the corrected data sequence meets temporal continuity requirements. For example, the difference between adjacent time points does not exceed a threshold. Spatial-dimensional constraints can also be applied, such as verifying the spatial correlation of multiple sensors to ensure consistency of detection data in adjacent areas, thus avoiding logical conflicts caused by isolated corrections. Furthermore, business logic verification can be performed, combining port operation rules such as the matching relationship between cargo throughput and equipment load to verify the rationality of the correction results.

[0063] The fourth step is to evaluate the quality of the corrected data.

[0064] The embodiment introduces a statistical indicator verification method to compare and analyze the statistical characteristics such as mean, variance, and skewness of the multi-source detection information in the port area before and after the correction, so as to further ensure that the corrected multi-source detection information in the port area has not introduced systematic bias. A model fitting test method can also be used to input the corrected multi-source detection information in the port area into models such as port flow prediction and equipment health assessment to verify the improvement of the correction effect on the model accuracy.

[0065] Based on the above correction process, the corrected multi-source detection information in the embodiments was obtained.

[0066] Furthermore, the data optimization processing mechanism is configured with an information state function-observation function and an information iterative optimization model.

[0067] The time-series multi-source detection information set can be obtained based on the corrected multi-source detection information. Based on the implementation details, the corrected time-series multi-source detection information can be structurally represented, and the time-series multi-source detection information set can be constructed by integrating the corrected multi-source detection information.

[0068] Based on the above implementation details, a unified framework is used to describe the spatiotemporal distribution characteristics of the corrected port area monitoring data. The specific definition of the time series-multi-source detection information set is as follows:

[0069] ,

[0070] in, This represents the time series set of corrected multi-source detection information. This indicates the time collection points (time series) of the multi-source testing information in the port area after the correction. Indicates after correction The first in the time series Data sampling (monitoring) points, Indicates after correction The first in the time series The data monitoring value corresponding to each data sampling (monitoring) point.

[0071] Based on the aforementioned time series-multi-source detection information set, an information state function-observation function was further constructed.

[0072] To analyze the dynamic evolution and data observation characteristics of the port area data detection module, a joint model of state equation and observation equation was constructed based on the corrected time series-multi-source detection information set. In this embodiment, the state space model is optimized based on the operating mechanism of the port area data detection module to describe the mapping relationship between the implicit state of the port area detection information and the observed values. The specific definitions are as follows:

[0073] The above mapping relationship is analyzed through the information state function-observation function, i.e., the state equation and the observation equation, and satisfies the following relationship:

[0074] ,

[0075] in, This represents the state equation for the modified multi-source detection information. Represents the state transition matrix. Indicates the corrected number Predicted estimates for each sampling point This represents the control matrix of the port area data detection module. This represents the noise figure of the port area data detection module. This represents the observation equation after the correction of multi-source detection information. This represents the observation matrix after correction of multi-source detection information. Indicates the corrected number Predicted estimates for each sampling point This represents the observation noise figure of the multi-source detection information after correction.

[0076] The state equation in the information state function-observation function can describe the implicit state dynamic evolution of the port area data detection module and reflect the state transition law within the module.

[0077] The state variables of the sampling points at the current moment (such as equipment health status and cargo flow status) are latent variables and cannot be directly observed.

[0078] The predicted state value of the sampling point at the previous time step can be mapped to the current time step through the state transition matrix.

[0079] The state transition matrix can characterize the temporal dependence of module states (such as equipment degradation rate, cargo flow growth trend).

[0080] The control matrix can reflect the impact of external inputs (such as environmental disturbances and human scheduling) on ​​state evolution.

[0081] The process noise figure can describe random disturbances (such as sudden equipment failures or sudden environmental changes) during state transitions.

[0082] The observation equation in the information state function-observation function can describe the relationship between the observed values ​​and the implicit states of the port area data detection module, further reflecting the actual measurement characteristics of the multi-source detection data in the port area.

[0083] The predicted state value of the sampling point at the current moment is mapped to the observation space through the observation matrix.

[0084] The observation matrix can characterize the linear or nonlinear relationship between the latent state and the observed value (such as sensor sensitivity and data fusion weights).

[0085] The observation noise figure can describe random errors in the measurement process (such as sensor noise and data transmission interference).

[0086] The information state function-observation function can capture the time-varying characteristics of the port area monitoring system, avoiding the limitations of static models for complex dynamic scenarios; at the same time, it can support joint modeling of multi-source detection data, and improve the reliability of module observation data values ​​by fusing information from different detection sources through matrix fusion.

[0087] Furthermore, the aforementioned data optimization processing mechanism analyzes the corrected multi-source detection information based on the information state function-observation function to obtain the state analysis results and observation analysis results of the multi-source detection information.

[0088] The example extracts implicit state features and observation patterns from the corrected multi-source detection information. Based on the information state function and observation function, a two-layer analysis framework is set up to dynamically analyze the port area monitoring data and output state analysis results and observation analysis results to support the precision of port area operation decisions. The specific implementation path is as follows:

[0089] The two-layer analysis framework in this embodiment includes:

[0090] The first layer is the state analysis layer, based on state equations. Implicit state reasoning on the corrected multi-source detection data can reveal the dynamic patterns within the module and output the corresponding state analysis results, including but not limited to the temporal evolution trajectory of implicit features such as equipment health status (e.g., degradation trend), cargo flow status (e.g., fluctuation pattern), and environmental risk status (e.g., pollution diffusion trend).

[0091] The second layer of observation and analysis is based on the observation equation. The system performs observation quality assessment on the corrected multi-source detection data, verifies the matching between the data and the system state, and outputs the corresponding observation analysis results. These mainly include the fusion consistency of multi-source data (such as the correlation between sensor data and video data), observation noise distribution (such as the sensor error range), and identification of abnormal observations (such as outliers caused by noise interference).

[0092] An information iterative optimization model was established based on the aforementioned time series-multi-source detection information set and information state function-observation function.

[0093] To achieve dynamic iterative optimization of the corrected multi-source detection information in the port area, the embodiment constructs an information iterative optimization model based on the time series-multi-source detection information set and the information state function-observation function (state equation and observation equation), thereby obtaining a port area detection information iterative optimization model driven by state-observation. This model quantifies the deviation of the detection information and combines the state estimation error and observation noise characteristics to iteratively correct the state estimation value of the sampling point at the current time, outputting a more accurate state optimization result.

[0094] The above iterative optimization model satisfies the following relationship:

[0095] ,

[0096] in, Indicates the result of iterative optimization , Indicates the corrected number Predicted estimates for each sampling point express The corresponding state estimation error covariance matrix, Represents the transpose of the observation matrix. This represents the observation matrix after correction of multi-source detection information. express The inverse matrix, The covariance matrix representing the observation noise. This represents the observation equation after the correction of multi-source detection information. This represents the state equation for the modified multi-source detection information.

[0097] The output of the iteratively optimized state estimate further yielded the optimal data information for the port area data detection module.

[0098] Input variables are based on the sampling points of the previous time step. The predicted estimate is obtained by using the state transition law (state equation) to obtain the prior state at the current moment.

[0099] The state estimation error covariance matrix can represent the uncertainty of the state estimation of the sampling point at the current moment, such as the uncertainty of the equipment degradation rate and the discreteness of cargo flow fluctuations.

[0100] The observation matrix can map the hidden state to the observation space, such as sensor sensitivity and data fusion weights.

[0101] The transpose of the observation matrix is ​​mainly used for matrix operations during the optimization process.

[0102] The covariance matrix of observation noise can represent the distribution of random errors in the observations, such as the variance of sensor noise and the covariance of data transmission interference.

[0103] The aforementioned iterative optimization model can quantify the degree of deviation between the observed value prediction and the state prediction, reflecting the source of error in the current estimation. It combines the state estimation error and the observation noise characteristics to dynamically adjust the optimization weights. When the state uncertainty is high, it relies more on the observed data; when the observation noise is large, it reduces the observation weights. By combining the state estimation error covariance matrix and the observation matrix, the deviation term is mapped to the state space, thereby achieving iterative correction of the data state estimation values.

[0104] Finally, the aforementioned data optimization and processing mechanism combines the state analysis results, observation analysis results, and information iterative optimization model to iteratively optimize and integrate the corrected multi-source detection information to obtain a multi-dimensional detection information database for the port area.

[0105] To achieve deep integration and dynamic optimization of the corrected multi-source detection information in the port area, the embodiment combines the information state function-observation function (state equation and observation equation) and the information iterative optimization model to perform multi-stage optimization processing on the multi-source detection information in the port area. This multi-stage optimization processing mechanism gradually corrects the state estimation and error covariance matrix through a prediction-update iterative loop, and finally generates a multi-dimensional port area detection information database.

[0106] The above prediction and update steps can be repeated multiple times based on data collection needs until the convergence condition is met or the maximum number of iterations is reached. In an optional embodiment, iteration convergence is determined when the update magnitude of the state estimate is less than a preset threshold; if the number of iterations reaches a preset upper limit... The iteration is forcibly terminated; after termination, the optimal data sampling point is obtained, and the corresponding port area multi-dimensional detection information database can be obtained. The time series-port area multi-dimensional detection information database set satisfies the following relationship:

[0107] ,

[0108] in, This indicates the Hong Kong area's multi-dimensional testing information database. Time series information collection, This indicates the Hong Kong area's multi-dimensional testing information database. The information subset of the first monitoring sampling point in the time series. This indicates the Hong Kong area's multi-dimensional testing information database. The information subset of the second monitoring sampling point in the time series. This indicates the Hong Kong area's multi-dimensional testing information database. The first in the time series A subset of information from each monitoring sampling point.

[0109] Furthermore, the optimization processing mechanism and implementation method for multi-source detection information in the port area in this embodiment are merely optional conditions of the present invention. In other embodiments, the optimization processing mechanism and implementation method for multi-source detection information in the port area can be replaced according to the data collection conditions of the port area data detection module and the actual situation of the detection information. The optimization processing mechanism and implementation method can be flexibly replaced according to the hardware conditions, data characteristics and business needs of the port area data detection module, which is conducive to adapting to the differentiated needs of different port area scenarios.

[0110] S2. Establish a port area dust characteristic analysis system. Based on the port area multi-dimensional detection information database and the port area dust characteristic analysis system, obtain the data characteristic analysis results, dust movement characteristic analysis results, and dust spatial distribution characteristic analysis results of port area dust. The specific implementation steps and contents are as follows:

[0111] First, a dust characteristic analysis system for the port area was established based on the multi-dimensional detection information database of the port area and the historical monitoring data of dust pollution in the port area. The aforementioned dust characteristic analysis system for the port area mainly includes a data characteristic analysis layer, a dust movement characteristic analysis layer, and a dust spatial distribution characteristic analysis layer.

[0112] A layered and multi-dimensional analysis system for port area dust characteristics was constructed. Based on the multi-dimensional detection information database and historical dust pollution monitoring data of the port area, a dust characteristic analysis system for the port area was built. It mainly achieves refined characterization and source analysis of port area dust pollution through the deep integration of data-driven and physical mechanisms. With data characteristics, motion characteristics and spatial characteristics as the core, the system mainly sets up a data characteristic analysis layer, a dust motion characteristic analysis layer and a dust spatial distribution characteristic analysis layer, which further supports the dynamic monitoring and treatment plan formulation of port area dust pollution.

[0113] I. Based on the multi-dimensional detection information database of the port area and the data feature analysis layer in the port area dust feature analysis system, the data feature analysis results of port area dust are obtained.

[0114] In this embodiment, a multi-dimensional detection information database and data feature analysis layer of the port area are combined. Through a three-stage process of statistical parameter quantification, time-series dynamic calculation, and feature association mapping, the standardized characterization and dynamic tracking of dust pollution characteristic data in the port area are realized. The specific implementation process is as follows:

[0115] In the data feature analysis layer, data statistical parameters are set based on the time-series data of the port area's multi-dimensional detection information database. In this embodiment, the above-mentioned data statistical parameters mainly include the mean. Maximum value Minimum value Standard deviation Based on the above statistical parameters, the concentration trend, fluctuation range and dispersion of dust pollution are further quantified.

[0116] mean It reflects the long-term exposure level of dust pollution in the port area and can be used to assess the regional environmental baseline load.

[0117] Maximum value It can identify instantaneous high pollution events (such as sudden dust storms) and can be used to trigger emergency response mechanisms and early warning signals.

[0118] Minimum value It can reflect the concentration of dust in the environment and is used to distinguish between natural background values ​​and contributions from anthropogenic pollution.

[0119] Standard deviation It can characterize the stability of dust concentration fluctuations and help identify drastic concentration changes caused by equipment malfunctions or operational abnormalities.

[0120] The average, maximum, minimum, and standard deviation of port area dust in different time series are calculated by using the data feature analysis layer and the port area multi-dimensional detection information database. Then, the data feature analysis results of port area dust are obtained by combining the average, maximum, minimum, and standard deviation.

[0121] The embodiment introduces a time-series dynamic calculation method, which uses a fixed time window (such as 1 hour, 12 hours, 24 hours) to perform sliding statistics on dust concentration data, generating a time-series statistical parameter sequence. ,in For time step.

[0122] Then, data statistical parameters are fused, and the statistical parameters are spatiotemporally aligned with the associated data to construct a joint matrix of data statistical parameters, satisfying the following relationship:

[0123] ,

[0124] in, This indicates the temporal information contained in the data feature analysis layer. This indicates the change of the mean over a time series. This represents temporal information indicating how the maximum value evolves over time. This represents the temporal information of the minimum value in the time dimension. It represents the time-series information of the standard deviation over a time series.

[0125] Based on the time-series information from the data feature analysis layer, a method combining mean and standard deviation analysis can be used to determine the warning threshold. This threshold can effectively identify abnormal upward trends in pollution concentration. Simultaneously, by combining maximum value data and spatial distribution characteristics, the operating equipment or cargo type corresponding to high-concentration areas can be accurately located. Finally, based on the port area's multi-dimensional detection information database and the data feature analysis layer within the port area dust feature analysis system, the data feature analysis results for port area dust can be obtained.

[0126] 2. Based on the multi-dimensional detection information database of the port area and the dust movement characteristic analysis layer in the dust characteristic analysis system of the port area, carry out analysis tasks to obtain the dust movement characteristic analysis results of the port area dust.

[0127] Considering the actual working needs of the port area, the specific conditions of loading and unloading operations, and the characteristics of the acquired data, this embodiment divides the port area into different functional areas, which will help with the subsequent monitoring and effective analysis of dust pollution in different functional areas of the port area.

[0128] In actual port environments, dust piles are composed of a mixture of particles of various sizes. When air flows, these dust particles are agitated and lifted into the air. This movement is not the motion of individual particles, but rather the result of the collective motion of a group of particles of different sizes. Extensive experimental research and theoretical analysis have shown that the distance dust travels in the air is closely related to factors such as dust particle size, airflow velocity, and air density. Specific relationships exist between the distances dust travels of different particle sizes. In the dust motion characteristic analysis layer, prediction functions for the distances dust travels of different particle sizes are set based on factors such as dust particle size, airflow velocity, and air density. These prediction functions satisfy the following relationship:

[0129] ,

[0130] in, This indicates the distance that dust particles of different sizes can travel. This represents the constant term in the function representing the distance dust travels. This indicates the highest dust monitoring point in different functional areas of the port area. This indicates the lowest monitoring point for dust data in different functional areas of the port area. This represents a constant related to airflow characteristics and dust properties. This indicates the viscosity of airflow within different functional areas of the port area. This indicates the average wind speed in different functional areas of the port during the monitoring period. This indicates the average particle size of dust particles in different functional areas of the port area. The index represents the reduction in dust particle size in the port area. This indicates the average air density in different functional areas of the port during the testing period. This indicates the average dust density in different functional areas of the port during the monitoring period. It represents the acceleration due to gravity.

[0131] The distance at which dust particles of different sizes can travel is a reference indicator for measuring how far dust particles can spread in different functional areas of the port area under the influence of various factors.

[0132] The constant term in the dust dispersion distance function can be determined through experimental data and theoretical analysis, and it reflects the comprehensive influence of some inherent characteristics in the dust dispersion process.

[0133] The highest dust monitoring point in different functional areas of the port area represents the maximum height of dust monitoring location in different functional areas, reflecting the highest distribution position of dust in the vertical direction.

[0134] The lowest monitoring point for dust data in different functional areas of the port area, i.e. the minimum height of dust monitoring location in different functional areas, reflects the lowest distribution location of dust in the vertical direction.

[0135] The airflow kinematic viscosity, measured in square millimeters per second, reflects the viscous properties of the airflow and significantly influences the motion of dust particles within it. The above formula uses an exponential function to describe the effect of airflow kinematic viscosity on the distance dust travels. This constant, representing the relationship between airflow characteristics and dust properties, can be measured and adjusted using experimental data. The kinematic viscosity of airflow in different functional areas of the port area describes the magnitude of internal friction during airflow and has a significant impact on dust dispersion and diffusion.

[0136] The airflow viscosity in different functional areas of the port area describes the magnitude of internal friction during airflow and has a significant impact on dust dispersion and diffusion.

[0137] The average wind speed in different functional areas of the port during the detection period. Wind speed is one of the key factors affecting the distance dust flies. Higher wind speeds make it easier for dust to be blown to farther places.

[0138] The average particle size of dust particles in different functional areas of the port area refers to the distance that dust particles with different average particle sizes can fly due to differences in their own gravity and air resistance.

[0139] The reduction index of dust particle size in port areas reflects the variation of dust particle size with distance and other factors during the flying process, which helps to understand and predict the diffusion characteristics of dust.

[0140] The average air density in different functional areas of the port during the detection period. Changes in air density affect the suspension and diffusion of dust in the air.

[0141] The difference between the average density of sand particles in different functional areas of the port during the testing period and the density of air will affect the stress on dust particles when they are airborne.

[0142] Gravitational acceleration is a fundamental physical constant that causes dust to be subjected to a downward force of gravity, thus affecting the distance the dust travels.

[0143] Furthermore, the dust flight distance at the center of the dust source in different functional areas is analyzed using a dust flight distance prediction function with different particle sizes. The relevant implementation steps and contents are as follows:

[0144] First, determine the location of the dust source center in different functional areas of the port area. :

[0145] Then, based on the work content of different functional areas of the port area and the materials piled up in the port area, the dust generation intensity at the dust source center was determined. The average particle size of dust particles at the center of dust sources in different functional areas, and the material correction coefficients corresponding to different functional areas. ;

[0146] Next, the average particle size of dust particles at the center of the dust source in different functional areas was analyzed. Prediction functions for the flight distance of dust particles of different sizes and the location of the dust source center in different functional areas. This allows for the analysis of the location of dust source centers in different functional areas of the port area. The distance at which dust particles fly;

[0147] Finally, the relevant data is input into the flight distance prediction function. among;

[0148] This yielded the locations of dust source centers in different functional areas of the port area. The distance the dust particles travel, and satisfies the following expression:

[0149] ,

[0150] in, This indicates the distance dust particles travel from the center of the dust source in different functional areas of the port area. This represents the constant term in the function representing the distance dust travels. This indicates the highest monitoring point representing the center of dust sources in different functional areas of the port area. This indicates the lowest monitoring point representing the center of dust sources in different functional areas of the port area. This represents a constant related to airflow characteristics and dust properties. This indicates the airflow viscosity at the center of the dust source in different functional areas of the port area. This indicates the average wind speed at the center of the dust source in different functional areas of the port during the detection period. This indicates the average particle size of dust particles at the center of dust sources in different functional areas of the port area. The index represents the reduction in dust particle size in the port area. This indicates the average air density at the center of dust sources in different functional areas of the port area. This indicates the average dust density at the center of the dust source in different functional areas of the port area during the detection period. It represents the acceleration due to gravity.

[0151] By using prediction functions for dust flight distances of different particle sizes, it is possible to conduct in-depth analysis of the flight distances of dust with different particle sizes in different functional areas of the port (including storage yards, loading and unloading areas, and transport roads) under different time series. In other words, different time series information can be combined with prediction functions to calculate the flight distances of dust with different average particle sizes in different functional areas within a specific time period.

[0152] Based on this, it is necessary to fully integrate the data on the flying distance of dust particles in different functional areas of the port area, as well as the information in the multi-dimensional detection information database of the port area, including but not limited to dust concentration monitoring data, meteorological data, and operational activity data. Through scientific and reasonable analysis models and algorithms, the motion characteristics of dust can be comprehensively analyzed to obtain accurate and reliable dust motion characteristic analysis results.

[0153] Meanwhile, based on the analysis results of dust movement characteristics, this study explores the intrinsic relationship between dust flight distance and different functional areas of the port (stockyard, loading and unloading area, transportation roads, etc.). In the implementation examples, statistical analysis, spatial analysis and other methods are used to quantitatively analyze the movement patterns of dust in different functional areas. Combined with the characteristics of port operations and the characteristics of stockpiled materials, the distribution location and operation area of ​​the main pollution sources in the port are determined, thus providing a theoretical basis for the analysis of dust pollution in the port and the formulation of dust pollution management plans.

[0154] III. Based on the multi-dimensional detection information database of the port area and the dust feature analysis system of the port area, the spatial distribution characteristics of dust in the port area were analyzed.

[0155] Step 1. In the dust spatial distribution characteristic analysis layer, a dust source center dust flight distance prediction function and a monitoring area grid division mechanism were configured.

[0156] In the dust spatial distribution characteristic analysis layer, a dust flying distance prediction function of the dust source center was further constructed, and a grid division mechanism for the functional areas of the port area was designed. In combination with the above implementation steps, this embodiment has divided the port area into different functional areas according to the actual working scenario and operation characteristics of the port area, laying the foundation for subsequent analysis.

[0157] The second step involves dividing the monitoring area into smaller monitoring grids based on the grid division mechanism and the dust movement characteristics analysis results (analysis results of dust particle flight distance in different functional areas).

[0158] Based on the grid division mechanism of the port area's functional zones and combined with the dust movement characteristic analysis results (covering the dust particle flight distance analysis results of different functional zones), the monitoring area is further divided to obtain unit small grids for each functional zone. That is, different functional zones can be divided into several small grids, each serving as an independent analysis unit, in order to explore the spatial distribution patterns of dust at different locations within the functional zone.

[0159] In this embodiment, the division parameters of the functional area small grid are set based on the dust movement characteristic analysis results.

[0160] The port area functional zones are pre-defined as rectangles with length 'a' and width 'b'. The port area functional zones are then divided into... A square (rectangular) grid is formed, and the side length of a single unit grid satisfies the following relationship: , Furthermore, the center coordinates of a single unit grid can be expressed as: ,in .

[0161] Step 3. Locate the center coordinates of each unit grid. By combining the dust generation intensity, average particle size, material correction coefficient, and dust source center distance prediction function at different small grid center locations, the coordinates of the center of each unit small grid in different functional areas of the port area can be calculated. Knowing the corresponding distance at which dust particles fly is helpful for fully understanding the spatial distribution characteristics of dust in different functional areas of the port area.

[0162] Step 4. Introduce the spatial attenuation mechanism and Gaussian diffusion model. Based on the above spatial attenuation mechanism, Gaussian diffusion model and port area multi-dimensional detection information database, analyze the dust concentration at the center of the small grid to obtain dust concentration information at the center of different monitoring small grids.

[0163] To accurately analyze the dust concentration distribution in different functional areas of the port area, a spatial attenuation mechanism and a Gaussian diffusion model are introduced in this embodiment. Combining the spatial attenuation mechanism, the Gaussian diffusion model, and the port area's multi-dimensional detection information database, the dust concentration at the center of each unit's small grid is analyzed. By comprehensively analyzing multi-dimensional factors such as meteorological conditions, topography, and operational activities within the port area, and using relevant mathematical models and algorithms, the dust concentration information at the center of different unit's small grid can be calculated.

[0164] Based on the spatial decay principle and Gaussian diffusion model, the center position of each unit small grid is... The dust particle propagation distance is corrected. In this embodiment, it is considered that dust is affected by various factors during its spatial propagation, such as air turbulence and particle settling. These factors all cause the dust concentration to gradually decrease with increasing distance. Therefore, the dust particle propagation distance is corrected, and the dust diffusion process is simulated using a Gaussian diffusion model, which can accurately obtain the center position of each unit small grid. Dust concentration information.

[0165] Center position of each unit's small grid within different functional areas of the port area The dust concentration needs to satisfy the following relationship:

[0166] ,

[0167] in, Expressing the center position of the small grid unit dust concentration, This represents the average dust density at the center of a unit small grid during the detection time. This indicates the dust source generation intensity at the center of a unit small grid. The material correction factor represents the center position of a unit small grid. This represents the ability of dust particles at the center of a unit small grid to diffuse in the x-direction. This represents the ability of dust particles at the center of a unit small grid to diffuse in the x-direction. Indicates the location of the dust source center in different functional areas. This indicates the center position of different small grid units within a functional area.

[0168] The diffusion capacity of dust at the center of the aforementioned small grid in the x-direction The following relationship must be satisfied:

[0169] ,

[0170] in, This represents the ability of dust particles at the center of a unit small grid to diffuse in the x-direction. The material correction factor represents the center position of a unit small grid. This represents the diffusion ratio of dust in the x-direction. This indicates the distance dust particles of different sizes can travel from the center of the dust source in different functional areas. This indicates the average particle size of dust particles at the center of the dust source in different functional areas.

[0171] The ability of dust particles at the center of a small grid unit to diffuse in the y-direction. The following relationship must be satisfied:

[0172] ,

[0173] in, This indicates the ability of dust particles at the center of a unit small grid to diffuse in the y-direction. The material correction factor represents the center position of a unit small grid. This represents the diffusion ratio of dust in the y-direction. This indicates the distance dust particles of different sizes can travel from the center of the dust source in different functional areas. This indicates the average particle size of dust particles at the center of the dust source in different functional areas.

[0174] Based on the dust concentration information at the central location and the multi-dimensional detection information database of the port area, the spatial distribution analysis results of dust can be obtained.

[0175] Based on the dust concentration information at the center of the unit small grid and the multi-dimensional detection information database of the port area, the embodiment comprehensively uses data analysis and spatial modeling techniques to obtain the spatial distribution analysis results of dust in different functional areas of the port area. The results can comprehensively present the concentration distribution characteristics of dust in different spatial locations within the port area.

[0176] Furthermore, by combining the actual total area of ​​different functional areas of the port (such as storage yards, loading and unloading areas, and transport roads) with key parameters such as the flight distance of dust particles of different sizes, the area of ​​the most severely dust-polluted area within each functional area is analyzed in depth. In an optional embodiment, based on the dust particle flight distance and combined with the geometry and layout of the functional areas of the port, the area of ​​the region where the dust concentration exceeds a specific threshold is calculated, while comprehensively considering the impact of factors such as flight distance on the dust diffusion range.

[0177] Based on this, weighted averaging and interpolation methods are introduced to transform discrete monitoring data into a continuous pollution level distribution map. The above process fully considers the spatial correlation between different port monitoring areas and the continuity of dust concentration changes, so that the port dust level distribution map can more intuitively and accurately reflect the spatial distribution of dust pollution in the port area.

[0178] You can also refer to the dust distribution map of the port area to set a scientific and reasonable dust warning threshold. Mark the small grid units where the dust pollution level exceeds the threshold as dust exceeding the standard area or areas with serious air quality. The relevant areas are the key targets for dust pollution prevention and control in the port area, which will help to take targeted treatment measures in the future.

[0179] Furthermore, by integrating and analyzing dust concentration data from different time periods (time series), such as daytime, nighttime, and different seasons, it is beneficial to reveal the intrinsic relationship and changing patterns between dust concentration and time. In the example, by using time series analysis, statistical modeling, and other methods, the high-incidence time periods (time series) of dust pollution in the port area can be identified, providing a time-dimensional reference for the subsequent formulation of dust pollution prevention and control strategies.

[0180] On the other hand, dust spatial distribution analysis can also be conducted on different functional areas of the port area as a whole.

[0181] Based on spatial distribution analysis, Geographic Information System (GIS) technology was used to deeply correlate dust concentration data with port area geographic information. This process encompassed multi-dimensional geographic elements such as port topography, functional zoning, and road network distribution. Through spatial data matching and integration techniques, a high-precision spatial distribution map of port area dust concentration was obtained. This map presents the degree of dust pollution in different areas of the port area in an intuitive graphical manner. Furthermore, using visualization techniques such as color gradients and contour lines, it clearly identifies severely polluted, moderately polluted, and lightly polluted areas, providing an intuitive and accurate information foundation for the study of the spatial distribution characteristics of dust pollution in the port area.

[0182] Furthermore, the construction mechanism of the port area dust feature analysis system in this embodiment is only an optional condition of the present invention. In other embodiments, the construction mechanism of the port area dust feature analysis system can be optimized according to the multi-dimensional detection information features of the port area and the feature analysis conditions of the port area dust. This can ensure that the dust feature analysis system accurately matches the actual detection needs of the port area, accurately describes the dust diffusion law, and thus provides more targeted reference information for dust control.

[0183] S3. Establish a prediction system for sensitive factors of dust pollution in the port area. Utilize this system and the multi-dimensional monitoring information database of the port area to obtain the interaction relationships between different sensitive factors. The specific implementation steps and contents are as follows:

[0184] Sensitive factors for dust pollution in the port area are set in the port area dust pollution sensitive factor prediction system. In the example, the above sensitive factors mainly include the meteorological conditions, loading and unloading operation methods and material characteristics of the port area monitoring area.

[0185] First, in order to explore the intrinsic driving mechanism and impact of dust pollution in the port area, sensitivity analysis technology was used to systematically analyze the relevant factors affecting dust pollution in the port area. Through theoretical derivation and practical analysis, meteorological conditions, loading and unloading operation methods and material characteristics were selected as key sensitive factors. The above sensitive factors play an important role in the formation, diffusion and accumulation of dust pollution in the port area, and their changes have a significant impact on the degree of dust pollution.

[0186] Then, based on the aforementioned identified sensitive factors, and combined with the multi-dimensional detection information database of the port area, data information corresponding to different sensitive factors is obtained.

[0187] Meteorological condition sensitive factor data information: covering key meteorological parameters such as wind speed, wind direction, temperature, humidity, and atmospheric pressure. Wind speed and direction directly affect the direction and speed of dust diffusion, and the diffusion range and concentration distribution of dust vary significantly under different wind speeds; temperature and humidity affect the diffusion and settling process of dust by influencing the physical state of dust particles and the turbulent motion of air; changes in atmospheric pressure also affect the diffusion and accumulation of dust to some extent.

[0188] Sensitive factors for loading and unloading operations include: the type of loading and unloading equipment (such as grab cranes, conveyor belts, etc.), the intensity of the loading and unloading operation (the amount loaded and unloaded per unit time), the frequency of the loading and unloading operation (the number of loading and unloading operations per unit time), and the degree of standardization in the operation. Different types of loading and unloading equipment generate different amounts of dust during operation. Increased loading and unloading intensity and frequency directly lead to increased dust emissions, while the degree of standardization in the operation is closely related to the fugitive dust emissions.

[0189] Material characteristic sensitive factor data information: This includes the particle size distribution, moisture content, density, hardness, and chemical composition of the material. The particle size distribution determines the size and number of dust particles; smaller particles are more easily suspended and diffused in the air. Moisture content directly affects the material's dust-generating ability; materials with lower moisture content are more likely to generate dust during loading, unloading, and transportation. The density and hardness of the material affect its breakage during loading and unloading, thus affecting the amount of dust generated. The chemical composition of the material may influence the toxicity of the dust and the degree of environmental harm.

[0190] Subsequently, an evaluation function for the contribution of sensitive factors was established in the prediction system for sensitive factors of dust pollution in the port area.

[0191] To accurately quantify the role of each sensitive factor in the formation of dust pollution in the port area, a sensitive factor contribution evaluation function was constructed in the port area dust pollution sensitive factor prediction system. In this embodiment, the sensitive factor contribution evaluation function was constructed based on the contribution rate weighting method. This function can be used to scientifically and systematically evaluate the contribution of each dust sensitive factor.

[0192] The above-mentioned sensitivity factor contribution evaluation function satisfies the following relationship:

[0193] ,

[0194] in, This indicates the contribution of different dust-sensitive factors in different port areas to changes in dust concentration. This indicates the number of factors influencing changes in dust concentration. This indicates the numbering of dust-sensitive factors in different port areas. This indicates the probability of changes in dust-sensitive factors in different port areas. This represents the diffusion constraint coefficient of turbulence in the port area. This indicates the contribution rate of dust-sensitive factors in different port areas in historical statistical data. This represents the local diffusion contribution rate of dust-sensitive factors in different port areas during the fluid dynamics simulation. This represents the overall diffusion contribution rate of dust-sensitive factors in different port areas during the fluid dynamics simulation.

[0195] The contribution of different dust-sensitive factors in port areas to changes in dust concentration directly reflects the relative role of specific sensitive factors in the formation of dust pollution in port areas, and is a key basis for assessing the importance of each sensitive factor. Calculating and analyzing the contribution can clearly identify sensitive factors that have a significant impact on changes in dust concentration in port areas, providing clear monitoring targets for subsequent pollution prevention and control work.

[0196] The number of factors influencing dust concentration changes covers all sensitive factors that may affect dust concentration in the port area, ensuring the comprehensiveness and completeness of the assessment results. In practical applications, it is necessary to comprehensively consider various possible influencing factors based on the specific conditions of the port area to ensure the accuracy of the assessment results.

[0197] The numbering of dust-sensitive factors in different port areas can be used to distinguish each sensitive factor. During the assessment process, each sensitive factor with a different number is analyzed and calculated to obtain the contribution of each sensitive factor to the change in dust concentration, thereby achieving refined management of different sensitive factors.

[0198] The probability of change of dust sensitive factors in different port areas reflects the possibility of changes in sensitive factors during actual operation of the port area. Taking into account the dynamic characteristics of sensitive factors, the greater the probability of change, the more uncertain the impact of the sensitive factor on dust concentration changes, and more attention needs to be paid to it when assessing its contribution.

[0199] Historical statistics are an objective record of dust pollution in the port area in the past. By analyzing and mining a large amount of historical data, we can obtain the average contribution of each sensitive factor to the change in dust concentration at different times, that is, obtain the contribution rate of different dust sensitive factors in the historical statistics.

[0200] The diffusion constraint coefficient of turbulence in port areas is crucial, as turbulence is a significant factor influencing dust diffusion and concentration distribution. The diffusion constraint coefficient reflects the hindering effect of turbulence on dust diffusion. In actual port environments, the presence of turbulence restricts dust diffusion to a certain extent. The value of the diffusion constraint coefficient directly affects the contribution of sensitive factors to dust concentration changes. Introducing the diffusion constraint coefficient allows for a more accurate consideration of the impact of turbulence on dust diffusion, improving the accuracy of the assessment results.

[0201] Fluid dynamics simulation is an important research tool that can simulate the diffusion process of port dust in complex environments. By analyzing the results of fluid dynamics simulation, the contribution of each sensitive factor to dust diffusion in local areas and the overall port area was obtained, further ensuring the effective operation of the sensitive factor contribution evaluation function.

[0202] Next, the contribution of different sensitive factors to dust pollution in the port area was analyzed using the sensitive factor contribution assessment function.

[0203] By using the sensitivity factor contribution assessment function, the contribution of different sensitivity factors in the dust pollution process in the port area can be calculated, which can accurately quantify the relative impact of different sensitivity factors on dust pollution in the port area.

[0204] The contribution assessment results clearly and intuitively present the differences in the impact of different dust-sensitive factors on dust pollution in the port area. The above-mentioned quantitative analysis process helps to identify the sensitive factors that play a dominant role in dust pollution in the port area, providing technical support and adjustment and optimization basis for the formulation of dust control strategies. Subsequently, resources can be allocated and measures can be formulated in a targeted manner according to the contribution of different sensitive factors to improve the efficiency and effectiveness of dust control in the port area.

[0205] Finally, based on the data and contribution levels of the different sensitive factors mentioned above, the interaction relationships between the different sensitive factors are obtained.

[0206] Based on the data and contribution assessment results of the different sensitive factors, this study explores the intrinsic relationships among them and uses correlation analysis to reveal the mutual influence mechanism and synergistic effect of different sensitive factors in the formation of dust pollution in the port area. This will help to build a more comprehensive dust pollution prevention and control system in the port area.

[0207] Furthermore, the method for analyzing sensitive factors of port area dust pollution in this embodiment is only an optional condition of this embodiment. In other embodiments, the method for analyzing sensitive factors of port area dust pollution can be optimized according to the port area dust control target and the actual situation of dust pollution. This can fully take into account the actual situation of dust pollution in the port area and conduct targeted analysis of sensitive factors of dust generated by different pollution sources in the port area.

[0208] S4. Based on the data feature analysis results, dust movement feature analysis results, and dust spatial distribution feature analysis results, an initial management plan for port area dust pollution is designed. This initial management plan is then further adjusted according to the interaction relationships to obtain a final management plan for port area dust pollution. The specific implementation steps and contents are as follows:

[0209] The first step is to design an initial management plan for dust pollution in the port area.

[0210] The initial management plan in this embodiment is designed based on the multi-dimensional characteristics of dust in the port area. Specifically, the initial management plan for dust pollution in the port area needs to be designed by comprehensively considering the data characteristics, motion characteristics and spatial distribution characteristics of dust in the port area.

[0211] Port area dust data characteristic analysis results:

[0212] Based on the port area's multi-dimensional detection information database and the data feature analysis layer of the port area's dust feature analysis system, the data feature analysis results of port area dust were obtained. The aforementioned data features mainly include the mean value. Maximum value Minimum value Standard deviation The time-series information contained in the data feature analysis layer includes the dynamic changes of the mean over time, the trend of the maximum value over time, the fluctuation pattern of the minimum value over time, and the variation characteristics of the standard deviation over time. This time-series information can clearly reflect the changing trend and fluctuation of dust concentration in the port area over different time periods, providing information basis and technical support for grasping and analyzing the patterns of dust pollution.

[0213] Analysis results of dust movement characteristics in the port area:

[0214] Based on the port area's multi-dimensional monitoring information database and the dust movement characteristic analysis layer within the port area's dust characteristic analysis system, dust movement characteristic analysis results were obtained. These results include the flight distance of dust particles of different sizes, the dust diffusion range at the dust source center location in different functional areas, dust concentration monitoring data, meteorological data, and operational activity data. The differences in flight distance of dust particles of different sizes reflect the influence of the physical properties of dust particles on their diffusion ability; the dust flight distance at the dust source center location in different functional areas is closely related to the port area's operational layout and environmental conditions; and the comprehensive analysis of dust concentration monitoring data, meteorological data, and operational activity data reveals the intrinsic connection between dust movement and external factors, providing a basis for formulating targeted management measures.

[0215] Analysis results of spatial distribution characteristics of dust in the port area:

[0216] Based on the port area's multi-dimensional monitoring information database and dust characteristic analysis system, the spatial distribution characteristics of dust in the port area were analyzed. These results include the spatial distribution patterns of dust at different locations within the functional area, the coordinates of the center of each small grid cell, dust concentration information at the center of each small grid cell, concentration distribution characteristics at different spatial locations, dust intensity distribution maps, and spatial distribution pattern information. Additionally, dust concentration data for different time periods (time series) and port area geographic information are included. This information helps to explore the dynamic changes in dust pollution in time and space, providing reference information for port area layout and the formulation of regional management strategies.

[0217] Based on the analysis results of the above-mentioned characteristics, motion characteristics and spatial distribution characteristics of dust in the port area, and combined with the actual situation and management needs of the port area, an initial management plan for dust pollution in the port area was designed. This plan mainly covers multiple aspects such as pollution source identification, pollution propagation path analysis and control measures recommendations, to ensure that the management plan can effectively address the dust pollution problem in the port area.

[0218] In one alternative embodiment, for specific areas of the port area with severe dust pollution, the construction of a real-time dust monitoring and early warning system can be strengthened, high-precision monitoring equipment can be installed to realize real-time dynamic monitoring of dust concentration, and early warning information can be issued in a timely manner; for loading and unloading operation areas, comprehensive measures such as closed operation and water spraying can be adopted to reduce the generation and spread of dust; for transport roads, the cleaning frequency should be increased, and dust curtains and other facilities should be set up to reduce the impact of road dust on the surrounding environment.

[0219] The second step is to optimize the initial management plan for dust pollution in the port area to obtain the final management plan for dust pollution in the port area.

[0220] Based on meteorological condition sensitivity factors within the dust sensitivity factor analysis, this includes core meteorological parameters such as wind speed, wind direction, temperature, humidity, and atmospheric pressure. Wind speed and direction are key factors, directly determining the direction and speed of dust diffusion. Under different wind speeds, the diffusion range and concentration distribution of dust exhibit significant differences; for example, at high wind speeds, dust diffuses further and its concentration distribution is more dispersed, while at low wind speeds, dust tends to accumulate in localized areas. Temperature and humidity exert complex influences on dust diffusion and settling processes by altering the physical properties of dust particles (such as surface tension and adhesion) and the intensity of air turbulence. Changes in atmospheric pressure also disturb airflow to some extent, thus affecting dust diffusion and accumulation patterns.

[0221] Sensitive factors for loading and unloading operations mainly include the type of loading and unloading equipment, the intensity of the operation, the frequency of the operation, and the degree of standardization in the operation. Different types of loading and unloading equipment generate significantly different amounts of dust during operation. For example, grab cranes may generate more dust when grabbing materials, while conveyor belts generate relatively less dust when running smoothly. Increased loading and unloading intensity and frequency directly lead to a significant increase in dust emissions; high-intensity, high-frequency loading and unloading operations release large amounts of dust into the air. The degree of standardization in loading and unloading operations is closely related to fugitive dust emissions; improper operation can easily lead to dust leakage and diffusion.

[0222] Material characteristic sensitive factors include particle size distribution, moisture content, density, hardness, and chemical composition. Particle size distribution determines the size and quantity distribution of dust particles; smaller particles remain suspended in the air longer and spread over a wider area. Moisture content is a crucial factor affecting a material's dust-generating ability; materials with lower moisture content are more prone to dust generation during loading, unloading, and transportation due to friction and collision. Density and hardness affect the degree of breakage during loading and unloading; materials with lower hardness and density are more easily broken into fine particles, increasing dust generation. The chemical composition of a material can affect the toxicity and environmental hazard of dust; dust with certain chemical compositions may cause more serious damage to human health and the ecological environment.

[0223] Based on the contribution of sensitive factors to changes in dust concentration, this study quantifies the degree of contribution of different sensitive factors to changes in dust concentration in the port area, clarifies the relative influence of each factor in the dust pollution formation process, identifies the sensitive factors that play a dominant role in changes in dust concentration, and provides direction for optimizing dust pollution management measures in the port area.

[0224] This study explores the interaction mechanisms and synergistic effects of different sensitive factors in the formation of dust pollution in port areas by combining the intrinsic relationships among them. In one optional embodiment, the correlation between wind speed and loading / unloading operation intensity is analyzed to understand how changes in loading / unloading operation intensity affect the dust diffusion range under specific wind speed conditions. The relationship between material moisture content and temperature and humidity is also studied to explore how environmental meteorological conditions affect the dust generation characteristics of materials. Through these intrinsic relationships, a more comprehensive understanding of the formation mechanism of dust pollution in port areas can be achieved, providing a basis for optimizing initial management plans.

[0225] Based on the above dust-sensitive factors and their interactions, the initial management plan for dust pollution in the port area was adjusted accordingly. In terms of pollution source identification, potential pollution sources arising from changes in sensitive factors can be located more accurately. During pollution propagation path analysis, the impact of sensitive factors on dust diffusion was fully considered, and the propagation path model was optimized. Regarding control measures, more targeted and effective measures were proposed based on the characteristics and interactions of the sensitive factors, such as setting up windbreak facilities to address wind speed effects, optimizing operational processes and equipment for loading and unloading operations, and implementing pretreatment measures based on material characteristics. This resulted in the final management plan for dust pollution in the port area.

[0226] In this embodiment, the method for analyzing the characteristics of dust pollution in port areas and identifying sensitive factors also includes the evaluation, optimization, and long-term management mechanism of the port area dust pollution management plan.

[0227] To ensure that the port area dust pollution management plan is always in optimal condition, a comprehensive and systematic evaluation, optimization and management mechanism has been constructed in the port area dust pollution characteristic analysis and sensitive factor identification method.

[0228] I. Evaluate and optimize the dust pollution management plan.

[0229] Multi-dimensional evaluation and verification: Simulation analysis methods or field experiments can be used to evaluate the dust pollution management plan in the port area. During the simulation analysis, computer models, such as atmospheric diffusion models and dust source apportionment models, are used to simulate the diffusion, transmission and deposition of dust under different working conditions, quantify the changes in dust concentration before and after the implementation of the plan, and evaluate the control effect of the management plan on dust pollution.

[0230] Field trials need to be conducted in the actual port area environment, setting up monitoring points to collect data such as dust concentration and meteorological conditions in real time, and comparing and verifying the results with the simulation results to ensure the effectiveness and feasibility of the management plan.

[0231] II. Adjustment and improvement of dust pollution management plan.

[0232] Based on the above assessment results, the management plan was adjusted and improved accordingly. The causes of the problems identified in the assessment were analyzed; for example, substandard dust concentration control in certain functional areas might be due to insufficient coverage of control measures or unreasonable parameter settings. On this basis, the layout, parameters, and operation methods of the control measures were optimized to improve the plan's relevance and operability. Simultaneously, control costs were fully considered; through techno-economic analysis, cost-effective control technologies and equipment were selected to reduce costs and ensure the plan's economic viability.

[0233] III. Establish an operation and maintenance mechanism for the dust pollution management plan.

[0234] Regular inspection and maintenance plan: Establish a comprehensive operation and maintenance mechanism for port area dust pollution management, and formulate detailed inspection and maintenance plans. Conduct regular comprehensive inspections of management equipment and monitoring systems, including equipment operating status, performance indicators, and component wear, to promptly identify and address potential problems and malfunctions. Simultaneously, regularly analyze and audit monitoring data to ensure the accuracy and reliability of relevant data.

[0235] Emergency Response Support: Establish an emergency response mechanism, formulate emergency plans, clarify the response measures for sudden problems and malfunctions that occur during the operation of the management plan, and equip it with professional emergency response personnel and equipment to ensure that the problem can be responded to and resolved in the first instance, and to ensure the safe and stable operation of the dust pollution management plan.

[0236] IV. Dynamically update and continuously improve the dust pollution management plan.

[0237] Data-driven updates: As the port area develops and changes, such as the expansion of port scale, adjustments to loading and unloading operations, and the increase in the types of materials, the characteristics and patterns of dust pollution will also change accordingly. Therefore, it is necessary to continuously collect new data, including meteorological data, loading and unloading operation data, and material characteristic data, and use data mining and machine learning technologies to deeply analyze the changing trends and influencing factors of dust pollution, providing a basis for updating and optimizing management plans.

[0238] User Feedback Improvement: Actively collect user feedback, including opinions and suggestions from port management personnel, operational staff, and surrounding residents. Understand the problems and shortcomings of the solution in practical application, as well as users' needs for treatment effectiveness and operational convenience. Based on user feedback, continuously improve the functionality and treatment measures of the management solution, enhance the efficiency and level of dust pollution source tracing and management in the port area, and achieve continuous optimization and upgrading of the solution.

[0239] Please see Figure 2 In an optional embodiment, to efficiently execute the port dust pollution feature analysis and sensitive factor identification method provided by the present invention, the present invention also provides a port dust pollution feature analysis and sensitive factor identification system. In this system, input devices, a processor, an output device, and a memory are interconnected. The memory stores a computer program, which includes program instructions. The processor is configured to call the program instructions and execute the specific steps of the relevant embodiments of the port dust pollution feature analysis and sensitive factor identification method provided by the present invention. The port dust pollution feature analysis and sensitive factor identification system of the present invention has a complete and stable structure, and can efficiently execute the port dust pollution feature analysis and sensitive factor identification method of the present invention, thereby improving the overall applicability and practical application capability of the present invention.

[0240] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be covered within the scope of the claims and specification of the present invention.

Claims

1. A method for analyzing the characteristics of dust pollution in port areas and identifying sensitive factors, characterized in that, Includes the following steps: The port area data detection module obtains multi-source detection information of the port area, and the data optimization processing mechanism in the port area data detection module is used to process the multi-source detection information of the port area to obtain a multi-dimensional detection information database of the port area. A port area dust characteristic analysis system is established. Based on the port area multi-dimensional detection information database and the port area dust characteristic analysis system, data characteristic analysis results, dust motion characteristic analysis results, and dust spatial distribution characteristic analysis results of port area dust are obtained. Specifically, this includes: setting up a data characteristic analysis layer, a dust motion characteristic analysis layer, and a dust spatial distribution characteristic analysis layer in the port area dust characteristic analysis system; setting data statistical parameters in the data characteristic analysis layer, including average value, maximum value, minimum value, and standard deviation; setting prediction functions for dust flight distance of different particle sizes in the dust motion characteristic analysis layer; and setting prediction functions for dust flight distance from the dust source center and a monitoring area grid division mechanism in the dust spatial distribution characteristic analysis layer. A prediction system for sensitive factors of dust pollution in port areas is established, and the interaction relationships between different sensitive factors are obtained by using the prediction system and the multi-dimensional detection information database of port areas. Based on the data feature analysis results, the dust movement feature analysis results, and the dust spatial distribution feature analysis results, an initial management plan for dust pollution in the port area is designed. The initial management plan is then adjusted according to the interaction relationship to obtain a final management plan for dust pollution in the port area.

2. The method for analyzing the characteristics of dust pollution in port areas and identifying sensitive factors according to claim 1, characterized in that, The process of using the data optimization and processing mechanism in the port area data detection module to process multi-source detection information in the port area and obtain a multi-dimensional detection information database for the port area includes: Based on the data optimization processing mechanism, the multi-source detection information of the port area is analyzed to obtain the difference sequence information of the multi-source detection information of the port area; The degree of information deviation of the difference sequence information is analyzed based on the data optimization processing mechanism and the difference sequence information. The multi-source detection information in the port area is corrected based on the degree of deviation of the information to obtain the corrected multi-source detection information.

3. The method for analyzing the characteristics of dust pollution in port areas and identifying sensitive factors according to claim 2, characterized in that, The process of using the data optimization and processing mechanism in the port area data detection module to process multi-source detection information in the port area and obtain a multi-dimensional detection information database for the port area includes: The data optimization processing mechanism is configured with an information state function-observation function and an information iterative optimization model. The data optimization processing mechanism analyzes the corrected multi-source detection information based on the information state function-observation function to obtain the state analysis results and observation analysis results of the multi-source detection information; The data optimization processing mechanism combines the state analysis results, the observation analysis results, and the information iterative optimization model to iteratively optimize and integrate the corrected multi-source detection information to obtain a multi-dimensional detection information database for the port area.

4. The method for analyzing the characteristics of dust pollution in port areas and identifying sensitive factors according to claim 3, characterized in that, The configuration of the information state function-observation function and the information iterative optimization model in the data optimization processing mechanism includes: A time-series multi-source detection information set is obtained based on the corrected multi-source detection information; Based on the aforementioned time series-multi-source detection information set, an information state function-observation function is constructed. An information iterative optimization model is established based on the time series-multi-source detection information set and the information state function-observation function.

5. The method for analyzing the characteristics of dust pollution in port areas and identifying sensitive factors according to claim 1, characterized in that, The aforementioned port area dust characteristic analysis system, based on the port area multi-dimensional detection information database and the port area dust characteristic analysis system, yields the following data characteristic analysis results, dust movement characteristic analysis results, and dust spatial distribution characteristic analysis results: The average, maximum, minimum, and standard deviation of port area dust in different time series are calculated using the data feature analysis layer and the port area multi-dimensional detection information database. The data feature analysis results of port area dust are obtained by combining the average, maximum, minimum, and standard deviation. The dust flight distance prediction function of different particle sizes is used to analyze the flight distance of dust of different particle sizes in different time series, and the dust movement characteristic analysis results are obtained based on the flight distance and the multi-dimensional detection information database of the port area.

6. The method for analyzing the characteristics of dust pollution in port areas and identifying sensitive factors according to claim 1, characterized in that, The aforementioned port area dust characteristic analysis system, based on the port area multi-dimensional detection information database and the port area dust characteristic analysis system, yields the following data characteristic analysis results, dust movement characteristic analysis results, and dust spatial distribution characteristic analysis results: The monitoring area is divided according to the grid division mechanism of the monitoring area and the dust movement characteristic analysis results, and the monitoring small grid of the monitoring area is obtained; Introducing spatial decay mechanism and Gaussian diffusion model; Based on the spatial attenuation mechanism, the Gaussian diffusion model, and the port area multi-dimensional detection information database, the dust concentration at the center of the monitoring grid is corrected to obtain the dust concentration information at the center of different monitoring grids. The spatial distribution analysis results of dust are obtained based on the dust concentration information at the central location and the multi-dimensional detection information database of the port area.

7. The method for analyzing the characteristics of dust pollution in port areas and identifying sensitive factors according to claim 1, characterized in that, The establishment of a port area dust pollution susceptibility factor prediction system, and the use of the port area dust pollution susceptibility factor prediction system and the port area multi-dimensional detection information database to obtain the interaction relationships between different susceptibility factors, includes: Sensitive factors for port area dust pollution are set in the port area dust pollution sensitive factor prediction system. The sensitive factors include meteorological conditions, loading and unloading operation methods, and material characteristics of the port area monitoring area. An evaluation function for the contribution of sensitive factors is established in the dust pollution sensitive factor prediction system of the port area.

8. The method for analyzing the characteristics of dust pollution in port areas and identifying sensitive factors according to claim 7, characterized in that, The establishment of a port area dust pollution susceptibility factor prediction system, and the use of the port area dust pollution susceptibility factor prediction system and the port area multi-dimensional detection information database to obtain the interaction relationships between different susceptibility factors, includes: Based on the aforementioned sensitive factors and the multi-dimensional detection information of the port area, different sensitive factor data information is obtained; The contribution of different sensitive factors to dust pollution in the port area was analyzed using the aforementioned sensitive factor contribution assessment function. The interaction relationships between different sensitive factors are obtained based on the data information of the different sensitive factors and the degree of contribution.

9. A system for analyzing the characteristics of dust pollution in port areas and identifying sensitive factors, characterized in that, The system includes a processor, an input device, an output device, and a memory, which are interconnected. The memory stores a computer program, which includes program instructions. The processor is configured to invoke the program instructions to execute the port area dust pollution characteristic analysis and sensitive factor identification method as described in any one of claims 1-8.

Images