System and method for intelligent energy management of excavator

Abstract

The invention discloses a system and a method for intelligent energy management of an excavator. The method comprises the following steps: acquiring intelligent energy sensing information of the excavator; preprocessing the intelligent energy sensing information of the excavator to obtain target processing intelligent energy information; and analyzing and processing the target processing intelligent energy information to obtain target intelligent energy analysis result information.

Description

Technical Field

[0001] This invention relates to the field of excavator technology, and in particular to a system and method for intelligent energy management of excavators. Background Technology

[0002] With the acceleration of industrialization, excavators, as important engineering machinery, play an indispensable role in construction, mining, road construction, and other fields. Energy management is a key factor in ensuring the efficient and stable operation of excavators. However, traditional excavator energy management methods often rely on manual inspection and experience-based judgment, which has many shortcomings: low efficiency (manual inspection is cumbersome, time-consuming, and inefficient, failing to achieve real-time monitoring and rapid response); insufficient accuracy (manual inspection is greatly affected by subjective factors, making it inaccurate in identifying external defects in power supplies, easily leading to missed detections or misjudgments); high maintenance costs (frequent manual inspections increase maintenance costs and also increase the risk of equipment failure due to human error); and poor environmental adaptability (in harsh working environments and complex lighting conditions, the accuracy and reliability of manual inspection are further reduced). Therefore, providing a system and method for intelligent energy management of excavators to improve the efficiency, accuracy, and reliability of excavator energy management and reduce maintenance costs has significant practical application value and market prospects. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to provide a system and method for intelligent energy management of excavators, which is beneficial to improving the efficiency, accuracy and reliability of excavator energy management, reducing maintenance costs, and has important practical application value and market prospects.

[0004] To address the aforementioned technical problems, a first aspect of the present invention discloses a method for intelligent energy management of an excavator, the method comprising: Acquire intelligent energy sensing information from the excavator; The excavator's intelligent energy sensing information is preprocessed to obtain the target processed intelligent energy information; The target intelligent energy information is analyzed and processed to obtain the target intelligent energy analysis result information.

[0005] A second aspect of this invention discloses a system for intelligent energy management of excavators, the system comprising: The acquisition module is used to acquire intelligent energy sensing information from the excavator. The first processing module is used to preprocess the excavator's intelligent energy sensing information to obtain the target intelligent energy information. The second processing module is used to analyze and process the target intelligent energy information to obtain target intelligent energy analysis result information.

[0006] A third aspect of the present invention discloses another system for intelligent energy management of excavators, the system comprising: Memory containing executable program code; A processor coupled to the memory; The processor calls the executable program code stored in the memory to execute some or all of the steps in the method for intelligent energy management of excavators disclosed in the first aspect of the present invention.

[0007] The fourth aspect of the present invention discloses a computer-readable storage medium storing computer instructions, which, when invoked, are used to perform some or all of the steps in the method for intelligent energy management of excavators disclosed in the first aspect of the present invention. Attached Figure Description

[0008] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0009] Figure 1 This is a schematic diagram of a system for intelligent energy management of excavators provided in an embodiment of the present invention; Figure 2 This is a flowchart illustrating a method for intelligent energy management of excavators disclosed in an embodiment of the present invention; Figure 3 This is a schematic diagram of the structure of a system for intelligent energy management of excavators disclosed in an embodiment of the present invention; Figure 4 This is a schematic diagram of another system for intelligent energy management of excavators disclosed in an embodiment of the present invention; Figure 5 This is a schematic diagram of the structure of a target energy management model disclosed in an embodiment of the present invention; Figure 6 This is a schematic diagram of the structure of a feature fusion module disclosed in an embodiment of the present invention. Detailed Implementation

[0010] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0011] The terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this invention are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or apparatuses.

[0012] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0013] In this application, the term "exemplary" is used to mean "used as an example, illustration, or description." Any embodiment described as "exemplary" in this application is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is provided to enable any person skilled in the art to make and use this application. Details are set forth in the following description for purposes of explanation. It should be understood that those skilled in the art will recognize that this application can be made without using these specific details. In other instances, well-known structures and processes are not described in detail to avoid obscuring the description of this application with unnecessary detail. Therefore, this application is not intended to be limited to the embodiments shown, but is consistent with the broadest scope of the principles and features disclosed in this application.

[0014] It should be noted that since the method in this application embodiment is executed in a computer device, the processing objects of each computer device exist in the form of data or information, such as time, which is essentially time information. It is understood that if size, quantity, position, etc. are mentioned in subsequent embodiments, they are all corresponding data that exist so that the computer device can process them. Specific details will not be elaborated here.

[0015] It should be noted that the artificial intelligence-related technologies that may be involved in this application will be briefly described. Artificial intelligence (AI) is the theory, method, technology, and application system that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results. In other words, artificial intelligence is a comprehensive technology in computer science that attempts to understand the essence of intelligence and produce a new kind of intelligent machine that can react in a way similar to human intelligence. Artificial intelligence is the study of the design principles and implementation methods of various intelligent machines, enabling machines to have the functions of perception, reasoning, and decision-making.

[0016] Artificial intelligence (AI) is a comprehensive discipline encompassing a wide range of fields, including both hardware and software technologies. Fundamental AI technologies generally include sensors, dedicated AI chips, cloud computing, distributed storage, big data processing, operating / interactive systems, and mechatronics. AI software technologies primarily include computer vision, speech processing, natural language processing, and machine learning / deep learning.

[0017] Computer vision (CV) is a science that studies how to enable machines to "see." More specifically, it refers to machine vision, which uses cameras and computers to replace human eyes in recognizing and measuring targets, and then performs image processing to create images more suitable for human observation or transmission to instruments. As a scientific discipline, computer vision studies related theories and technologies, attempting to build artificial intelligence systems capable of extracting information from images or multidimensional data. Computer vision technologies typically include image processing, image recognition, image semantic understanding, image retrieval, OCR, video processing, video semantic understanding, video content / behavior recognition, 3D object reconstruction, 3D technology, virtual reality, augmented reality, simultaneous localization and mapping (SLAM), and common biometric recognition technologies such as facial recognition and fingerprint recognition.

[0018] Monomodal information refers to data of only one type, such as text, images, audio, video, or electromagnetic signals. Multimodal information refers to data that includes at least two types of monomodal information. Furthermore, multimodal information is suitable for complex tasks that require the integration of multiple information sources, such as sentiment analysis, robot interaction, and autonomous driving. By integrating information from multiple modalities, higher performance and accuracy can usually be achieved in these tasks.

[0019] Large models refer to artificial neural network models with a very large number of parameters. In the field of artificial intelligence, large models typically refer to models with hundreds of millions to trillions of parameters. These models usually need to be trained on large-scale datasets and require a significant amount of computing resources for optimization and tuning. Large models are commonly used to solve complex tasks such as natural language processing, computer vision, and speech recognition. Generative AI is a type of AI that can create new content and ideas, including dialogues, stories, images, videos, and music. In this embodiment, the large model can be a language model of the scale of ChatGPT, BERT, XLNet, Zhipu model, Claude, Moonshot AI model, ChatGLM model, Tongwen Qianyi model, MiniMax model, Xinghuo model, Llama model, 360GPT model, Qwen model, Baichuan model, Yunque model, vivoLM model, and Wenxin Yiyan, etc., and this embodiment does not limit the scope of the large model.

[0020] This application provides a method, system, computer device, and computer-readable storage medium for intelligent energy management of excavators, which will be described in detail below.

[0021] Please see Figure 1 , Figure 1 This is a schematic diagram of a system for intelligent energy management of excavators provided in an embodiment of this application. The system may include a computer device 100, which integrates the system for intelligent energy management of excavators, such as... Figure 1 Computer equipment in the country.

[0022] In this embodiment of the application, the computer device 100 is mainly used to acquire intelligent energy sensing information of the excavator; The excavator's intelligent energy sensing information is preprocessed to obtain the target processed intelligent energy information; The target intelligent energy information is analyzed and processed to obtain the target intelligent energy analysis result information.

[0023] It can improve the efficiency, accuracy and reliability of excavator energy management, reduce maintenance costs, and has significant practical application value and market prospects.

[0024] In this embodiment, the computer device 100 can be a standalone server, a server network, or a server cluster. For example, the computer device 100 described in this embodiment includes, but is not limited to, a computer, a network host, a single network server, a set of multiple network servers, or a cloud server composed of multiple servers. The cloud server is composed of a large number of computers or network servers based on cloud computing.

[0025] It is understood that the computer device 100 used in the embodiments of this application can be a device that includes both receiving and transmitting hardware, that is, a device having receiving and transmitting hardware capable of performing bidirectional communication on a bidirectional communication link. Such a device may include: cellular or other communication devices having a single-line display, a multi-line display, or a cellular or other communication device without a multi-line display. Specifically, the computer device 100 may be a desktop terminal or a mobile terminal, and may also be one of a mobile phone, tablet computer, laptop computer, etc.

[0026] Those skilled in the art will understand that Figure 1 The application environment shown is merely one application scenario of the solution in this application and does not constitute a limitation on the application scenario of the solution in this application. Other application environments may include those that are more specific to this application. Figure 1 The number of computer devices shown is more or less, for example Figure 1 Only one computer device is shown in the diagram. It is understood that the system for intelligent energy management of excavators may also include one or more other services, which are not limited here.

[0027] In addition, such as Figure 1 As shown, the system for intelligent energy management of excavators may also include a memory 200 for storing data, such as image data and location information.

[0028] It should be noted that, Figure 1 The schematic diagram of the system for intelligent energy management of excavators shown is merely an example. The system and scenario for intelligent energy management of excavators described in this application are for the purpose of more clearly illustrating the technical solutions of this application and do not constitute a limitation on the technical solutions provided in this application. As those skilled in the art will know, with the evolution of the system for intelligent energy management of excavators and the emergence of new business scenarios, the technical solutions provided in this application are also applicable to similar technical problems.

[0029] This invention discloses a system and method for intelligent energy management of excavators, which helps improve the efficiency, accuracy, and reliability of excavator energy management, and reduces maintenance costs. It has significant practical application value and market prospects. The following sections provide detailed descriptions.

[0030] Example 1 Please see Figure 2 , Figure 2 This is a flowchart illustrating a method for intelligent energy management of excavators disclosed in an embodiment of the present invention. Figure 2The described method for intelligent energy management of excavators is applied in a management system, such as a local server or cloud server for management, and this embodiment of the invention is not limited thereto. Figure 2 As shown, the method for intelligent energy management of excavators may include the following operations: 101. Obtain intelligent energy sensing information from the excavator.

[0031] 102. Preprocess the intelligent energy sensing information of the excavator to obtain the target intelligent energy information.

[0032] 103. Analyze and process the target intelligent energy information to obtain the target intelligent energy analysis results.

[0033] It should be noted that the images corresponding to the aforementioned intelligent energy sensing information of the excavator are captured by a binocular imaging device installed on the excavator's working device, and this embodiment of the invention does not impose limitations on this. Furthermore, the excavator's energy system can be based on a motor + battery drive + pump control system. Furthermore, the aforementioned intelligent energy sensing information of the excavator is an image captured of the excavator's energy system. Furthermore, the aforementioned target intelligent energy analysis result information characterizes the identification of external defects or damage to the excavator's energy system, and this embodiment of the invention does not impose limitations on this.

[0034] It should be noted that, considering that the intelligent energy perception information of the excavator contains a large amount of irrelevant information, which not only interferes with the processing results of the target energy management model, but also occupies storage space and reduces the processing efficiency of the target energy management model, this application removes this unnecessary information by preprocessing the images corresponding to the intelligent energy perception information of the excavator, optimizes the image quality, and improves the accuracy and efficiency of image analysis. The embodiments of this invention are not limited.

[0035] It should be noted that, to illustrate the effectiveness of the method in this application, after using training samples (total number of samples greater than or equal to 1000) obtained by users taking images of the excavator's intelligent energy system with high-definition imaging equipment (such as cameras), 80% of the training samples were selected as the training set (no less than 400 images without defects or damage), and 20% of the training samples were selected as the test set (half of the images without defects or damage and half of the images with each type of defect or damage). Under the same experimental environment, the Padim and Retro-KD models were selected for comparative experiments.

[0036] As can be seen from the above comparative experiments, the present application has significant advantages over existing model algorithms, with obvious improvements in various evaluation indicators, thus demonstrating that the method of the present application has high-precision defect or damage identification capabilities. The embodiments of the present invention are not limited.

[0037] It is evident that implementing the method for intelligent energy management of excavators described in the embodiments of the present invention is beneficial to improving the efficiency, accuracy, and reliability of excavator energy management, reducing maintenance costs, and has significant practical application value and market prospects.

[0038] In an optional embodiment, the excavator's intelligent energy sensing information is preprocessed to obtain target intelligent energy information, including: The intelligent energy sensing information of the excavator is processed by contour detection to obtain the first processed sensing image information; The first processed perceived image information is subjected to image segmentation processing to obtain the second processed perceived image information; The second-processed perceived image information is filtered to obtain the third-processed perceived image information. The image information perceived by the third processing is normalized to obtain the target processing intelligent energy information.

[0039] It should be noted that the above normalization process can be based on mean-variance normalization to transform the image corresponding to the third-processed perceived image information, so as to normalize the abnormal data, thereby facilitating the efficient identification of abnormal data by the subsequent target energy management model and mitigating the model data processing efficiency problem caused by excessively large abnormal data gradient values. This embodiment of the invention does not limit the scope of the problem.

[0040] It should be noted that the above-mentioned filtering of the second-processed perceived image information can be based on median filtering to filter out unnecessary noise, improve image clarity, and facilitate subsequent model recognition and processing. This embodiment of the invention does not limit this.

[0041] It should be noted that the image segmentation processing of the first processed perceived image information described above can be implemented based on the Segment Anything Model (SAM) model or a model built based on Transformer; this embodiment of the invention does not impose any limitation. Furthermore, the image segmentation described above takes into account that the captured image has a large pixel count, while the defect or damage range is relatively small. Therefore, by segmenting the image, a single captured image is divided into multiple images, so that the subsequent model can more efficiently and accurately identify defects or damage; this embodiment of the invention does not impose any limitation.

[0042] It should be noted that the above-mentioned contour detection processing of the excavator's intelligent energy sensing information is a preliminary contour definition of the defect or damage location so that efficient image segmentation processing can be performed in the next step. This embodiment of the invention does not limit this.

[0043] It is evident that implementing the method for intelligent energy management of excavators described in the embodiments of the present invention is beneficial to improving the efficiency, accuracy, and reliability of excavator energy management, reducing maintenance costs, and has significant practical application value and market prospects.

[0044] In another optional embodiment, contour detection processing is performed on the excavator's intelligent energy sensing information to obtain first processed sensing image information, including: The excavator's intelligent energy sensing information is noise-reduced to obtain the first intelligent energy sensing information. Pixel gradient calculation is performed on the first intelligent energy sensing and processing information to obtain the second intelligent energy sensing and processing information; The second intelligent energy sensing and processing information is subjected to non-maximum suppression processing to obtain the third intelligent energy sensing and processing information; The edge contour analysis process is performed on the third intelligent energy sensing and processing information to obtain the fourth intelligent energy sensing and processing information. The fourth intelligent energy sensing information is subjected to contour connection processing to obtain the first processed sensing image information.

[0045] It should be noted that the above-mentioned noise reduction processing of the excavator's intelligent energy sensing information can be implemented based on Gaussian filtering to remove and reduce noise in the image and improve the clarity of defects or losses in the image. This embodiment of the invention does not impose any limitations on this.

[0046] It should be noted that the above-mentioned pixel gradient calculation of the first intelligent energy sensing information is to calculate the gradient of the pixels in the image to determine the contour intensity and direction of each pixel, thereby improving the accuracy and efficiency of contour positioning. This embodiment of the invention does not limit this.

[0047] It should be noted that the above-mentioned non-maximum suppression processing of the second intelligent energy sensing information is to retain the point with the largest local gradient in the gradient direction of each pixel in the image corresponding to the second intelligent energy sensing information, and suppress other pixels, so as to make the contour localization of defects or losses more accurate. This embodiment of the present invention does not limit this.

[0048] It should be noted that the edge contour analysis processing of the third intelligent energy sensing information described above utilizes two different contour filtering thresholds (one large and one small, where the large threshold is more than four times the small threshold, and the values ​​are between 0 and 100; this embodiment of the invention does not impose any limitations). For pixel classification, if the value exceeds the larger threshold, it is directly identified as a clear contour; if the value is below the smaller threshold, it is considered a non-edge part. For pixels between the two thresholds, their classification needs to be determined based on the characteristics of the surrounding pixels. If there are clear contour pixels around them, these pixels are classified as weak contours; if there are no clear contour pixels, they are considered non-contour parts; this embodiment of the invention does not impose any limitations.

[0049] It should be noted that the above-mentioned contour connection processing of the fourth intelligent energy sensing information connects all the pixels that are determined to be strong contours to form a closed loop contour within a certain area. This embodiment of the invention does not limit this.

[0050] It is evident that implementing the method for intelligent energy management of excavators described in the embodiments of the present invention is beneficial to improving the efficiency, accuracy, and reliability of excavator energy management, reducing maintenance costs, and has significant practical application value and market prospects.

[0051] In another optional embodiment, the target smart energy information is analyzed and processed to obtain target smart energy analysis result information, including: The target energy management model is used to identify and process the target smart energy information to obtain the target identification result information; Based on the target identification results, the target intelligent energy analysis results are determined.

[0052] It should be noted that the above-mentioned target recognition result information is a characterization of the identification results of defects or damage problems in the intelligent energy part of the excavator, and the embodiments of the present invention are not limited thereto.

[0053] It should be noted that the above-mentioned target identification result information, which determines the target smart energy analysis result information, is a visualization of the analyzed smart energy's defects or damage problems, and this embodiment of the invention is not limited to this. Furthermore, it can use the Marching Cubes (MC) algorithm to draw the volume data field, utilize graphics hardware to draw the data field, and use different colors for rendering, so as to intuitively display the defects, and this embodiment of the invention is not limited to this either.

[0054] It should be noted that the aforementioned target energy management model was obtained through unsupervised training. Furthermore, it was implemented using Python 3.8 or later and trained on an NVIDIA GeForce RTX 3090 graphics card. Training samples were obtained by users annotating fatigue damage images of construction machinery taken with high-definition cameras (such as cameras). During training, the batch size was no less than 30 samples, the number of iterations was no less than 100, the optimizer was AdamW, and the learning rate was no greater than 1×10⁻⁶. -4 The loss function can be the cross-entropy loss function, and the trained model can be evaluated using accuracy, precision, and recall. This embodiment of the invention does not limit the scope of the loss function.

[0055] It is evident that implementing the method for intelligent energy management of excavators described in the embodiments of the present invention is beneficial to improving the efficiency, accuracy, and reliability of excavator energy management, reducing maintenance costs, and has significant practical application value and market prospects.

[0056] In another optional embodiment, the target energy management model includes a first convolution module, a first pooling module, a feature extraction module, a feature fusion module, a first connection module, a second connection module, and a first activation module; wherein, The input of the first convolutional module is configured to receive the model input of the target energy management model. The first convolutional module, the first pooling module, the feature extraction module, the feature fusion module, the first connection module, the second connection module, and the first activation module are connected in sequence. The output of the first activation module is configured to output the model output of the target energy management model.

[0057] It should be noted that the first pooling module described above is built based on the max pooling layer, and this embodiment of the invention does not limit it.

[0058] It should be noted that the convolution kernel of the first convolution module can be one of 1×1, 3×1, 3×3, 5×1, 5×3, 5×5, 7×5 and 7×7, with a stride of 1 or 2. This embodiment of the invention does not limit the type of kernel.

[0059] It should be noted that the first and second connection modules described above are constructed based on fully connected layers, and this embodiment of the invention does not impose any limitations on them. Furthermore, through mapping processing using two fully connected layers, nonlinear transformations are performed on the features. The first fully connected layer can linearly combine the input features to generate a new set of high-dimensional features, and the second fully connected layer can further combine these features to generate higher-level abstract features. This nonlinear transformation enables the network to learn complex function mappings, and this embodiment of the invention does not impose any limitations on it.

[0060] It should be noted that the first activation module described above is constructed based on the ReLU activation function, and this embodiment of the invention does not limit it.

[0061] It should be noted that the above-mentioned target energy management model identifies defects or damages in the intelligent energy appearance image perceived by the excavator in order to determine the problems of the excavator's intelligent energy, so as to realize intelligent health management of the excavator's intelligent energy. This embodiment of the invention does not limit the scope of the invention.

[0062] It is evident that implementing the method for intelligent energy management of excavators described in the embodiments of the present invention is beneficial to improving the efficiency, accuracy, and reliability of excavator energy management, reducing maintenance costs, and has significant practical application value and market prospects.

[0063] In an optional embodiment, the feature extraction module includes a first convolutional unit, a second convolutional unit, a third convolutional unit, a fourth convolutional unit, a fifth convolutional unit, a sixth convolutional unit, a first activation unit, a second activation unit, a third activation unit, a fourth activation unit, a first fusion unit, a second fusion unit, a third fusion unit, and a fourth fusion unit; wherein, The input terminals of the first convolutional unit and the first fusion unit are configured as the input terminals of the feature extraction module; the first convolutional unit, the first activation unit, and the first fusion unit are connected sequentially; the output terminal of the first fusion unit is connected to the input terminals of the second convolutional unit and the second fusion unit respectively; the second convolutional unit, the second activation unit, and the second fusion unit are connected sequentially; the output terminal of the second fusion unit is connected to the input terminals of the third convolutional unit and the third fusion unit respectively; the third convolutional unit, the fourth activation unit, the fourth convolutional unit, and the third fusion unit are connected sequentially; the output terminal of the third fusion unit is connected to the input terminals of the fifth convolutional unit and the fourth fusion unit respectively; the fifth convolutional unit, the fourth activation unit, the sixth convolutional unit, and the fourth fusion unit are connected sequentially; the output terminal of the fourth fusion unit is configured as the output terminal of the feature extraction module.

[0064] It should be noted that the convolution kernels of the first, second, third, fourth, fifth, and sixth convolution units can be one of 1×1, 3×1, 3×3, 5×1, 5×3, 5×5, 7×5, and 7×7, with a stride of 1 or 2. This embodiment of the invention does not limit the specific type of kernel.

[0065] It should be noted that the first activation unit, the second activation unit, the third activation unit, and the fourth activation unit mentioned above are constructed based on the ReLU activation function, and this embodiment of the invention does not limit them.

[0066] It should be noted that the first fusion unit, the second fusion unit, the third fusion unit, and the fourth fusion unit mentioned above are constructed based on splicing operations, and this embodiment of the invention does not limit them.

[0067] It should be noted that the aforementioned feature extraction module uses multiple residual modules (in a 2+2 form, i.e., 2 convolutions + activations + fusion, or 2 convolutions + activations + convolutions + fusion) constructed from convolution and activation units to extract features at different levels from the image information perceived by the excavator's intelligent energy system. As the residual structure progresses downwards, the extracted feature depth increases, resulting in richer feature representations. This embodiment of the invention does not impose limitations on this. Furthermore, the residual structure constructed from convolutions + activations + convolutions + fusion includes a 1×1 convolutional layer to reduce the feature dimensionality, thereby reducing the channel dimension, decreasing the number of feature channels, and improving the model's processing efficiency. This embodiment of the invention does not impose limitations on this either.

[0068] It is evident that implementing the method for intelligent energy management of excavators described in the embodiments of the present invention is beneficial to improving the efficiency, accuracy, and reliability of excavator energy management, reducing maintenance costs, and has significant practical application value and market prospects.

[0069] In another optional embodiment, the feature fusion module includes a seventh convolutional unit, an eighth convolutional unit, a ninth convolutional unit, a first normalization unit, a second normalization unit, a third normalization unit, a fifth activation unit, a sixth activation unit, a seventh activation unit, a first pooling unit, a second pooling unit, a first connection unit, a fifth fusion unit, and a first attention unit; wherein, The input of the seventh convolutional unit is connected to the output of the feature extraction module; the seventh convolutional unit, the first normalization unit, the fifth activation unit, the eighth convolutional unit, the second normalization unit, and the sixth activation unit are connected in sequence; the output of the sixth activation unit is connected to the input of the second pooling unit and the input of the first connection unit; the second pooling unit and the first attention unit are connected in sequence; the output of the first attention unit is connected to the input of the fifth fusion unit; the first connection unit, the third normalization unit, the seventh activation unit, and the first pooling unit are connected in sequence; the output of the first pooling unit is connected to the input of the fifth fusion unit; the output of the fifth fusion unit is connected to the input of the ninth convolutional unit; the output of the ninth convolutional unit is connected to the input of the first connection module.

[0070] It should be noted that the convolution kernels of the seventh, eighth, and ninth convolution units mentioned above can be one of 1×1, 3×1, 3×3, 5×1, 5×3, 5×5, 7×5, and 7×7, with a stride of 1 or 2. This embodiment of the invention does not limit the specific type of kernel.

[0071] It should be noted that the first normalization unit, the second normalization unit, and the third normalization unit mentioned above are constructed based on the batch normalization layer, and this embodiment of the invention does not limit them.

[0072] It should be noted that the fifth, sixth, and seventh activation units mentioned above are constructed based on the ReLU activation function, and this embodiment of the invention does not limit them.

[0073] It should be noted that the first pooling unit and the second pooling unit mentioned above are constructed based on the max pooling layer, and this embodiment of the invention does not limit them.

[0074] It should be noted that the first connection unit mentioned above is constructed based on a fully connected layer, and this embodiment of the present invention does not limit it.

[0075] It should be noted that the first attention unit mentioned above is constructed based on a multi-head attention mechanism, and this embodiment of the invention does not limit it.

[0076] It should be noted that the fifth fusion unit mentioned above is constructed based on matrix multiplication operations, and this embodiment of the invention does not limit it.

[0077] It should be noted that the aforementioned feature fusion module first uses a feature encoding module constructed with two convolution, normalization, and activation units to compress and map the input data to a low-dimensional space. By abstracting and compressing the data layer by layer, the intrinsic features of the data are extracted to reduce storage and computational overhead while retaining important feature information, thus achieving efficient representation of deep-level structures and semantic relationships. Furthermore, the features are fused in a dual-branch manner. One branch uses a linear fully connected layer-normalization-activation representation nonlinear module to nonlinearly organize the features, while the other branch uses a multi-head attention mechanism to extract features at a deeper level. At the same time, the pooling unit is used in both branches to transform the dimensions of multiple features, raising them to matrix form. Then, the fifth fusion unit is used to perform feature fusion in the form of matrix multiplication. Finally, the connection unit is used to map it to a one-dimensional vector, thereby achieving multi-level high-dimensional feature fusion. This embodiment of the invention is not limited.

[0078] It is evident that implementing the method for intelligent energy management of excavators described in the embodiments of the present invention is beneficial to improving the efficiency, accuracy, and reliability of excavator energy management, reducing maintenance costs, and has significant practical application value and market prospects.

[0079] Example 2 Please see Figure 3 , Figure 3 This is a schematic diagram of a system for intelligent energy management of excavators disclosed in an embodiment of the present invention. Figure 3 The described system can be applied to management systems, such as local servers or cloud servers, and this invention does not limit its application. Figure 3 As shown, the system may include: The acquisition module 201 is used to acquire intelligent energy sensing information of the excavator; The first processing module 202 is used to preprocess the excavator's intelligent energy sensing information to obtain the target intelligent energy information. The second processing module 203 is used to analyze and process the target intelligent energy information to obtain the target intelligent energy analysis result information.

[0080] It is evident that implementation Figure 3 The system described for intelligent energy management of excavators helps improve the efficiency, accuracy, and reliability of excavator energy management, reduces maintenance costs, and has significant practical application value and market prospects.

[0081] In another alternative embodiment, such as Figure 3 As shown, the excavator's intelligent energy sensing information is preprocessed to obtain the target intelligent energy information, including: The intelligent energy sensing information of the excavator is processed by contour detection to obtain the first processed sensing image information; The first processed perceived image information is subjected to image segmentation processing to obtain the second processed perceived image information; The second-processed perceived image information is filtered to obtain the third-processed perceived image information. The image information perceived by the third processing is normalized to obtain the target processing intelligent energy information.

[0082] It is evident that implementation Figure 3 The system described for intelligent energy management of excavators helps improve the efficiency, accuracy, and reliability of excavator energy management, reduces maintenance costs, and has significant practical application value and market prospects.

[0083] In yet another alternative embodiment, such as Figure 3 As shown, contour detection processing is performed on the intelligent energy sensing information of the excavator to obtain the first processed sensing image information, including: The excavator's intelligent energy sensing information is noise-reduced to obtain the first intelligent energy sensing information. Pixel gradient calculation is performed on the first intelligent energy sensing and processing information to obtain the second intelligent energy sensing and processing information; The second intelligent energy sensing and processing information is subjected to non-maximum suppression processing to obtain the third intelligent energy sensing and processing information; The edge contour analysis process is performed on the third intelligent energy sensing and processing information to obtain the fourth intelligent energy sensing and processing information. The fourth intelligent energy sensing information is subjected to contour connection processing to obtain the first processed sensing image information.

[0084] It is evident that implementation Figure 3 The system described for intelligent energy management of excavators helps improve the efficiency, accuracy, and reliability of excavator energy management, reduces maintenance costs, and has significant practical application value and market prospects.

[0085] In yet another alternative embodiment, such as Figure 3 As shown, the target smart energy information is analyzed and processed to obtain the target smart energy analysis results, including: The target energy management model is used to identify and process the target smart energy information to obtain the target identification result information; Based on the target identification results, the target intelligent energy analysis results are determined.

[0086] It is evident that implementation Figure 3 The system described for intelligent energy management of excavators helps improve the efficiency, accuracy, and reliability of excavator energy management, reduces maintenance costs, and has significant practical application value and market prospects.

[0087] In yet another alternative embodiment, such as Figure 3 As shown, the target energy management model includes a first convolutional module, a first pooling module, a feature extraction module, a feature fusion module, a first connection module, a second connection module, and a first activation module; wherein, The input of the first convolutional module is configured to receive the model input of the target energy management model. The first convolutional module, the first pooling module, the feature extraction module, the feature fusion module, the first connection module, the second connection module, and the first activation module are connected in sequence. The output of the first activation module is configured to output the model output of the target energy management model.

[0088] It is evident that implementation Figure 3 The system described for intelligent energy management of excavators helps improve the efficiency, accuracy, and reliability of excavator energy management, reduces maintenance costs, and has significant practical application value and market prospects.

[0089] In yet another alternative embodiment, such as Figure 3 As shown, the feature extraction module includes a first convolutional unit, a second convolutional unit, a third convolutional unit, a fourth convolutional unit, a fifth convolutional unit, a sixth convolutional unit, a first activation unit, a second activation unit, a third activation unit, a fourth activation unit, a first fusion unit, a second fusion unit, a third fusion unit, and a fourth fusion unit; wherein, The input terminals of the first convolutional unit and the first fusion unit are configured as the input terminals of the feature extraction module; the first convolutional unit, the first activation unit, and the first fusion unit are connected sequentially; the output terminal of the first fusion unit is connected to the input terminals of the second convolutional unit and the second fusion unit respectively; the second convolutional unit, the second activation unit, and the second fusion unit are connected sequentially; the output terminal of the second fusion unit is connected to the input terminals of the third convolutional unit and the third fusion unit respectively; the third convolutional unit, the fourth activation unit, the fourth convolutional unit, and the third fusion unit are connected sequentially; the output terminal of the third fusion unit is connected to the input terminals of the fifth convolutional unit and the fourth fusion unit respectively; the fifth convolutional unit, the fourth activation unit, the sixth convolutional unit, and the fourth fusion unit are connected sequentially; the output terminal of the fourth fusion unit is configured as the output terminal of the feature extraction module.

[0090] It is evident that implementation Figure 3 The system described for intelligent energy management of excavators helps improve the efficiency, accuracy, and reliability of excavator energy management, reduces maintenance costs, and has significant practical application value and market prospects.

[0091] In yet another alternative embodiment, such as Figure 3 As shown, the feature fusion module includes a seventh convolutional unit, an eighth convolutional unit, a ninth convolutional unit, a first normalization unit, a second normalization unit, a third normalization unit, a fifth activation unit, a sixth activation unit, a seventh activation unit, a first pooling unit, a second pooling unit, a first connection unit, a fifth fusion unit, and a first attention unit; wherein, The input of the seventh convolutional unit is connected to the output of the feature extraction module; the seventh convolutional unit, the first normalization unit, the fifth activation unit, the eighth convolutional unit, the second normalization unit, and the sixth activation unit are connected in sequence; the output of the sixth activation unit is connected to the input of the second pooling unit and the input of the first connection unit; the second pooling unit and the first attention unit are connected in sequence; the output of the first attention unit is connected to the input of the fifth fusion unit; the first connection unit, the third normalization unit, the seventh activation unit, and the first pooling unit are connected in sequence; the output of the first pooling unit is connected to the input of the fifth fusion unit; the output of the fifth fusion unit is connected to the input of the ninth convolutional unit; the output of the ninth convolutional unit is connected to the input of the first connection module.

[0092] It is evident that implementation Figure 3 The system described for intelligent energy management of excavators helps improve the efficiency, accuracy, and reliability of excavator energy management, reduces maintenance costs, and has significant practical application value and market prospects.

[0093] Example 3 Please see Figure 4, Figure 4 This is a schematic diagram of another system for intelligent energy management of excavators disclosed in an embodiment of the present invention. Wherein, Figure 4 The described system can be applied to management systems, such as local servers or cloud servers, and this invention does not limit its application. Figure 4 As shown, the system may include: Memory 301 storing executable program code; Processor 302 coupled to memory 301; The processor 302 calls the executable program code stored in the memory 301 to execute the steps in the method for intelligent energy management of excavators described in Embodiment 1.

[0094] Example 4 This invention discloses a computer-readable storage medium storing a computer program for electronic data interchange, wherein the computer program causes a computer to perform the steps in the method for intelligent energy management of an excavator described in Embodiment 1.

[0095] Example 5 This invention discloses a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to perform the steps in the method for intelligent energy management of an excavator described in Embodiment 1.

[0096] The system embodiments described above are merely illustrative. The modules described as separate components may or may not be physically separate, and the components shown as modules may or may not be physical modules; that is, they may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0097] Through the detailed description of the above embodiments, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, including read-only memory (ROM), random access memory (RAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), one-time programmable read-only memory (OTPROM), electrically-Erasable Programmable Read-Only Memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, disk storage, magnetic tape storage, or any other computer-readable medium that can be used to carry or store data.

[0098] Finally, it should be noted that the system and method for intelligent energy management of excavators disclosed in the embodiments of the present invention are merely preferred embodiments of the present invention, and are only used to illustrate the technical solutions of the present invention, not to limit it; 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 of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for intelligent energy management of excavators, characterized in that, The method includes: Acquire intelligent energy sensing information from the excavator; The excavator's intelligent energy sensing information is preprocessed to obtain the target processed intelligent energy information; The target intelligent energy information is analyzed and processed to obtain the target intelligent energy analysis result information.

2. The method for intelligent energy management of excavators according to claim 1, characterized in that, The preprocessing of the excavator's intelligent energy sensing information to obtain target intelligent energy information includes: The intelligent energy sensing information of the excavator is processed by contour detection to obtain the first processed sensing image information; The first processed perceptual image information is subjected to image segmentation processing to obtain the second processed perceptual image information; The second processed perceived image information is filtered to obtain the third processed perceived image information; The third processed perceived image information is normalized to obtain the target processed smart energy information.

3. The method for intelligent energy management of excavators according to claim 2, characterized in that, The process of performing contour detection processing on the intelligent energy sensing information of the excavator to obtain first processed sensing image information includes: The excavator's intelligent energy sensing information is subjected to noise reduction processing to obtain the first intelligent energy sensing processing information; Pixel gradient calculation is performed on the first intelligent energy sensing and processing information to obtain the second intelligent energy sensing and processing information. The second intelligent energy sensing and processing information is subjected to non-maximum suppression processing to obtain the third intelligent energy sensing and processing information. The third intelligent energy sensing and processing information is subjected to edge contour analysis to obtain the fourth intelligent energy sensing and processing information. The fourth intelligent energy sensing information is subjected to contour connection processing to obtain the first processed sensing image information.

4. The method for intelligent energy management of excavators according to claim 1, characterized in that, The analysis and processing of the target smart energy information to obtain target smart energy analysis result information includes: The target processing smart energy information is identified and processed using a target energy management model to obtain target identification result information; Based on the target identification results, the target intelligent energy analysis results are determined.

5. The method for intelligent energy management of excavators according to claim 4, characterized in that, The target energy management model includes a first convolution module, a first pooling module, a feature extraction module, a feature fusion module, a first connection module, a second connection module, and a first activation module; wherein, The input of the first convolution module is configured to receive the model input of the target energy management model. The first convolution module, the first pooling module, the feature extraction module, the feature fusion module, the first connection module, the second connection module, and the first activation module are connected in sequence. The output of the first activation module is configured to output the model output of the target energy management model.

6. The method for intelligent energy management of excavators according to claim 4, characterized in that, The feature extraction module includes a first convolutional unit, a second convolutional unit, a third convolutional unit, a fourth convolutional unit, a fifth convolutional unit, a sixth convolutional unit, a first activation unit, a second activation unit, a third activation unit, a fourth activation unit, a first fusion unit, a second fusion unit, a third fusion unit, and a fourth fusion unit; wherein, The input terminals of the first convolutional unit and the first fusion unit are configured as the input terminals of the feature extraction module; the first convolutional unit, the first activation unit, and the first fusion unit are connected sequentially; the output terminal of the first fusion unit is connected to the input terminals of the second convolutional unit and the second fusion unit respectively; the second convolutional unit, the second activation unit, and the second fusion unit are connected sequentially; the output terminal of the second fusion unit is connected to the input terminals of the third convolutional unit and the third fusion unit respectively; the third convolutional unit, the fourth activation unit, the fourth convolutional unit, and the third fusion unit are connected sequentially; the output terminal of the third fusion unit is connected to the input terminals of the fifth convolutional unit and the fourth fusion unit respectively; the fifth convolutional unit, the fourth activation unit, the sixth convolutional unit, and the fourth fusion unit are connected sequentially; the output terminal of the fourth fusion unit is configured as the output terminal of the feature extraction module.

7. The method for intelligent energy management of excavators according to claim 4, characterized in that, The feature fusion module includes a seventh convolutional unit, an eighth convolutional unit, a ninth convolutional unit, a first normalization unit, a second normalization unit, a third normalization unit, a fifth activation unit, a sixth activation unit, a seventh activation unit, a first pooling unit, a second pooling unit, a first connection unit, a fifth fusion unit, and a first attention unit; wherein, The input of the seventh convolutional unit is connected to the output of the feature extraction module; the seventh convolutional unit, the first normalization unit, the fifth activation unit, the eighth convolutional unit, the second normalization unit, and the sixth activation unit are connected sequentially; the output of the sixth activation unit is connected to the input of the second pooling unit and the input of the first connection unit; the second pooling unit and the first attention unit are connected sequentially; the output of the first attention unit is connected to the input of the fifth fusion unit; the first connection unit, the third normalization unit, the seventh activation unit, and the first pooling unit are connected sequentially; the output of the first pooling unit is connected to the input of the fifth fusion unit; the output of the fifth fusion unit is connected to the input of the ninth convolutional unit; and the output of the ninth convolutional unit is connected to the input of the first connection module.

8. A system for intelligent energy management of excavators, characterized in that, The system includes: The acquisition module is used to acquire intelligent energy sensing information from the excavator. The first processing module is used to preprocess the excavator's intelligent energy sensing information to obtain the target intelligent energy information. The second processing module is used to analyze and process the target intelligent energy information to obtain target intelligent energy analysis result information.

9. A system for intelligent energy management of excavators, characterized in that, The system includes: Memory containing executable program code; A processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the method for intelligent energy management of excavators as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions, which, when invoked, are used to perform the method for intelligent energy management of an excavator as described in any one of claims 1-7.

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