Wharf dumper operation mode optimization method and system based on differential box algorithm

Abstract

The invention relates to the technical field of port intellectualization, and discloses a wharf dumper operation mode optimization method and system based on a differential box algorithm, and the method comprises the steps: collecting a dumper operation process image, calculating fractal dimension features through the differential box algorithm, and building a correlation model of fractal dimensions and energy consumption data; the method comprises the following steps: firstly, preprocessing multiple groups of operation images, extracting fractal dimension features and constructing a feature database; determining a fractal dimension threshold interval of the high-energy-consumption cluster through clustering analysis; after the fractal dimension of the operation image is calculated in real time, the fractal dimension is compared with the threshold interval of the high-energy-consumption cluster, and the operation mode is dynamically adjusted according to the comparison result, so that the real-time optimization of the operation mode is realized, the energy consumption efficiency is remarkably improved, and the operation cost is reduced.

Description

Technical Field

[0001] This invention relates to the field of intelligent port technology, and in particular to a method and system for optimizing the operation mode of terminal dump trucks based on the differential box algorithm. Background Technology

[0002] With the rapid development of global trade, port logistics operational efficiency has become a key factor affecting supply chain effectiveness. Dump trucks, especially those used for transporting bulk cargo such as coal and ore, are the primary horizontal transport tools at bulk cargo terminals, and their energy consumption management directly impacts port operating costs and environmental performance. Currently, most ports prioritize improving operational efficiency during production operations, lacking sophisticated energy consumption analysis methods for dump truck operation, resulting in low energy utilization efficiency and persistently high fossil fuel consumption.

[0003] Traditional energy consumption monitoring methods rely primarily on sensor data and human experience, which has significant limitations. On the one hand, monitoring schemes based on physical sensors are costly to implement and complex to maintain, making large-scale deployment difficult. On the other hand, existing image processing technologies are mostly focused on basic applications such as vehicle recognition and trajectory tracking, failing to delve into the intrinsic relationship between visual features and energy consumption indicators. These technological shortcomings prevent port managers from accurately assessing the impact of different operating modes on energy consumption, making it even more difficult to formulate scientific and effective optimization strategies.

[0004] Fractal geometry theory, as an important tool for describing complex systems, has shown potential in engineering applications, but its application in equipment energy consumption analysis still has significant shortcomings. In calculating the fractal dimension using the difference box algorithm, a mature method for fractal dimension calculation, the parameter settings are crucial to the accuracy of the results. However, current parameter selection largely relies on manual experience or simple heuristic rules, lacking systematic optimization methods and making it difficult to adapt to the feature extraction needs of different scenarios.

[0005] Therefore, there is an urgent need for a method and system for optimizing the operation mode of dump trucks at the dock based on the differential box algorithm. The differential box algorithm is used to obtain the fractal dimension of the dump truck operation mode image, and then the unit consumption of different vehicle operation states is determined based on the fractal dimension, thereby optimizing the operation mode. Summary of the Invention

[0006] To address the aforementioned technical problems, this invention provides a method and system for optimizing the operation mode of dump trucks at the dock based on the differential box algorithm. By obtaining the fractal dimension of the dump truck operation mode image through the differential box algorithm, the energy consumption of different vehicle operation states can be determined, thereby optimizing the operation mode.

[0007] This invention provides a method for optimizing the operation mode of terminal dump trucks based on the differential box algorithm, comprising the following steps: Step 1: Collect multiple sets of images of the dump truck in operation and preprocess them; the images of the dump truck in operation are M×M pixel images; Step 2: Based on the preprocessed images of multiple sets of dump truck operation processes, the fractal dimension is calculated using an improved difference box algorithm; Step 3: Based on the preprocessed multiple sets of dump truck operation process images, construct a feature database, which includes: the fractal dimension and corresponding energy consumption data for each dump truck operation process image; Step 4: Use K-means clustering analysis to identify the relationship between fractal dimension and energy consumption data, and set the fractal dimension threshold range for high energy consumption clusters based on the relationship. Step 5: Calculate the real-time fractal dimension based on the real-time images of the dump truck's operation. Step 6: Compare the real-time fractal dimension with the fractal dimension threshold range of the high-energy-consuming cluster, and adjust the dump truck operation mode according to the comparison result.

[0008] Furthermore, in step 1, the preprocessing includes: performing grayscale processing, image size standardization, and image enhancement processing on the multiple sets of dump truck operation process images. Furthermore, in step 2, the improved difference box algorithm is: using a genetic algorithm to obtain the optimal combination of scale parameters for the difference box algorithm.

[0009] Furthermore, the step of using a genetic algorithm to obtain the optimal combination of scale parameters for the difference box algorithm specifically includes: Step 2-1: Randomly generate multiple scale parameter combinations as the initial population. Each scale parameter combination contains several different scale parameters. Step 2-2: For each combination of scale parameters in the population, the difference box algorithm is used to calculate the fractal dimension sequence of multiple sets of sample images, and the fitness value is calculated based on the standard deviation of the fractal dimension and the contour coefficient in different energy consumption data intervals. Steps 2-3: Select the next generation population based on fitness values ​​and preset ratios; Steps 2-4: Perform crossover and mutation operations on the selected next generation population to generate new combinations of scale parameters; Step 2-5: Repeat steps 2-2 to 2-4 until the maximum number of iterations is reached, and output the optimal combination of scale parameters.

[0010] Furthermore, in step 2, the improved difference box algorithm is used to calculate the fractal dimension, specifically including: Step 2-1-1: Divide the pre-processed dump truck operation images into multiple images of size s. i ×s i A grid of pixels, where si Let be the scale parameter, and s i The range of values ​​for s satisfies 2 ≤ s i ≤M / 2, where M is the width of the image; Step 2-1-2: For each grid cell, calculate the maximum and minimum grayscale values ​​of the pixels within it. Step 2-1-3: Determine the box height k based on the total gray level G of the image; Step 2-1-4: Calculate the number of boxes required to cover each grid based on the maximum and minimum grayscale values; Step 2-1-5: Sum the number of boxes in all grids to obtain the total number of boxes N(s) covering the entire image. i ); Step 2-1-6: Iterate through all scale parameters in the optimal scale parameter combination, repeating steps 2-1-1 to 2-1-5 to obtain different s i The corresponding N(s) i )sequence; Step 2-1-7, calculate log(N(s) i )) and log(1 / s i Perform a least-squares linear fit, and the slope of the fitted line is the fractal dimension D.

[0011] Furthermore, step 4 specifically includes: Step 4-1: Standardize the fractal dimension data and corresponding energy consumption data in the feature database to construct a feature matrix; Step 4-2: Use the elbow rule to calculate the sum of squared errors within clusters for different K values, and select the K value corresponding to the point where the rate of decrease of the sum of squared errors changes abruptly as the optimal number of clusters; Step 4-3: Initialize cluster centers using the K-means+ algorithm, set the maximum number of iterations, use Euclidean distance as a similarity measure, perform cluster analysis on the feature matrix, iterate to the maximum number of iterations, and output the mean fractal dimension, standard deviation, and average energy consumption of each cluster; Step 4-4: Based on the clustering results, identify the cluster with the highest average energy consumption as a high-energy-consuming cluster; Steps 4-5: Based on the mean fractal dimension and standard deviation of the fractal dimension of the high-energy-consuming cluster, calculate the fractal dimension threshold range of the high-energy-consuming cluster.

[0012] Furthermore, in steps 4-5, the fractal dimension threshold range for high-energy-consuming clusters is [μ]. high -2σ high μ high +2σ high ]; where μ high Let σ be the mean fractal dimension of the high-energy-consuming cluster. highis the standard deviation of the fractal dimension of the high-energy-consuming cluster.

[0013] Furthermore, adjusting the dump truck's operating mode in step 6 includes: Based on the comparison results, when the real-time fractal dimension falls into the threshold range of the high-energy-consuming cluster, a running path optimization suggestion or speed control strategy is generated. The proposed route optimization or speed control strategy is sent to the terminal scheduling system to adjust the operating parameters of the dump truck.

[0014] Furthermore, the method for optimizing the operation mode of terminal dump trucks based on the differential box algorithm also includes: The operating status of the dump truck is monitored in real time, and abnormal fluctuations are detected by continuously analyzing the deviation of the real-time fractal dimension from the fractal dimension threshold range of the high-energy-consumption cluster. When the real-time fractal dimension is detected to continuously deviate from the fractal dimension threshold range of the high-energy-consuming cluster, an early warning signal is issued to the monitoring platform; Based on the aforementioned warning signal, a manual intervention or automatic adjustment mechanism is triggered.

[0015] This invention also provides a terminal dump truck operation mode optimization system based on the differential box algorithm, used to execute the above-mentioned terminal dump truck operation mode optimization method based on the differential box algorithm, including: Image collection module: Used to collect multiple sets of images of the dump truck in operation and perform preprocessing; Fractal dimension calculation module: Used to calculate the fractal dimension based on multiple pre-processed images of dump truck operation, using the difference box algorithm. Feature database construction module: used to construct a feature database based on multiple pre-processed images of dump truck operation process, the feature database including: the fractal dimension and corresponding energy consumption data of each image of dump truck operation process; Threshold interval setting module: used to identify the relationship between fractal dimension and energy consumption data using K-means clustering analysis, and to set the fractal dimension threshold interval for high energy consumption clusters based on the relationship; Real-time fractal dimension calculation module: used to calculate the real-time fractal dimension based on real-time images of the dump truck's operation process; The dump truck operation mode adjustment module is used to compare the real-time fractal dimension with the dimension threshold range of the high-energy-consumption cluster, and adjust the dump truck operation mode according to the comparison result.

[0016] The present invention has the following technical effects: 1. By extracting the fractal dimension features of dump truck operation images using an improved difference box algorithm, a quantitative correlation between fractal dimension and energy consumption status was established, enabling non-contact intelligent assessment of operation modes. Operation mode optimization is achieved through image analysis, reducing energy consumption and pollutant emissions during vehicle operation. Compared to complex big data statistical analysis of vehicle location and energy consumption, this approach lowers implementation costs while improving system scalability. Real-time fractal dimension calculation and threshold comparison mechanisms transform energy consumption optimization from post-event analysis to dynamic control, achieving proactive optimization of operation modes.

[0017] 2. By adaptively optimizing the scale parameter combination of the difference box algorithm using a genetic algorithm, the stability and discriminative power of fractal dimension calculation are significantly improved, making energy consumption status identification more accurate and reliable. The fractal dimension threshold range set based on K-means clustering analysis can effectively distinguish energy consumption levels, providing a scientific basis for adjusting operational strategies.

[0018] 3. By integrating visual feature analysis with intelligent algorithms, a new technological approach has been provided for energy consumption management of port equipment. Non-contact monitoring reduces reliance on hardware and improves system reliability. Dynamic optimization mechanisms ensure the continuous effectiveness of energy consumption control, providing key technological support for the construction of green ports. The overall solution achieves refined energy consumption management while maintaining operational efficiency. Attached Figure Description

[0019] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0020] Figure 1 This is a flowchart of the dock dump truck operation mode optimization method based on the differential box algorithm provided in the embodiments of the present invention; Figure 2 This is a schematic diagram of a dock dump truck operation mode optimization system based on the differential box algorithm provided in an embodiment of the present invention. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below. Obviously, the described embodiments are only a part of the embodiments of this invention, and not all of them. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0022] Figure 1 This is a flowchart of a method for optimizing the operation mode of dock dump trucks based on the differential box algorithm, provided in an embodiment of the present invention. (See also...) Figure 1 This invention provides a method for optimizing the operation mode of terminal dump trucks based on the differential box algorithm, comprising the following steps: Step 1: Collect multiple sets of images of the dump truck in operation and preprocess them; the images of the dump truck in operation are M×M pixel images; In some embodiments, step 1 includes preprocessing the multiple sets of dump truck operation process images by performing grayscale processing, image size standardization, and image enhancement processing.

[0023] It is worth noting that when collecting multiple sets of images of dump trucks in operation, it is necessary to ensure coverage of different operational scenarios, such as typical working conditions like yard loading and unloading and road driving, to guarantee the representativeness and diversity of the data. Image acquisition is typically accomplished using high-definition cameras deployed at key locations on the dock. These cameras must be able to adapt to complex lighting conditions to avoid image quality degradation due to weather changes or insufficient light. The acquired images need to undergo systematic preprocessing to eliminate noise and standardize the format, providing clear and consistent input for subsequent fractal dimension calculations.

[0024] The first step in preprocessing is grayscale conversion, which transforms color images into grayscale images. This process reduces data complexity by extracting brightness information from the image while preserving key texture features. For example, the outline and motion trajectory of a dump truck in an image are more prominently displayed through grayscale contrast, avoiding interference from color information in the analysis results. Grayscale conversion enhances the stability of image features, making images acquired at different times comparable.

[0025] The next step is image size standardization, which aims to resize all images to a uniform size, eliminating scale inconsistencies caused by differences in shooting distance or angle. This step can be achieved through interpolation or cropping algorithms, ensuring that each image remains consistent in spatial dimensions, thereby avoiding deviations in fractal dimension calculations due to size variations.

[0026] Image enhancement is a crucial preprocessing step, focusing on improving the visual quality of the image. Through techniques such as contrast adjustment and histogram equalization, enhancement can highlight details along the dump truck's path and suppress background interference. For example, under low-light conditions, enhancement algorithms can effectively improve image brightness and clarity, making vehicle motion characteristics more apparent. This process improves the usability of image information, providing a more reliable foundation for fractal dimension extraction.

[0027] Step 2: Based on the preprocessed images of multiple sets of dump truck operation processes, the fractal dimension is calculated using an improved difference box algorithm.

[0028] In some embodiments, in step 2, the improved differential box algorithm refers to using a genetic algorithm to obtain the optimal combination of scale parameters for the differential box algorithm.

[0029] In the process of optimizing the operation mode of dump trucks, the core of step two lies in extracting fractal dimension features from the preprocessed image using the differential box algorithm.

[0030] Step 2 employs a genetic algorithm to determine the optimal parameter combination for the difference box algorithm. The implementation of the genetic algorithm begins with generating an initial population containing multiple scale parameter combinations. Each combination includes a different set of scale values, covering different levels of analysis from fine-grained to coarse-grained. For each scale parameter combination in the population, the fractal dimension sequence of multiple sets of sample images is calculated, and the fitness of each parameter combination is evaluated by analyzing the standard deviation of these sequences and the silhouette coefficients in different energy consumption ranges. The fitness calculation comprehensively considers the stability of fractal dimension calculation and the ability to distinguish different energy consumption states, ensuring that the optimized parameter combination not only guarantees the consistency of results but also effectively identifies different operating modes.

[0031] In some embodiments, obtaining the optimal combination of scale parameters for the difference box algorithm using a genetic algorithm specifically includes: Step 2-1: Randomly generate multiple scale parameter combinations as the initial population. Each scale parameter combination contains several different scale parameters. Step 2-2: For each combination of scale parameters in the population, the difference box algorithm is used to calculate the fractal dimension sequence of multiple sets of sample images, and the fitness value is calculated based on the standard deviation of the fractal dimension and the contour coefficient in different energy consumption data intervals. Steps 2-3: Select the next generation population based on fitness values ​​and preset ratios; Steps 2-4: Perform crossover and mutation operations on the selected next generation population to generate new combinations of scale parameters; Step 2-5: Repeat steps 2-2 to 2-4 until the maximum number of iterations is reached, and output the optimal combination of scale parameters.

[0032] Specifically, the implementation of the genetic algorithm begins with generating an initial population containing multiple combinations of scale parameters, each combination containing a different set of scale parameters s1, s2, ..., s n These scale parameters need to satisfy the constraint: 2≤s i ≤ M / 2. For each combination of scale parameters in the population, calculate the fractal dimension sequence of multiple sets of sample images, and calculate the fitness value using the following formula: The fitness function is: F = α × (1 / σ) + β × Ss; where F is the fitness value; σ is the standard deviation of the fractal dimension sequence; Ss is the silhouette coefficient; and α and β are weighting coefficients. Based on the fitness value, high-performing individuals are selected according to a preset ratio to form the next generation population. Then, the selected individuals undergo crossover and mutation operations to generate new combinations of scale parameters. This evolutionary process is repeated until the set maximum number of iterations is reached, finally outputting the optimal combination of scale parameters obtained after optimization.

[0033] After obtaining the optimal combination of scale parameters, the specific calculation process of the difference box algorithm is executed.

[0034] In some embodiments, step 2, which involves calculating the fractal dimension using an improved difference box algorithm, specifically includes: Step 2-1-1: Divide the pre-processed dump truck operation images into multiple images of size s. i ×s i The grid, where s i Let be the scale parameter, and s i The range of values ​​for s satisfies 2 ≤ s i ≤M / 2; Step 2-1-2: For each grid cell, calculate the maximum and minimum grayscale values ​​of the pixels within it. Step 2-1-3: Determine the box height k based on the total gray level G of the image; that is... Where ceil() is the floor function; Step 2-1-4: Based on the maximum and minimum grayscale values, calculate the coverage of each s. i ×s i The number of boxes required for the grid; ;n ij To cover each s i ×s i The number of boxes required for the grid, max is the maximum gray value of each pixel in the grid, min is the minimum gray value of each pixel in the grid, and floor() is the floor function. Step 2-1-5: Sum the number of boxes in all grids to obtain the total number of boxes N(s) covering the entire image. i ),Right now ; Step 2-1-6: Iterate through all scale parameters in the optimal scale parameter combination, repeating steps 2-1-1 to 2-1-5 to obtain different s i The corresponding N(s) i )sequence; By iterating through all scale parameters in the optimal scale parameter combination and repeatedly performing the above mesh generation and box counting process, a series of total box counts corresponding to different scale parameters can be obtained, forming N(s) i )sequence; Step 2-1-7, calculate log(N(s) i )) and log(1 / s i Perform a least-squares linear fit, and the slope of the fitted line is the fractal dimension D.

[0035] The above calculation process significantly improves the representational ability of fractal dimension features through optimized scale parameter combinations. This fractal dimension calculation method based on genetic algorithm parameter optimization not only improves the accuracy of feature extraction but also enhances the algorithm's adaptability to complex operating environments, providing high-quality feature inputs for subsequent energy consumption analysis and operating mode optimization.

[0036] Step 3: Based on the preprocessed images of multiple sets of dump truck operation process, construct a feature database, which includes: the fractal dimension and corresponding energy consumption data for each dump truck operation process image.

[0037] In this embodiment, constructing the feature database specifically includes: Step 3-1: Obtain vehicle operation data synchronously collected with the preprocessed multiple sets of dump truck operation process images. The vehicle operation data includes, but is not limited to, the instantaneous fuel consumption, cumulative energy consumption, or energy consumption per unit mileage of the dump truck. Step 3-2: Perform time alignment processing on the vehicle operation data to establish a time-series correspondence between dump truck operation process images and energy consumption data; Step 3-3: Record the image identifier, corresponding fractal dimension, and corresponding energy consumption data of each dump truck operation process image as a data record.

[0038] Step 4: Use K-means clustering analysis to identify the relationship between fractal dimension and energy consumption data, and set the fractal dimension threshold range for high energy consumption clusters based on the relationship.

[0039] Step 4 establishes a quantitative relationship between fractal dimension and energy consumption data through cluster analysis, and sets a scientific fractal dimension threshold range for high-energy-consuming clusters.

[0040] Furthermore, step 4 specifically includes: Step 4-1: Standardize the fractal dimension data and corresponding energy consumption data in the feature database to construct a feature matrix.

[0041] Optionally, the Z-score normalization method is used to process the fractal dimension data and corresponding energy consumption data in the feature database, eliminating the dimensional differences between the original features. The normalized data is organized in matrix form and used as input for K-means clustering.

[0042] Step 4-2: Calculate the sum of squared errors within clusters for different K values ​​using the elbow rule. Select the K value corresponding to the point where the rate of decrease in the sum of squared errors abruptly changes as the optimal number of clusters. By calculating the sum of squared errors within clusters for multiple K values, observe the inflection point of the error decrease curve, and select the point where the rate of decrease changes significantly as the optimal number of clusters. This avoids the arbitrariness of subjectively selecting the number of clusters and ensures the objectivity of the clustering results.

[0043] Step 4-3: Initialize cluster centers using the K-means+ algorithm, set the maximum number of iterations, use Euclidean distance as a similarity measure, perform cluster analysis on the feature matrix, iterate to the maximum number of iterations, and output the mean fractal dimension, standard deviation, and average energy consumption of each cluster.

[0044] Step 4-4: Based on the clustering results, the cluster with the highest average energy consumption is identified as a high-energy-consumption cluster.

[0045] Steps 4-5: Based on the mean fractal dimension and standard deviation of the fractal dimension of the high-energy-consuming cluster, calculate the fractal dimension threshold range of the high-energy-consuming cluster. In some embodiments, the fractal dimension threshold range for high-energy-consuming clusters is [μ]. high -2σ high μ high +2σ high ]; where μ high Let σ be the mean fractal dimension of the high-energy-consuming cluster. high The fractal dimension standard deviation of the high-energy-consuming cluster is used. Threshold intervals for both the mean and standard deviation of the fractal dimension of the high-energy-consuming cluster are calculated. The threshold interval for the fractal dimension of the high-energy-consuming cluster is based on the range from the mean of its fractal dimension minus twice the standard deviation to the mean plus twice the standard deviation. This interval setting ensures coverage of the vast majority of data points within the cluster, providing a reliable classification boundary. Establishing the threshold interval enables the system to evaluate the operating status of dump trucks in real time, providing a basis for subsequent optimization decisions.

[0046] Step 5: Calculate the real-time fractal dimension based on the real-time images of the dump truck's operation.

[0047] Step 6: Compare the real-time fractal dimension with the fractal dimension threshold range of the high-energy-consuming cluster, and adjust the dump truck operation mode according to the comparison result.

[0048] In some embodiments, adjusting the dump truck's operating mode in step 6 includes: Based on the comparison results, when the real-time fractal dimension falls within the fractal dimension threshold range of the high-energy-consuming cluster, an operation path optimization suggestion or speed control strategy is generated. The proposed route optimization or speed control strategy is sent to the terminal scheduling system to adjust the operating parameters of the dump truck.

[0049] Specifically, after the real-time fractal dimension calculation is completed, the fractal dimension threshold range of the corresponding high-energy-consuming cluster is called. The comparison logic is based on a simple range judgment: if the real-time fractal dimension falls within the fractal dimension threshold range of the high-energy-consuming cluster, the optimization mechanism is triggered.

[0050] Optionally, when the real-time fractal dimension reaches the high-energy-consumption range, a suggestion generation module will be immediately activated. The operational path optimization suggestion primarily focuses on the rationality of the travel route, recommending more direct routes or more efficient work sequences by analyzing efficient path patterns in historical data. The speed control strategy focuses on adjusting the operational rhythm, suggesting a smoother acceleration curve and a more economical cruising speed. The adjusted operational status is continuously monitored, and the optimization effect is verified through continuous fractal dimension calculations. If the adjusted fractal dimension remains in the high-energy-consumption range, further optimization procedures will be initiated.

[0051] In some embodiments, the terminal dump truck operation mode optimization method based on the differential box algorithm further includes: The operating status of the dump truck is monitored in real time, and abnormal fluctuations are detected by continuously analyzing the deviation of the real-time fractal dimension from the fractal dimension threshold range of the high-energy-consumption cluster. When the real-time fractal dimension is detected to continuously deviate from the fractal dimension threshold range of the high-energy-consuming cluster, an early warning signal is issued to the monitoring platform; Based on the aforementioned warning signal, a manual intervention or automatic adjustment mechanism is triggered.

[0052] When monitoring the operation of dump trucks, the fractal dimension of the images of the dump truck's operation process is calculated in real time and dynamically compared with a pre-set threshold range for the fractal dimension of high-energy-consuming clusters. The monitoring process focuses on analyzing the continuous trend of the fractal dimension. When a real-time value is detected to deviate continuously from the normal range (remaining within the threshold range of the fractal dimension of high-energy-consuming clusters for an extended period), it is marked as an abnormal state. The degree of abnormality is assessed by calculating the magnitude and duration of the deviation between the fractal dimension and the threshold range. For example, if the fractal dimension exceeds the threshold range for several consecutive periods, a warning signal is generated. The warning signal is sent to the central monitoring platform, where the details of the abnormality are displayed in a visual format, including the vehicle number, deviation time, and suggested measures. Based on the warning signal, a two-level response mechanism is triggered: for minor deviations, an automatic adjustment program is initiated, such as fine-tuning the operating speed or route planning; for severe or persistent abnormalities, operators are notified to intervene manually to ensure timely correction of the operating mode. The entire monitoring process operates in a closed loop, continuously optimizing the energy consumption performance of the dump truck through feedback.

[0053] This invention targets typical stages of dump truck operations at bulk cargo terminals, including loading, unloading, and horizontal transportation. It precisely captures operational mode characteristics through continuous image acquisition. The core technology lies in using an improved difference box algorithm to calculate the fractal dimension of the images and establishing a quantitative correlation model between the fractal dimension and energy consumption data based on K-means clustering analysis. This enables non-contact intelligent assessment of operational status. By comparing the fractal dimension with a preset threshold range in real time, speed control or path optimization strategies are dynamically generated and sent to the scheduling system. This transforms operational mode assessment from post-event statistics to in-event intervention, significantly improving energy efficiency and reducing operating costs and emissions.

[0054] like Figure 2 As shown, the present invention also provides a terminal dump truck operation mode optimization system based on the differential box algorithm, used to execute the above-mentioned terminal dump truck operation mode optimization method based on the differential box algorithm, including: Image collection module: Used to collect multiple sets of images of the dump truck in operation and perform preprocessing; Fractal dimension calculation module: Used to calculate the fractal dimension based on multiple pre-processed images of dump truck operation, using the difference box algorithm. Feature database construction module: used to construct a feature database based on multiple pre-processed images of dump truck operation process, the feature database including: the fractal dimension and corresponding energy consumption data of each image of dump truck operation process; Threshold interval setting module: used to identify the relationship between fractal dimension and energy consumption data using K-means clustering analysis, and to set the fractal dimension threshold interval for high energy consumption clusters based on the relationship; Real-time fractal dimension calculation module: used to calculate the real-time fractal dimension based on real-time images of the dump truck's operation process; The dump truck operation mode adjustment module is used to compare the real-time fractal dimension with the fractal dimension threshold range of the high-energy-consumption cluster, and adjust the dump truck operation mode according to the comparison result.

[0055] 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; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the technical solutions of the embodiments of the present invention.

Claims

1. A method for optimizing the operation mode of terminal dump trucks based on the differential box algorithm, characterized in that, Includes the following steps: Step 1: Collect multiple sets of images of the dump truck in operation and preprocess them; the images of the dump truck in operation are M×M pixel images; Step 2: Based on the preprocessed images of multiple sets of dump truck operation processes, the fractal dimension is calculated using an improved difference box algorithm; Step 3: Based on the preprocessed multiple sets of dump truck operation process images, construct a feature database, which includes: the fractal dimension and corresponding energy consumption data for each dump truck operation process image; Step 4: Use K-means clustering analysis to identify the relationship between fractal dimension and energy consumption data, and set the fractal dimension threshold range for high energy consumption clusters based on the relationship. Step 5: Calculate the real-time fractal dimension based on the real-time images of the dump truck's operation. Step 6: Compare the real-time fractal dimension with the fractal dimension threshold range of the high-energy-consumption cluster, and adjust the dump truck operation mode according to the comparison result; In step 2, the improved differential box algorithm refers to using a genetic algorithm to obtain the optimal combination of scale parameters for the differential box algorithm. The process of using a genetic algorithm to obtain the optimal combination of scale parameters for the difference box algorithm specifically includes: Step 2-1: Randomly generate multiple scale parameter combinations as the initial population. Each scale parameter combination contains several different scale parameters. Step 2-2: For each combination of scale parameters in the population, the difference box algorithm is used to calculate the fractal dimension sequence of multiple sets of sample images, and the fitness value is calculated based on the standard deviation of the fractal dimension and the contour coefficient in different energy consumption data intervals. Steps 2-3: Select the next generation population based on fitness values ​​and preset ratios; Steps 2-4: Perform crossover and mutation operations on the selected next generation population to generate new combinations of scale parameters; Step 2-5: Repeat steps 2-2 to 2-4 until the maximum number of iterations is reached, and output the optimal combination of scale parameters.

2. The method for optimizing the operation mode of terminal dump trucks based on the differential box algorithm according to claim 1, characterized in that, In step 1, the preprocessing includes: performing grayscale processing, image size standardization, and image enhancement processing on the multiple sets of dump truck operation process images.

3. The method for optimizing the operation mode of terminal dump trucks based on the differential box algorithm according to claim 1, characterized in that, In step 2, the fractal dimension is calculated using an improved difference box algorithm, specifically including: Step 2-1-1: Divide the pre-processed dump truck operation images into multiple images of size s. i ×s i A grid of pixels, where s i Let be the scale parameter, and s i The range of values ​​for s satisfies 2 ≤ s i ≤M / 2; Step 2-1-2: For each grid cell, calculate the maximum and minimum grayscale values ​​of the pixels within it. Step 2-1-3: Determine the box height k based on the total gray level G of the image; Step 2-1-4: Calculate the number of boxes required to cover each grid based on the maximum and minimum grayscale values; Step 2-1-5: Sum the number of boxes in all grids to obtain the total number of boxes N(s) covering the entire image. i ); Step 2-1-6: Iterate through all scale parameters in the optimal scale parameter combination, repeating steps 2-1-1 to 2-1-5 to obtain different s i The corresponding N(s) i )sequence; Step 2-1-7, calculate log(N(s) i )) and log(1 / s i Perform a least-squares linear fit, and the slope of the fitted line is the fractal dimension D.

4. The method for optimizing the operation mode of terminal dump trucks based on the differential box algorithm according to claim 1, characterized in that, Step 4 specifically includes: Step 4-1: Standardize the fractal dimension data and corresponding energy consumption data in the feature database to construct a feature matrix; Step 4-2: Use the elbow rule to calculate the sum of squared errors within clusters for different K values, and select the K value corresponding to the point where the rate of decrease of the sum of squared errors changes abruptly as the optimal number of clusters; Step 4-3: Initialize cluster centers using the K-means+ algorithm, set the maximum number of iterations, use Euclidean distance as a similarity measure, perform cluster analysis on the feature matrix, iterate to the maximum number of iterations, and output the mean fractal dimension, standard deviation, and average energy consumption of each cluster. Step 4-4: Based on the clustering results, identify the cluster with the highest average energy consumption as a high-energy-consuming cluster; Steps 4-5: Based on the mean fractal dimension and standard deviation of the fractal dimension of the high-energy-consuming cluster, calculate the fractal dimension threshold range of the high-energy-consuming cluster.

5. The method for optimizing the operation mode of terminal dump trucks based on the differential box algorithm according to claim 4, characterized in that, In steps 4-5, the fractal dimension threshold range for high-energy-consuming clusters is [μ]. high -2σ high μ high +2σ high ]; where μ high Let σ be the mean fractal dimension of the high-energy-consuming cluster. high is the standard deviation of the fractal dimension of the high-energy-consuming cluster.

6. The method for optimizing the operation mode of terminal dump trucks based on the differential box algorithm according to claim 4, characterized in that, Step 6, adjusting the dump truck's operating mode, includes: Based on the comparison results, when the real-time fractal dimension falls into the threshold range of the high-energy-consuming cluster, a running path optimization suggestion or speed control strategy is generated. The proposed route optimization or speed control strategy is sent to the terminal scheduling system to adjust the operating parameters of the dump truck.

7. The method for optimizing the operation mode of terminal dump trucks based on the differential box algorithm according to claim 1, characterized in that, The method for optimizing the operation mode of terminal dump trucks based on the differential box algorithm also includes: The system monitors the operating status of the dump truck in real time and detects abnormal fluctuations by continuously analyzing the deviation of the real-time fractal dimension from the fractal dimension threshold range. When the real-time fractal dimension is detected to continuously deviate from the fractal dimension threshold range, an early warning signal is sent to the monitoring platform; Based on the aforementioned warning signal, a manual intervention or automatic adjustment mechanism is triggered.

8. A terminal dump truck operation mode optimization system based on differential box algorithm, used to execute the terminal dump truck operation mode optimization method based on differential box algorithm as described in any one of claims 1-7, characterized in that, The system includes: Image collection module: Used to collect multiple sets of images of the dump truck in operation and perform preprocessing; Fractal dimension calculation module: Used to calculate the fractal dimension based on multiple pre-processed images of dump truck operation, using the difference box algorithm. Feature database construction module: used to construct a feature database based on multiple pre-processed images of dump truck operation process, the feature database including: the fractal dimension and corresponding energy consumption data of each image of dump truck operation process; Threshold interval setting module: used to identify the relationship between fractal dimension and energy consumption data using K-means clustering analysis, and to set the fractal dimension threshold interval for high energy consumption clusters based on the relationship; Real-time fractal dimension calculation module: used to calculate the real-time fractal dimension based on real-time images of the dump truck's operation process; The dump truck operation mode adjustment module is used to compare the real-time fractal dimension with the fractal dimension threshold range of the high-energy-consumption cluster, and adjust the dump truck operation mode according to the comparison result.

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