An analysis optimization method for a loader's shovel loading operation and related apparatus

Abstract

The application discloses a kind of loader shoveling operation analysis optimization method and related equipment, wherein the method obtains the material weight data and operation data of loader under target shoveling type, and efficiency data group corresponding to the target shoveling type and operation operating parameter corresponding to the operation data;According to the operation data and the efficiency data group, the material weight data is analyzed by type, and target material category is obtained;Operation data is analyzed by stage, and target shoveling stage is obtained;According to the target material category, the target shoveling stage and the operation data, the operation operating parameter is analyzed and optimized.The method can effectively improve the accuracy of loader shoveling operation energy consumption analysis, and improve the timeliness of loader shoveling operation optimization.The application relates to the field of engineering machinery automation control technology.

Description

Technical Field

[0001] This application relates to the field of automated control technology for construction machinery, and in particular to an analysis and optimization method and related equipment for loader loading operations. Background Technology

[0002] In the construction machinery industry, loading and unloading operations are common earthmoving operations, widely used in mining, construction, logistics, and other fields. With the development of IoT and AI technologies, the analysis and optimization of loader loading and unloading operations to reduce loader energy consumption and extend service life has become a focus of attention for relevant personnel.

[0003] Currently, the relevant technologies typically involve setting up various simple sensors on the loader and manually analyzing energy consumption and fine-tuning the control operation of the loader's loading operation to optimize the loading operation. However, this method has limited precision in energy consumption analysis and the timeliness of operation optimization is unsatisfactory.

[0004] Therefore, the problems existing in the current technology still need to be solved and optimized. Summary of the Invention

[0005] The purpose of this application is to at least partially solve one of the technical problems existing in the related art.

[0006] Therefore, one objective of this application is to provide an analysis and optimization method and related equipment for loader loading operations, which can effectively improve the precision of energy consumption analysis for loader loading operations and improve the timeliness of optimization for loader loading operations.

[0007] To achieve the above-mentioned technical objectives, the technical solutions adopted in the embodiments of this application include: On the one hand, embodiments of this application provide an analysis and optimization method for loader loading operations, including: Acquire material weight data and operation data of the loader under the target loading type, as well as efficiency data group corresponding to the target loading type and operation parameters corresponding to the operation data. The efficiency data group includes several material efficiency data, each of which is used to characterize the loading efficiency parameter of the corresponding type of material at different loading heights. Based on the operation data and the efficiency data set, the material weight data is analyzed to obtain the target material category; The target loading stage is obtained by performing stage analysis on the operation data; The operation parameters are analyzed and optimized based on the target material category, the target loading stage, and the operation data.

[0008] In addition, the method according to the above embodiments of this application may also have the following additional technical features: Furthermore, in one embodiment of this application, the step of performing type analysis on the material weight data based on the efficiency data set to obtain the target material category includes: Based on the loading height in the operation data, the efficiency data group is filtered to obtain several first target efficiency data, and the loading height corresponding to each first target efficiency data is the same as the loading height in the operation data. The material weight data is analyzed to obtain loading characteristic parameters. Based on the loading characteristic parameters, data matching is performed on all the first target efficiency data to obtain the target material category.

[0009] Furthermore, in one embodiment of this application, the step of performing data matching on all the first target efficiency data according to the loading characteristic parameters to obtain the target material category includes: Based on the aforementioned loading characteristic parameters, the loading coarse screen parameters and loading fine screen parameters are obtained; Based on the shovel-loaded coarse screening parameters, coarse screening matching is performed on all the first target efficiency data to obtain a number of second target efficiency data; the second target efficiency data is the first target efficiency data in which the deviation between the coarse efficiency parameters and the shovel-loaded coarse screening parameters in all the first target efficiency data is less than a preset threshold. Based on the shovel-loaded fine screen parameters, fine screen matching is performed on the fine efficiency parameters in all the second target efficiency data to obtain several fine screen matching degrees, and each fine screen matching degree corresponds to one second target efficiency data. The target material category is determined based on the second target efficiency data corresponding to the target matching degree; the target matching degree is the largest fine screen matching degree among all the fine screen matching degrees.

[0010] Furthermore, in one embodiment of this application, the step of analyzing and optimizing the operation parameters based on the target material category, the target loading stage, and the operation data includes: Obtain the optimal operating parameters corresponding to the target material category; Based on the target loading stage, the operation data is filtered to obtain stage data corresponding to the target loading stage. The stage data is used to characterize the operation data between the stage start time point and the stage end time point of the target loading stage. Multidimensional analysis of the stage data yields the stage energy consumption and several optimization time points for the target loading stage. The operation parameters are analyzed and optimized based on the stage energy consumption, the optimal operating parameters, and all the optimized time points.

[0011] Furthermore, in one embodiment of this application, multidimensional analysis is performed on the stage data to obtain the stage energy consumption and several optimization time points of the target loading stage, including: Obtain the deceleration threshold; Based on the stage data, obtain the first deceleration data and stage load data at each stage time point; Energy consumption analysis is performed on all the load data of the aforementioned stages to obtain the energy consumption of the aforementioned stages; Based on the deceleration threshold, threshold analysis is performed on all the first deceleration data to obtain several second deceleration data. The second deceleration data is the first deceleration data that is greater than or equal to the deceleration threshold among all the first deceleration data. Each stage time point of the second deceleration data is determined as an optimization time point.

[0012] Furthermore, in one embodiment of this application, the step of analyzing and optimizing the operation parameters based on the stage energy consumption, the optimal operating parameters, and all the optimized time points includes: Obtain the stage energy consumption ratio corresponding to the stage energy consumption, and the stage loading efficiency corresponding to the stage start time and stage end time. Based on the standard ton energy consumption ratio and standard loading efficiency corresponding to the optimal operating parameters, a data superiority-inferiority analysis is performed on the stage ton energy consumption ratio and the stage loading efficiency to obtain the superiority-inferiority analysis results; the superiority-inferiority analysis results are used to characterize whether the operating parameters are better than the optimal operating parameters. If the result of the performance analysis is that the operation parameters are better than the optimal operation parameters, then the optimal operation parameters are updated according to the operation parameters; or, if the result of the performance analysis is that the operation parameters are not better than the optimal operation parameters, then the operation parameters are analyzed and optimized according to the optimal operation parameters and all the optimization time points.

[0013] Furthermore, in one embodiment of this application, the step of analyzing and optimizing the job operation parameters based on the optimal operation parameters and all the optimized time points includes: Obtain the target time point, which is the optimization time point among all optimization time points that has not undergone difference analysis; Based on the target time point, a difference analysis of the operating parameters is performed on the optimal operating parameters and the operation parameters to obtain the difference optimization parameters; Based on the aforementioned difference optimization parameters, the operation parameters are optimized and adjusted.

[0014] Furthermore, in one embodiment of this application, the target loading type is determined through the following steps: Obtain the trained classification model; The job data is input into the trained classification model to classify the jobs and obtain the job classification results. If the operation classification result indicates that the loader is performing a loading operation, then the loading type is identified by performing loading type identification on the operation data to obtain the target loading type.

[0015] On the other hand, embodiments of this application provide an analysis and optimization system for loader loading operations, including: The first processing unit is used to acquire material weight data and operation data of the loader under the target loading type, as well as efficiency data group corresponding to the target loading type and operation parameters corresponding to the operation data. The efficiency data group includes several material efficiency data, and each material efficiency data is used to characterize the loading efficiency parameter of the corresponding type of material at different loading heights. The second processing unit is used to perform type analysis on the material weight data based on the operation data and the efficiency data group to obtain the target material category; The third processing unit is used to perform stage analysis on the operation data to obtain the target loading stage; The fourth processing unit is used to analyze and optimize the operation parameters based on the target material category, the target loading stage, and the operation data.

[0016] On the other hand, embodiments of this application also provide an electronic device, including: At least one processor; At least one memory for storing at least one program; When the at least one program is executed by the at least one processor, the at least one processor performs the method described above.

[0017] On the other hand, embodiments of this application also provide a computer-readable storage medium storing a processor-executable program, which, when executed by the processor, is used to implement the above-described method.

[0018] On the other hand, embodiments of this application also provide a computer program product, which includes a computer program stored in a computer-readable storage medium. The processor of an electronic device reads the computer program from the computer-readable storage medium and executes the computer program, causing the electronic device to perform the method described above.

[0019] The advantages and beneficial effects of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application: This application discloses a method and related equipment for analyzing and optimizing loader loading operations. The method acquires material weight data and operational data of the loader under a target loading type, as well as an efficiency data set corresponding to the target loading type and operational parameters corresponding to the operational data. The efficiency data set includes several material efficiency data points, each representing the loading efficiency parameter of the corresponding material type at different loading heights. Based on the operational data and the efficiency data set, the method performs type analysis on the material weight data to obtain the target material category; it performs stage analysis on the operational data to obtain the target loading stage; and it analyzes and optimizes the operational parameters based on the target material category, the target loading stage, and the operational data. This method optimizes the loader's operational operations during loading by performing type and stage analysis on the acquired material weight data and operational data, and by combining the obtained target material category and target loading stage with the operational data. This improves the precision of loader loading operation energy consumption analysis and the timeliness of operation optimization. Attached Figure Description

[0020] Figure 1 This is a flowchart illustrating an analysis and optimization method for loader loading operations provided in an embodiment of this application; Figure 2 This is a schematic flowchart of a target loading type provided in an embodiment of this application; Figure 3 This is a detailed flowchart of step S120 provided in an embodiment of this application; Figure 4 This is a detailed flowchart of step S330 provided in an embodiment of this application; Figure 5 This is a detailed flowchart of step S140 provided in an embodiment of this application; Figure 6 This is a detailed flowchart of step S530 provided in an embodiment of this application; Figure 7 This is a detailed flowchart of step S540 provided in an embodiment of this application; Figure 8 This is a detailed flowchart of step S740 provided in an embodiment of this application; Figure 9 This is a schematic diagram of the framework of an analysis and optimization system for loader loading operations provided in an embodiment of this application; Figure 10This is a schematic diagram of the hardware structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the reference to "embodiment" herein means that a specific feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent 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. The implementation methods described in the following exemplary embodiments do not represent all implementation methods consistent with the embodiments of this application; they are merely examples of apparatuses and methods consistent with some aspects of the embodiments of this application as detailed in the appended claims.

[0022] It is understood that the terms “first,” “second,” etc., used in this application may be used to describe various concepts herein, but unless otherwise specified, these concepts are not limited by these terms. These terms are only used to distinguish one concept from another. For example, without departing from the scope of the embodiments of this application, a first loss function may also be referred to as a second loss function, and similarly, a second loss function may also be referred to as a first loss function. Depending on the context, the words “if” or “when” as used herein may be interpreted as “when…” or “in response to determination.”

[0023] As used in this application, the terms "at least one", "multiple", "each", "any", etc., "at least one" includes one, two or more, "multiple" includes two or more, "each" refers to each of the corresponding multiples, and "any" refers to any one of the multiples.

[0024] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0025] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0026] The following is an explanation of the technical terms used in this application: A loader is a type of earthmoving machinery widely used in construction projects such as highways, railways, buildings, hydropower, ports, and mines. It is mainly used for loading and unloading bulk materials such as soil, sand, gravel, lime, and coal, and can also perform light digging operations on ores and hard soil. By changing different auxiliary working devices, it can also perform bulldozing, lifting, and loading and unloading operations of other materials (such as timber).

[0027] Currently, the relevant technologies typically involve setting up various simple sensors on the loader and manually analyzing energy consumption and fine-tuning the control operation of the loader's loading operation to optimize the loading operation. However, this method has limited precision in energy consumption analysis and the timeliness of operation optimization is unsatisfactory.

[0028] Furthermore, some related technologies introduce intelligent monitoring functions into loaders, such as collecting loader status data via CAN bus or using simple algorithms to provide early warnings of potential malfunctions during loader loading operations. However, this approach often focuses on equipment monitoring and management rather than operational optimization, and it cannot handle multivariate data (such as comprehensive analysis of acceleration, load, and speed).

[0029] Furthermore, some related technologies use computer vision to detect material categories. To meet the computational resource requirements of machine learning models, this method often requires additional cameras and computing units mounted on the loader, resulting in high costs. In practical applications, loader loading operations often occur in complex environments (such as dim lighting, excessively bright surroundings, and dust generated during loading) and are easily affected by vibrations during loading. This often leads to poor image quality from the cameras (such as blurry images and low differentiation between targets), resulting in low accuracy in material category identification and limited applicability to loader loading operations.

[0030] It should be noted that the aforementioned related technologies are only used to assist in understanding the technical solutions of this application and do not mean that they belong to the publicly disclosed prior art.

[0031] In view of this, this application proposes an analysis and optimization method and related equipment for loader loading operations. The method performs type and stage analysis on the acquired material weight data and operation data, and optimizes the operation of the loader during the loading process based on the obtained target material category and target loading stage, combined with the operation data. This can improve the precision of the energy consumption analysis of loader loading operations and improve the timeliness of operation optimization.

[0032] Furthermore, this method identifies material categories by analyzing the type of material weight data, specifically through feature parameters associated with material weight. This approach does not require additional camera devices or computing units and has low computational resource requirements, which helps reduce the cost of material identification. In addition, it can avoid the influence of complex environmental factors on material identification, ensuring the accuracy of material identification while being highly applicable to loader loading operations.

[0033] Furthermore, this method, through multidimensional analysis of stage data, can determine the stage energy consumption and optimization time points of the loader during loading operations, and optimize the current operating parameters of the loader by combining the best operating parameters. It can fully process the multivariate data of the loader during loading operations and achieve operation optimization, effectively ensuring the effect of operation optimization.

[0034] The method provided in this application can be applied to a terminal, a server, or software running on a terminal or server. In some embodiments, the terminal can be a smartphone, tablet, laptop, desktop computer, smart speaker, smartwatch, or in-vehicle terminal, but is not limited thereto; the server can be configured as an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms. The server can also be a node server in a blockchain network; the software can be an application implementing the method, but is not limited to the above forms.

[0035] This application can be used in a wide variety of general-purpose or special-purpose computer system environments or configurations. Examples include: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, and distributed computing environments including any of the above systems or devices. This application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform specific tasks or implement specific abstract data types. This application can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0036] Reference Figure 1 , Figure 1This is an optional flowchart illustrating an analysis and optimization method for loader loading operations provided in this application embodiment, which may include, but is not limited to, steps S110 to S140.

[0037] Step S110: Obtain material weight data and operation data of the loader under the target loading type, as well as efficiency data group corresponding to the target loading type and operation parameters corresponding to the operation data. The efficiency data group includes several material efficiency data, and each material efficiency data is used to characterize the loading efficiency parameters of the corresponding type of material at different loading heights. In the embodiments of this application, the target loading type can be the specific loading type of the loader in the loading operation process. Specifically, the loading type of the loader includes common types such as V-type loading and I-type loading; while V-type loading includes the pre-loading stage, loading stage, backward transport stage, forward transport stage and unloading stage; I-type loading includes the pre-loading stage, loading stage, backward transport stage and unloading stage.

[0038] Material weight data can be the weight of the loader bucket during the loading operation, which can be obtained by weighing sensors; while operation data can be data collected by various sensors during the loading operation, such as the deceleration data of the loader during the loading operation collected by an acceleration sensor, the voltage and current data of the loader engine during the loading operation collected by a voltage / current sensor, and the actual torque and speed of the engine collected by a force sensor and a speed sensor.

[0039] It is understandable that the operation parameters can be those provided by the operator to the loader through physical hardware during the loading operation. For example, the operator provides the loader with parameters such as boom lifting, bucket tilting, and steering through physical hardware such as control handles and / or pedals.

[0040] For ease of understanding, the following embodiments of this application will take Type I shovel loading as an example. The material type can be classified by type, such as soil, sand, lime, ore, etc.; or it can be classified by particle size, such as classifying materials with a diameter greater than 20mm as boulders and materials with a diameter between 5mm and 20mm as crushed stone, etc.

[0041] The efficiency data set may include material efficiency data for a corresponding type of material under type I loading. This material efficiency data consists of loading efficiency parameters for the corresponding type of material at different loading heights. Specifically, these loading efficiency parameters may include loading time, loading weight, loading weight change rate, ideal energy consumption, etc. This material efficiency data can be easily extracted from the loader's historical operational data; this application does not restrict the method of acquisition.

[0042] In practical applications, during loading operations, a loader can scoop material into the bucket based on a first boom height and unload the material to the target location using a second boom height. In one optional implementation, the loading height can be the first boom height of the loader during the loading operation; or, in another feasible implementation, the loading height can be a combination of the first boom height and the second boom height of the loader during the loading operation. For ease of understanding, the embodiments of this application will subsequently use the first boom height as an example.

[0043] Reference Figure 2 In some embodiments, the target loading type is determined by the following steps: Step S210: Obtain the trained classification model; Step S220: Input the job data into the trained classification model to classify the jobs and obtain the job classification results; Step S230: If the operation classification result is that the loader is performing a loading operation, then the loading type is identified in the operation data to obtain the target loading type.

[0044] In this embodiment, a classification model can be trained based on historical operation data of the loader during loading and unloading operations. The classification model can be any of the following: decision tree model, etc., to obtain a trained classification model. There are various training methods for the specific model, which will not be elaborated here.

[0045] Job classification can be achieved by inputting the current operation data of the loader during loading into a trained classification model. The trained classification model then determines whether the current operation data belongs to a loading operation, thus obtaining the job classification result output by the trained classification model. Specifically, if the job classification result indicates that the loader is performing a loading operation, the target loading type can be identified from the operation data. This loading type identification can be obtained by outputting the target loading type along with the job classification result from the trained classification model; alternatively, the target loading type can be identified using an additional trained classifier.

[0046] In another feasible implementation, if the operation data detects that the operation sequence in the most recent operation cycle during the loading operation is loader moving forward at low speed, loader loading, loader reversing, and loader directly unloading, the loading type of the operation data can be determined as Type I loading. The low-speed forward movement can be determined by detecting whether the forward speed of the loader in the operation data is greater than 0 and less than a preset low-speed threshold, which can be any one of 0.5 m / s, 1 m / s, etc. The loader loading can be determined by detecting whether the weight in the loader bucket increases. The loader reversing and loader directly unloading, as well as the target loading type being Type V loading, can be deduced by analogy, similar to the above.

[0047] Step S120: Based on the operation data and the efficiency data group, perform type analysis on the material weight data to obtain the target material category; In this embodiment of the application, the weight data of the material can be analyzed based on the loader's operation data on the material and a pre-built efficiency data set to determine the category of the material, which is then denoted as the target material category.

[0048] Reference Figure 3 In some embodiments, the step of performing type analysis on the material weight data based on the efficiency data set to obtain the target material category includes: Step S310: Based on the loading height in the operation data, filter the efficiency data group to obtain several first target efficiency data, and the loading height corresponding to each first target efficiency data is the same as the loading height in the operation data. Step S320: Analyze the loading parameters of the material weight data to obtain loading characteristic parameters; Data filtering can be based on the loading height in the operation data, filtering out several material efficiency data with the same loading height from the efficiency data group, and recording them as the first target efficiency data; In one feasible implementation, loading parameter analysis can be a loading coarse screening parameter that analyzes the actual weight change rate, actual weight difference, and actual weight mean of material weight data during the loading operation, and determines the actual weight mean as the loading characteristic parameter, and a loading fine screening parameter that determines the loading characteristic parameter by the actual weight difference and / or actual weight change rate.

[0049] In practical applications, after a loader loads material, the boom height is often adjusted several times during material transport. Since the loader's load cells typically calculate material weight by detecting boom cylinder pressure, and different boom heights require different cylinder pressures for the same weight of material, the actual weight change rate can be the ratio between the weight difference at all boom heights and the material weight detected at the lowest boom height. Specifically, the weight difference can be the difference between the material weight detected at the highest and lowest boom heights. The actual weight difference can be the absolute difference between the material weight detected at the highest and lowest boom heights; and the average actual weight can be the average of the material weights detected at different boom heights.

[0050] In another optional implementation, the loading efficiency parameter can be a parameter related to loading energy consumption. The corresponding loading parameter analysis can be a coarse screening parameter for loading that analyzes the loading characteristic parameters during the loading operation, which corresponds to the material weight data, and a fine screening parameter for loading that determines the actual energy consumption difference and / or the actual energy consumption change rate as the loading characteristic parameters.

[0051] Specifically, in practical applications, the required cylinder pressure varies depending on the boom height for the same weight of material. Furthermore, the loader's hydraulic system requires a hydraulic pump to continuously maintain cylinder pressure to overcome the gravitational torque generated by the material and the boom's own weight. This results in different energy consumption rates for the same weight of material at different boom heights. The method for obtaining the actual energy consumption change rate is similar to that for obtaining the actual weight change rate; similarly, the methods for obtaining the actual energy consumption difference and the actual energy consumption average are similar to those for obtaining the actual weight difference and the actual weight average, and can be easily deduced by analogy.

[0052] Step S330: Based on the loading characteristic parameters, perform data matching on all the first target efficiency data to obtain the target material category.

[0053] Reference Figure 4 Furthermore, the step of matching all the first target efficiency data according to the loading characteristic parameters to obtain the target material category includes: Step S410: Obtain the shovel loading coarse screen parameters and shovel loading fine screen parameters according to the shovel loading characteristic parameters; Step S420: According to the shovel-loaded coarse screening parameters, perform coarse screening matching on all the first target efficiency data to obtain a number of second target efficiency data; the second target efficiency data is the first target efficiency data in all the first target efficiency data where the deviation between the coarse efficiency parameters and the shovel-loaded coarse screening parameters is less than a preset threshold. Step S430: According to the shovel-loaded fine screen parameters, perform fine screen matching on all the fine efficiency parameters in the second target efficiency data to obtain several fine screen matching degrees, and each fine screen matching degree corresponds to one second target efficiency data. Step S440: Determine the target material category based on the second target efficiency data corresponding to the target matching degree; the target matching degree is the largest fine screen matching degree among all the fine screen matching degrees.

[0054] For ease of understanding, the embodiments of this application will subsequently use the loading efficiency parameter as a characteristic parameter related to the loading weight as an example. The content of the loading efficiency parameter as a characteristic parameter related to loading energy consumption is similar to that of the loading efficiency parameter as a characteristic parameter related to the loading weight, and can be easily deduced by analogy. This application will not repeat it here.

[0055] In this embodiment of the application, coarse screening matching may involve comparing the actual weight average, a coarse screening parameter, with the standard weight average, a coarse efficiency parameter, in each first target efficiency data. The first target efficiency data with a mean deviation less than a preset deviation threshold (such as 0.03, 0.05, etc.) is determined as the second target efficiency data. The standard weight average in the first target efficiency data is obtained in a similar way to the actual weight average.

[0056] Fine screening matching can be achieved by calculating the Euclidean distance between the shovel-loaded fine screening parameters and the fine efficiency parameters, and by determining the fine screening matching degree between the shovel-loaded fine screening parameters and each fine efficiency parameter based on the inverse relationship between the matching degree and the Euclidean distance; wherein, the fine efficiency parameters include the standard weight difference and / or the standard weight change rate, and their acquisition method is similar to that of the shovel-loaded fine screening parameters.

[0057] Understandably, the maximum value can be filtered for all fine screen matching degrees to determine the largest fine screen matching degree among all fine screen matching degrees as the target matching degree. Then, based on the material type corresponding to the second target efficiency data corresponding to the target matching degree, the target material category can be obtained.

[0058] Step S130: Perform stage analysis on the operation data to obtain the target loading stage; In practical applications, stage analysis can be used to determine the operational stage of the data at the current point in time within real-time updated operational data, thereby identifying the target loading stage. For example, for type I loading, if the operational stage of the data at the current point in time is loader loading, then loader loading can be identified as the target loading stage.

[0059] Step S140: Analyze and optimize the operation parameters based on the target material category, the target loading stage, and the operation data.

[0060] In this embodiment of the application, the operation parameters provided by the operator to the loader can be analyzed based on the target material category, the target loading stage and the operation data, and the operation parameters can be optimized based on the analysis results to achieve real-time operation optimization of the loader in the loading process.

[0061] Reference Figure 5 In some embodiments, the step of analyzing and optimizing the operation parameters based on the target material category, the target loading stage, and the operation data includes: Step S510: Obtain the optimal operating parameters corresponding to the target material category; Step S520: According to the target loading stage, the operation data is filtered to obtain the stage data corresponding to the target loading stage. The stage data is used to characterize the operation data between the stage start time point and the stage end time point of the target loading stage. In this embodiment of the application, the optimal operating parameters can be pre-set operating parameters or the optimal operating parameters of the loader during historical loading operations. The optimal operating parameters record the optimal driving speed sequence and action sequence of the loader during loading operations. The action sequence includes common actions of the loader such as bucket leveling, boom lifting, and boom lowering.

[0062] Data filtering can extract the loader's operational data during the target loading stage from real-time updated, time-series operational data, and record it as stage data; the stage start time point can be the start time point of the target loading stage, and the stage end time point can be the end time point of the target loading stage.

[0063] Step S530: Perform multidimensional analysis on the stage data to obtain the stage energy consumption and several optimization time points of the target loading stage; Reference Figure 6 Furthermore, multidimensional analysis is performed on the stage data to obtain the stage energy consumption and several optimization time points for the target loading stage, including: Step S610: Obtain the deceleration threshold; Step S620: Based on the stage data, obtain the first deceleration data and stage load data at each stage time point; Step S630: Perform energy consumption analysis on all the stage load data to obtain the stage energy consumption; Step S640: According to the deceleration threshold, perform threshold analysis on all the first deceleration data to obtain a number of second deceleration data. The second deceleration data are the first deceleration data that is greater than or equal to the deceleration threshold among all the first deceleration data. Step S650: Determine the stage time point of each of the second deceleration data as an optimization time point.

[0064] In this embodiment, the deceleration threshold is used to characterize the maximum deceleration of the loader during the target loading phase. Its specific value can be any one of 2 meters per second, 3 meters per second, etc. The phase time point can be the time point of the phase data on the time axis. The first deceleration data can be obtained simply by acquiring the acceleration data of the loader at the corresponding phase time point in the phase data, and then using the inverse relationship between acceleration and deceleration in the vector direction. The phase load data can be the current and voltage data of the loader motor, or the torque and speed data of the loader engine.

[0065] For stage load data, if the stage load data consists of the loader motor's current and voltage data, the power at each stage time point can be easily calculated based on the current and voltage data. Then, within the range from the stage start time point to the stage end time point, all power values ​​are integrated to obtain the loader's stage energy consumption under the target loading stage. The stage load data, which consists of the loader engine's torque and speed data, can be derived similarly.

[0066] For any given first deceleration data point, threshold analysis can be performed by comparing the magnitude of the deceleration threshold with that first deceleration data point. Specifically, if the first deceleration data point is less than the deceleration threshold, it is determined that the loader's travel speed at that stage time point is appropriate; or, if the first deceleration data point is greater than or equal to the deceleration threshold, it indicates that there may be at least one of the following situations: the loader may brake suddenly, the loader's current travel speed may be too high, or the impact of the loader loading may be high, leading to safety hazards. In this case, it is determined that the loader's travel speed at that stage time point is inappropriate, the first deceleration data point is determined as the second deceleration data point, and the corresponding stage time point is determined as an optimized time point.

[0067] Step S540: Analyze and optimize the operation parameters based on the stage energy consumption, the optimal operating parameters, and all the optimized time points.

[0068] Reference Figure 7 Furthermore, the step of analyzing and optimizing the operation parameters based on the stage energy consumption, the optimal operating parameters, and all the optimized time points includes: Step S710: Obtain the stage energy consumption ratio corresponding to the stage energy consumption, and the stage loading efficiency corresponding to the stage start time and stage end time. Step S720: Based on the standard ton energy consumption ratio and standard loading efficiency corresponding to the optimal operating parameters, perform a data superiority-inferiority analysis on the stage ton energy consumption ratio and the stage loading efficiency to obtain the superiority-inferiority analysis results; the superiority-inferiority analysis results are used to characterize whether the operating parameters are better than the optimal operating parameters. Step S730: If the result of the performance analysis is that the operation parameters are better than the optimal operation parameters, then the optimal operation parameters are updated according to the operation parameters. In the embodiments of this application, the stage ton energy consumption ratio is the ratio of loading weight to loading energy consumption, which is used to reflect energy utilization efficiency. The loading weight can be the weight when the loader shovels the material into the bucket, or it can be the average of the aforementioned actual weights; while the loading energy consumption can be the stage energy consumption of the target loading stage, or it can be the sum of the energy consumption of the target loading stage and all stages before the target loading stage.

[0069] Stage loading efficiency can be the ratio of loading weight to loading time, where loading time can be the absolute time difference between the start and end times of the stage; or, it can be the absolute time difference between the start time of the loader's loading operation and the end time of the target loading stage. Standard ton energy consumption ratio and standard loading efficiency can be preset by operators or calculated by averaging the ton energy consumption ratio and stage loading efficiency of the loader over several historical stages in the same loading phase.

[0070] Data performance analysis can compare the relationship between the stage energy consumption ratio and the standard energy consumption ratio, as well as the relationship between the stage loading efficiency and the standard loading efficiency. When the stage energy consumption ratio is less than the standard energy consumption ratio and the stage loading efficiency is less than the standard loading efficiency, a performance analysis result is generated indicating that the operation parameters are better than the optimal operation parameters. Based on the operation parameters, the optimal operation parameters are then replaced and updated, and the updated optimal operation parameters are used in the next loading operation. Conversely, a performance analysis result is generated indicating that the operation parameters are not better than the optimal operation parameters.

[0071] Alternatively, in step S740, if the result of the performance analysis is that the operation parameters are not better than the optimal operation parameters, then the operation parameters are analyzed and optimized based on the optimal operation parameters and all the optimization time points.

[0072] Reference Figure 8 Furthermore, the step of analyzing and optimizing the operation parameters based on the optimal operation parameters and all the optimized time points includes: Step S810: Obtain the target time point, which is the optimization time point among all optimization time points that has not undergone difference analysis; Step S820: Based on the target time point, perform an operational parameter difference analysis on the optimal operating parameters and the operation parameters to obtain the difference optimization parameters; Step S830: Optimize and adjust the operation parameters according to the difference optimization parameters.

[0073] In this embodiment, the analysis and optimization of the operation parameters can be achieved through a loop. For any given loop, an optimization time point that has not undergone difference analysis can be selected from all optimization time points and denoted as the target time point.

[0074] Operational parameter difference analysis can be performed by obtaining the optimal operational parameters and the specific operational parameters at the target time point. These specific operational parameters can be any of the following: the loader's travel speed, motion parameters, etc. The motion parameters can be parameters related to the loader's actions, such as the lifting parameters of the loader's boom (such as lifting height, lifting rate, etc.). Then, a difference analysis algorithm (such as regression analysis) is used to analyze the deviation between the two specific operational parameters to obtain the difference optimization parameters. For example, for the specific operational parameter of travel speed, the speed difference between the two travel speeds can be calculated through regression analysis, and this speed difference can be recorded as the difference optimization parameter at the travel speed level.

[0075] Understandably, optimization and adjustment can involve adding and updating the difference optimization parameters to the operation parameters. This addition and update can be achieved by summing the deviations of each specific operation parameter in the operation parameters to obtain the updated operation parameters, and then controlling the loader to execute the updated operation parameters.

[0076] On the other hand, please see Figure 9 This application also provides an analysis and optimization system for loader loading operations, including: The first processing unit 901 is used to acquire material weight data and operation data of the loader under the target loading type, as well as efficiency data group corresponding to the target loading type and operation parameters corresponding to the operation data. The efficiency data group includes several material efficiency data, and each material efficiency data is used to characterize the loading efficiency parameter of the corresponding type of material at different loading heights. The second processing unit 902 is used to perform type analysis on the material weight data based on the operation data and the efficiency data group to obtain the target material category; The third processing unit 903 is used to perform stage analysis on the operation data to obtain the target loading stage; The fourth processing unit 904 is used to analyze and optimize the operation parameters based on the target material category, the target loading stage, and the operation data.

[0077] It is understood that the content of the above method embodiments is applicable to this system embodiment. The specific functions implemented in this system embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

[0078] This application also provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the above-described method. This electronic device can be any smart terminal, including tablet computers, in-vehicle computers, etc.

[0079] It is understood that the content of the above method embodiments is applicable to this device embodiment. The specific functions implemented by this device embodiment are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0080] Please see Figure 10 , Figure 10 This illustration shows the hardware structure of an electronic device according to one embodiment, including: The processor 1001 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this application. The memory 1002 can be implemented as a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 1002 can store the operating system and other applications. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 1002 and is called and executed by the processor 1001. Input / output interface 1003 is used to implement information input and output; The communication interface 1004 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.). Bus 1005 transmits information between various components of the device (e.g., processor 1001, memory 1002, input / output interface 1003, and communication interface 1004); The processor 1001, memory 1002, input / output interface 1003 and communication interface 1004 are connected to each other within the device via bus 1005.

[0081] Similarly, the content of the above method embodiments is applicable to this device embodiment. The specific functions implemented by this device embodiment are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0082] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method.

[0083] It is understood that the content of the above method embodiments is applicable to this storage medium embodiment. The specific functions implemented in this storage medium embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

[0084] This application also discloses a computer program product or computer program, which includes computer instructions stored in the aforementioned computer-readable storage medium; the processor of the aforementioned electronic device can read the computer instructions from the aforementioned computer-readable storage medium, and the processor executes the computer instructions, causing the electronic device to perform the aforementioned method embodiment.

[0085] It is understood that the content of the above method embodiments is applicable to this computer program product or computer program embodiment. The specific functions implemented by this computer program product or computer program embodiment are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0086] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0087] The embodiments described in this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.

[0088] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of this application, and may include more or fewer steps than shown, or combine certain steps, or different steps.

[0089] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0090] Those skilled in the art will understand that all or some of the steps in the methods disclosed above, as well as the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, or suitable combinations thereof.

[0091] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. 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 comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0092] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0093] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of the units described above is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0094] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0095] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0096] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes multiple instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing programs, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0097] The preferred embodiments of the present application have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present application. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and substance of the embodiments of the present application shall be within the scope of the claims of the present application.

Claims

1. A method for analyzing and optimizing loader loading operations, characterized in that, include: Acquire material weight data and operation data of the loader under the target loading type, as well as efficiency data group corresponding to the target loading type and operation parameters corresponding to the operation data. The efficiency data group includes several material efficiency data, each of which is used to characterize the loading efficiency parameter of the corresponding type of material at different loading heights. Based on the operation data and the efficiency data set, the material weight data is analyzed to obtain the target material category; The target loading stage is obtained by performing stage analysis on the operation data; The operation parameters are analyzed and optimized based on the target material category, the target loading stage, and the operation data.

2. The method according to claim 1, characterized in that, The step of performing type analysis on the material weight data based on the efficiency data set to obtain the target material category includes: Based on the loading height in the operation data, the efficiency data group is filtered to obtain several first target efficiency data, and the loading height corresponding to each first target efficiency data is the same as the loading height in the operation data. The material weight data is analyzed to obtain loading characteristic parameters. Based on the loading characteristic parameters, data matching is performed on all the first target efficiency data to obtain the target material category.

3. The method according to claim 2, characterized in that, The step of matching all the first target efficiency data according to the loading characteristic parameters to obtain the target material category includes: Based on the aforementioned loading characteristic parameters, the loading coarse screen parameters and loading fine screen parameters are obtained; Based on the shovel-loaded coarse screening parameters, coarse screening matching is performed on the coarse efficiency parameters in all the first target efficiency data to obtain a number of second target efficiency data; the second target efficiency data is the first target efficiency data in all the first target efficiency data whose deviation from the coarse efficiency parameters and the shovel-loaded coarse screening parameters is less than a preset threshold. Based on the shovel-loaded fine screen parameters, fine screen matching is performed on all the fine efficiency parameters in the second target efficiency data to obtain several fine screen matching degrees, and each fine screen matching degree corresponds to one second target efficiency data. The target material category is determined based on the second target efficiency data corresponding to the target matching degree; the target matching degree is the largest fine screen matching degree among all the fine screen matching degrees.

4. The method according to claim 1, characterized in that, The step of analyzing and optimizing the operation parameters based on the target material category, the target loading stage, and the operation data includes: Obtain the optimal operating parameters corresponding to the target material category; Based on the target loading stage, the operation data is filtered to obtain stage data corresponding to the target loading stage. The stage data is used to characterize the operation data between the stage start time point and the stage end time point of the target loading stage. Multidimensional analysis of the stage data yields the stage energy consumption and several optimization time points for the target loading stage. The operation parameters are analyzed and optimized based on the stage energy consumption, the optimal operating parameters, and all the optimized time points.

5. The method according to claim 4, characterized in that, Multidimensional analysis of the stage data yields the stage energy consumption and several optimization time points for the target loading stage, including: Obtain the deceleration threshold; Based on the stage data, obtain the first deceleration data and stage load data at each stage time point; Energy consumption analysis is performed on all the load data of the aforementioned stages to obtain the energy consumption of the aforementioned stages; Based on the deceleration threshold, threshold analysis is performed on all the first deceleration data to obtain several second deceleration data. The second deceleration data is the first deceleration data that is greater than or equal to the deceleration threshold among all the first deceleration data. Each stage time point of the second deceleration data is determined as an optimization time point.

6. The method according to claim 4, characterized in that, The step of analyzing and optimizing the operation parameters based on the stage energy consumption, the optimal operating parameters, and all the optimized time points includes: Obtain the stage energy consumption ratio corresponding to the stage energy consumption, and the stage loading efficiency corresponding to the stage start time and stage end time. Based on the standard ton energy consumption ratio and standard loading efficiency corresponding to the optimal operating parameters, a data superiority-inferiority analysis is performed on the stage ton energy consumption ratio and the stage loading efficiency to obtain the superiority-inferiority analysis results; the superiority-inferiority analysis results are used to characterize whether the operating parameters are better than the optimal operating parameters. If the result of the performance analysis is that the operation parameters are better than the optimal operation parameters, then the optimal operation parameters are updated according to the operation parameters; or, if the result of the performance analysis is that the operation parameters are not better than the optimal operation parameters, then the operation parameters are analyzed and optimized according to the optimal operation parameters and all the optimization time points.

7. The method according to claim 6, characterized in that, The step of analyzing and optimizing the operation parameters based on the optimal operation parameters and all the optimized time points includes: Obtain the target time point, which is the optimization time point among all optimization time points that has not undergone difference analysis; Based on the target time point, a difference analysis of the operating parameters is performed on the optimal operating parameters and the operation parameters to obtain the difference optimization parameters; Based on the aforementioned difference optimization parameters, the operation parameters are optimized and adjusted.

8. The method according to any one of claims 1-7, characterized in that, The target loading type is determined through the following steps: Obtain the trained classification model; The job data is input into the trained classification model to classify the jobs and obtain the job classification results. If the operation classification result indicates that the loader is performing a loading operation, then the loading type is identified by performing loading type identification on the operation data to obtain the target loading type.

9. An analysis and optimization system for loader loading operations, characterized in that, include: The first processing unit is used to acquire material weight data and operation data of the loader under the target loading type, as well as efficiency data group corresponding to the target loading type and operation parameters corresponding to the operation data. The efficiency data group includes several material efficiency data, and each material efficiency data is used to characterize the loading efficiency parameter of the corresponding type of material at different loading heights. The second processing unit is used to perform type analysis on the material weight data based on the operation data and the efficiency data group to obtain the target material category; The third processing unit is used to perform stage analysis on the operation data to obtain the target loading stage; The fourth processing unit is used to analyze and optimize the operation parameters based on the target material category, the target loading stage, and the operation data.

10. An electronic device, characterized in that, include: At least one processor; At least one memory for storing at least one program; When the at least one program is executed by the at least one processor, the at least one processor implements the method as described in any one of claims 1 to 8.

11. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the method as described in any one of claims 1 to 8.

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