Method, device and equipment for acquiring and analyzing production and loading efficiency data of electric shovel

Abstract

The invention provides an electric shovel production and loading efficiency data acquisition and analysis method, device and equipment. The method comprises the following steps: acquiring electric shovel operation data in real time through a sensor integrated by data acquisition equipment; processing the operation data of the electric shovel in real time to obtain an efficiency index of the electric shovel in each operation link; the efficiency bottleneck of the electric shovel is analyzed according to the efficiency indexes, and time consumption abnormity in the operation process is recognized. According to the application, the data acquisition equipment integrated with various sensors is adopted to acquire various data in the operation process of the electric shovel in real time, and the acquired data is analyzed in real time in combination with the efficient data processing unit, so that the accurate evaluation of the operation efficiency of the electric shovel is realized. The time consumption of the operation links is calculated, the efficiency bottleneck in the operation is identified, the links with abnormal time consumption are found, and intelligent optimization suggestions are provided, so that fine management and continuous optimization of the electric shovel operation are realized, and the overall efficiency and production capacity of the mine operation are remarkably improved.

Description

Technical Field

[0001] This application belongs to the field of intelligent mining technology in open-pit mines, specifically, it relates to a method, device and equipment for data acquisition and analysis of electric shovel production efficiency. Background Technology

[0002] As a core mining equipment in open-pit mines, the efficiency of electric shovels directly affects the mine's production capacity and economic benefits. Traditional methods for statistically analyzing the efficiency of electric shovel operations rely on manual recording of hourly or shift-based loading volumes. This data collection method is crude and lagging, unable to achieve real-time, refined operational analysis and optimization. While existing technologies can provide some macro-level efficiency data, they cannot accurately assess the time distribution and performance bottlenecks of each operational stage, such as digging, rotating, unloading, and returning. Especially in the absence of simultaneous collection of bucket motion parameters (such as rotation speed, acceleration, and angle) and blast pile morphology data, it is impossible to accurately analyze every detail of the loading process, leading to distorted efficiency assessments. Furthermore, the problem of data silos has long existed between blasting and loading operations. The evaluation of blasting effects relies heavily on manual experience, making it difficult to establish a quantitative correlation with the loading process, thus hindering effective collaborative optimization of the two stages. Although some mines have attempted to introduce remote control and intelligent systems (such as sensor networks and 5G technology), these systems are mostly focused on equipment status monitoring and basic scheduling, lacking fusion analysis of multi-dimensional data. Especially in the area of ​​coordinating and optimizing loading efficiency and blasting effects, existing systems still cannot effectively solve the problem of real-time analysis and optimization. Therefore, how to achieve refined diagnosis of loading efficiency and blasting-loading coordination optimization through the integration of multiple sensors for real-time data acquisition and intelligent analysis has become a key requirement for improving the level of intelligence in mine operations. Summary of the Invention

[0003] This application provides a method, apparatus, and equipment for collecting and analyzing data on the production efficiency of electric shovels, in order to at least solve the problems of crude statistical analysis, data lag, and lack of real-time refined analysis in the prior art regarding the efficiency of electric shovel operations.

[0004] According to the first aspect of this application, a method for collecting and analyzing data on the production efficiency of electric shovels is provided, comprising: Data on electric shovel operation is collected in real time through sensors integrated into the data acquisition device; The electric shovel's operational data is processed in real time to obtain efficiency indicators for each operational stage. Analyze the efficiency bottlenecks of electric shovels based on efficiency indicators and identify abnormal time consumption during operation.

[0005] In one embodiment, the electric shovel's operational data is processed in real time to obtain efficiency indicators for each operational stage, including: Denoising and filtering are performed on the electric shovel operation data; The time consumption of each operation step is calculated based on the processed electric shovel operation data, and the efficiency index of each operation step is calculated in combination with the preset theoretical standard efficiency.

[0006] In one embodiment, analyzing the efficiency bottleneck of the electric shovel based on efficiency indicators and identifying abnormal time consumption during operation includes: By comparing the efficiency indicators with the preset standard efficiency indicators, the bottleneck in efficiency can be identified.

[0007] Identify the factors that cause the efficiency bottleneck based on the stage in which it occurs.

[0008] In one embodiment, the efficiency index of each work step is calculated based on a preset theoretical standard efficiency, including: By comparing the time consumed with the theoretical standard efficiency, the efficiency index of each work step is obtained.

[0009] In one embodiment, comparing the efficiency index with a preset standard efficiency index to determine the stage where the efficiency bottleneck is located includes: The efficiency indicators are compared with the preset standard efficiency indicators to obtain the comparison results; The bottleneck in efficiency is identified by comparing the results with the standard efficiency indicators.

[0010] In one embodiment, the method for collecting and analyzing electric shovel production efficiency data further includes: Based on the analysis results, adjustments were made to the operation of the electric shovel, including adjusting the rotation speed of the electric shovel.

[0011] According to another aspect of this application, an electric shovel production efficiency data acquisition and analysis device is also provided, comprising: The data acquisition unit is used to collect electric shovel operation data in real time through the sensors integrated into the data acquisition device; The efficiency index calculation unit is used to process the electric shovel operation data in real time and obtain the efficiency index of the electric shovel at each operation stage. The analysis unit is used to analyze the efficiency bottlenecks of the electric shovel based on efficiency indicators and identify abnormal time consumption during operation.

[0012] According to a third aspect of this application, an electric shovel production efficiency data acquisition and analysis device is also provided, used to perform an electric shovel production efficiency data acquisition and analysis method, including: The data acquisition equipment integrates multiple sensors for real-time acquisition of electric shovel operation data. The sensors include motion sensors, accelerometers, gyroscopes, cameras, and laser scanners. The data processor, connected to the data acquisition equipment, is used to process the acquired electric shovel operation data in real time, calculate the time consumption of each operation stage, and calculate the efficiency index of each operation stage based on the preset theoretical standard efficiency. The calculator, connected to the data processor, is used to analyze efficiency bottlenecks in the electric shovel operation process based on efficiency indicators and identify work steps with abnormal time consumption. The monitor and controller are used to display efficiency indicators, bottleneck analysis results, and optimize electric shovel operation, and allow users to adjust operating parameters through interface input.

[0013] This application employs a data acquisition device integrating multiple sensors to collect various data in real time during electric shovel operations, including bucket movement data, operation time, and blasting patterns. Combined with a high-efficiency data processing unit, the collected data is analyzed in real time, enabling precise evaluation of electric shovel operation efficiency. By calculating the time consumption of each operational stage (such as digging, rotating, unloading, and empty return) and comparing it with a preset standard efficiency model, this application can accurately identify efficiency bottlenecks and promptly detect stages with abnormal time consumption. Furthermore, by analyzing efficiency indicators and bottleneck factors at each stage, intelligent optimization suggestions are provided, thereby achieving refined management and continuous optimization of electric shovel operations, significantly improving the overall efficiency and production capacity of mining operations. Attached Figure Description

[0014] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0015] Figure 1 A flowchart illustrating a method for collecting and analyzing data on the production efficiency of an electric shovel, as provided in this application.

[0016] Figure 2 This is a flowchart illustrating the calculation method of the efficiency index of the electric shovel in each working stage in the embodiments of this application.

[0017] Figure 3 This is a flowchart of a method for identifying time consumption anomalies during a job operation, as described in this application.

[0018] Figure 4 This is a flowchart illustrating the method for determining the stage where the efficiency bottleneck is located in an embodiment of this application.

[0019] Figure 5 The structural block diagram of an electric shovel production efficiency data acquisition and analysis device provided in this application.

[0020] Figure 6 This is a specific implementation of an electronic device in the embodiments of this application. Detailed Implementation

[0021] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0022] As a core mining equipment in open-pit mines, the working efficiency of electric shovels directly affects the mine's production capacity and economic benefits. Traditional methods for statistically analyzing the efficiency of electric shovel operations rely on manual recording of hourly or shift-based loading volumes. This data collection method is crude and outdated, failing to enable real-time, refined operational analysis and optimization.

[0023] To address this issue, this application first provides a method for collecting and analyzing data on the production efficiency of electric shovels, such as... Figure 1 As shown, it includes: S101: Real-time acquisition of electric shovel operation data via sensors integrated into the data acquisition device; S102: Process the electric shovel's operating data in real time to obtain the efficiency index of the electric shovel at each operating stage; S103: Analyze the efficiency bottleneck of the electric shovel based on efficiency indicators and identify abnormal time consumption during operation.

[0024] By integrating multiple sensors onto the electric shovel, various data generated during operation are collected in real time. These sensors include motion sensors, accelerometers, gyroscopes, high-definition cameras, and laser scanners. The data acquisition equipment acquires real-time data on the bucket's motion (such as rotation angle, speed, and acceleration), as well as information such as operating time and bucket load. This data is transmitted in real-time to the data processing unit, where it is processed to generate efficiency indicators for each stage of the electric shovel's operation (such as time consumption and efficiency levels for digging, rotating, unloading, and empty return). This data allows for real-time analysis of the operation process, enabling timely identification of efficiency bottlenecks and operational anomalies.

[0025] In one embodiment, the electric shovel's operational data is processed in real time to obtain efficiency indicators for each operational stage, such as... Figure 2 As shown, it includes: S201: Perform noise reduction and filtering on electric shovel operation data; S202: Calculate the time consumption of each operation step based on the processed electric shovel operation data, and calculate the efficiency index of each operation step in combination with the preset theoretical standard efficiency.

[0026] To improve the accuracy of data analysis, in this embodiment, the collected electric shovel operation data will first undergo denoising and filtering. Denoising can be performed using low-pass filters (such as moving average filtering or Kalman filtering) to remove high-frequency noise while retaining useful low-frequency signals, thus ensuring the smoothness and accuracy of the analyzed data. After filtering, the data processing unit calculates the time consumption for each operation stage (such as digging, rotating, and unloading the bucket) based on the processed data. After calculating the time consumption, the efficiency index for each operation stage is calculated by comparing it with a preset theoretical standard efficiency model. The theoretical standard efficiency is an ideal working time derived from industry standards, historical data, or experimental models, used to measure the actual efficiency of the electric shovel operation stages.

[0027] In one specific embodiment, the main purpose of filtering is to remove high-frequency noise and irrelevant interference signals from the original signal, while retaining useful low-frequency signals. The bucket's rotation angle and speed data are affected by the electric shovel's movement, vibration, and other equipment interference, which typically manifest as high-frequency noise. Filtering can effectively extract the main signals reflecting the bucket's operating status, improving the accuracy of data analysis.

[0028] Based on the characteristics of electric shovel operation data, it is recommended to use a low-pass filter or a Kalman filter to reduce interference from high-frequency noise such as vibration and movement. A low-pass filter can simply and effectively remove high-frequency noise, while a Kalman filter performs better in situations with complex signal noise or significant dynamic changes.

[0029] Low-pass filter: A low-pass filter allows low-frequency signals to pass through while suppressing high-frequency signals. This is because changes in the bucket's rotation angle and speed are primarily low-frequency signals, while disturbances such as vibration and movement are typically high-frequency signals. The frequency response of a low-pass filter attenuates high-frequency signals while keeping low-frequency signals unchanged. Digital filtering algorithms, such as moving average filtering or FIR (finite impulse response) filters, are typically used to smooth the data.

[0030] For discrete signals and output signal The basic form of a low-pass filter is: (0.1) in The size of the filtering window represents the range of data that the filter smooths. A larger window... This will make the filtering effect more obvious, but it may also cause a response delay.

[0031] Kalman filtering is a recursive filtering method suitable for signal estimation in dynamic systems, particularly for noise removal and signal smoothing. This method uses a state-space model for prediction and updating, and can adaptively adjust the filtering parameters according to the dynamic changes of the system. Based on the system's state and observation equations, and incorporating the statistical characteristics of process and observation noise, the Kalman filter calculates the optimal signal estimate. Through recursive calculation, the Kalman filter can effectively combine historical and new data for optimization, adapting to different noise conditions.

[0032] State prediction equation: (0.2) Observational update equation: (0.3) in, It is a predicted state. It is Kalman gain. These are observed values. It is the observation matrix. , It is the system model matrix. It controls the input.

[0033] In one embodiment, efficiency bottlenecks of the electric shovel are analyzed based on efficiency indicators to identify anomalies in time consumption during operation, such as... Figure 3 As shown, it includes: S301: Compare the efficiency indicators with the preset standard efficiency indicators to determine the stage where the efficiency bottleneck is located.

[0034] S302: Identify the factors that cause the efficiency bottleneck based on the stage in which the bottleneck is located.

[0035] In this embodiment, by comparing the calculated efficiency index with a preset standard efficiency index, it is possible to identify which operational processes have efficiency bottlenecks. The standard efficiency index is derived from theoretical models or historical data and represents the time consumption level of the operational process under ideal conditions. After comparing the efficiency index, if the time consumption of a certain operational process exceeds the standard efficiency, then that process can be identified as an efficiency bottleneck. Next, by analyzing the efficiency bottleneck of that process, the causes of the bottleneck can be further determined, such as factors like the looseness of the blast pile, an unsatisfactory bucket rotation angle, or equipment failure.

[0036] In one embodiment, the efficiency index of each work step is calculated based on a preset theoretical standard efficiency, including: By comparing the time consumed with the theoretical standard efficiency, the efficiency index of each work step is obtained.

[0037] In this embodiment, when calculating the efficiency index of each work step, the actual time consumption of the work step is compared with the theoretical standard efficiency. The theoretical standard efficiency is calculated based on the expected work time and efficiency model under optimal working conditions. By comparing, the efficiency index of each work step is obtained. If the efficiency of a certain step is lower than the standard value, it indicates that there is an efficiency bottleneck in that step. The efficiency index is calculated using the following formula:

[0038] in, T ideal For theoretical standard efficiency time, T actual This represents the actual time consumed.

[0039] In one embodiment, the efficiency index is compared with a preset standard efficiency index to determine the stage where the efficiency bottleneck is located, such as... Figure 4 As shown, it includes: S401: Compare the efficiency index with the preset standard efficiency index to obtain the comparison result; S402: Determine the stage where the efficiency bottleneck is located based on the difference between the comparison results and the standard efficiency indicators.

[0040] In this embodiment, the efficiency index of each operation step is first compared with a preset standard efficiency index. The standard efficiency index is usually derived from historical data or theoretical models and represents the ideal working time for electric shovel operations. The comparison results show the difference between the actual working time and the standard time. If the difference is too large, the step can be identified as an efficiency bottleneck. This method can quickly locate inefficient steps in electric shovel operations and further analyze the problems in those steps to ensure targeted optimization.

[0041] In one specific embodiment, firstly, the bucket's rotation angle and angular velocity are analyzed. Using rotation angle and velocity data collected by a three-axis gyroscope and accelerometer, combined with the work cycle time, the bucket's operating efficiency is calculated. Changes in rotation angle and velocity reflect the bucket's operating state, allowing for further analysis of the work efficiency of each stage.

[0042]

[0043] in, T digging , T rotation , T unloading , T return These represent the time for the excavation, rotation, bucket unloading, and empty return stages, respectively.

[0044] Calculate the bucket rotation per unit time based on the changes in operating time and bucket angle. and rotational speed Furthermore, quantitative indicators of bucket operation efficiency were derived.

[0045] (0.4) This formula characterizes the relationship between the bucket's rotation angle per unit time and the work cycle, which helps to determine whether there are delays or unevenness in the work.

[0046] Analysis of operation cycle time and load capacity: By analyzing the relationship between work cycle time and bucket load, the coordination between loading and transportation processes is assessed. Regression analysis is used to predict operational efficiency under different working conditions.

[0047]

[0048] in, α and β For regression coefficients, Q This refers to the load capacity.

[0049] Burst information analysis: Analysis of blast pile block size distribution and looseness: Analyze the impact of different blast pile conditions (such as block size ratio and looseness) on loading efficiency, and establish a correlation model between blasting quality and loading efficiency.

[0050]

[0051] in, D i For the first i Particle size, f i For the frequency corresponding to the particle size, n This represents the number of block-size intervals.

[0052] The relationship between loading efficiency and block size distribution can be established through regression analysis:

[0053] in, c 0, c 1, c 2, c 3 is the regression coefficient.

[0054] Looseness ( ρ The relationship between the time spent on each work step and the time spent on each step: The impact of looseness on the operational process can be quantified using a regression model:

[0055]

[0056] In one embodiment, the method for collecting and analyzing electric shovel production efficiency data further includes: Based on the analysis results, adjustments were made to the operation of the electric shovel, including adjusting the rotation speed of the electric shovel.

[0057] This embodiment further optimizes the operation of electric shovels after analyzing efficiency bottlenecks. Based on the identified efficiency bottlenecks and analysis results, the operating parameters of the electric shovel can be adjusted accordingly. Taking bucket rotation speed as an example, if the time consumption of the rotation process exceeds the standard efficiency, the operating efficiency can be optimized by adjusting the bucket rotation speed. Through this adjustment, not only can the efficiency of a single operation be improved, but the overall production capacity of the mining operation can also be enhanced.

[0058] In mining production, data from blasting and loading processes are often isolated and lack effective linkage. This application establishes a "blasting-loading" collaborative optimization model by collecting data from the blasting and loading processes in real time, achieving effective coordination between the two. By integrating real-time collected information such as blast pile morphology, block size distribution, and looseness with bucket operation data, the time consumption of each step is accurately calculated. By comparing operation data under different blast pile conditions, efficiency bottlenecks in the operation are identified, providing data support for adjusting front-end blasting parameters. Based on the data analysis results, optimization suggestions are proposed for blasting processes (such as orifice design and charge structure) and loading processes, achieving coordinated adjustment of blasting and loading to improve operational efficiency. This system enables real-time coordination between penetration and blasting operations and mining operations, providing refined control methods, reducing manual intervention, and improving production efficiency.

[0059] According to a second aspect of this application, an electric shovel production efficiency data acquisition and analysis device is also provided, used to perform the above-described electric shovel production efficiency data acquisition and analysis method, comprising: The data acquisition equipment integrates multiple sensors for real-time acquisition of electric shovel operation data. The sensors include motion sensors, accelerometers, gyroscopes, cameras, and laser scanners. The data processor, connected to the data acquisition equipment, is used to process the acquired electric shovel operation data in real time, calculate the time consumption of each operation stage, and calculate the efficiency index of each operation stage based on the preset theoretical standard efficiency. The calculator, connected to the data processor, is used to analyze efficiency bottlenecks in the electric shovel operation process based on efficiency indicators and identify work steps with abnormal time consumption. The monitor and controller are used to display efficiency indicators, bottleneck analysis results, and optimize electric shovel operation, and allow users to adjust operating parameters through interface input.

[0060] In one specific embodiment, the data acquisition device consists of a series of integrated sensor modules, capable of acquiring multi-dimensional data of the electric shovel during operation in real time and with high accuracy. The device design takes into account the operating environment and motion characteristics of the electric shovel, ensuring stable operation even in harsh environments. The core components of the device include: 1) Motion sensor: Three-axis gyroscope: Used to measure the rotation angle and speed of the bucket. This sensor has high accuracy and can provide stable angle change data even under high-speed rotation or vibration conditions.

[0061] Triaxial accelerometer: Used to collect acceleration data of the bucket, including linear acceleration and angular acceleration. This data can reflect the dynamic changes of the bucket during operation, such as rapid start-up, stopping, or rotation of the bucket.

[0062] Three-axis electronic compass: used to obtain the bucket's direction angle, helping to analyze the bucket's spatial positioning and its impact on the operation process, especially the impact on the rotation and unloading of the bucket.

[0063] 2) Image acquisition and imaging sensor: High-definition cameras: Used to capture real-time images of the blast pile surface to analyze its physical properties such as morphology, block size distribution, and looseness. High-definition cameras can capture subtle changes, aiding in accurate assessment of blasting effects.

[0064] Laser scanner: Used to scan the three-dimensional morphology and block size distribution of the blast pile. Laser scanning can provide more detailed blast pile data, helping to analyze the impact of different block sizes on loading operations.

[0065] 3) Auxiliary sensors: Temperature sensor: Used to monitor temperature changes in the working environment of the electric shovel and ensure that the equipment operates within a suitable temperature range.

[0066] Vibration sensor: Monitors the vibration of the electric shovel during operation and promptly identifies equipment malfunctions or potential problems.

[0067] Humidity sensor: Monitors ambient humidity, especially providing early warning of possible slippage or jamming during loading and unloading.

[0068] 4) Power supply and computing unit: High-efficiency power supply unit: The device is designed with a high-efficiency battery pack to ensure continuous and stable operation in the mining environment. The battery pack's capacity and stability can meet the needs of long-term high-frequency data acquisition.

[0069] Computing Unit: Integrating a high-performance processor, this unit is capable of preliminary processing, fusion, and storage of collected data, as well as real-time data analysis. It supports data interface with the mine management system, enabling remote data transmission and real-time monitoring.

[0070] (2) Data collection during electric shovel operation 1) Data collection location and installation method: The data acquisition device is installed in the windshield area of ​​the electric shovel's cab. The sensor rotates synchronously with the shovel bucket to ensure that data acquisition is synchronized with the operation process. A camera is aimed at the bucket and the surrounding blast pile area to ensure real-time capture of image data during the loading process. A laser scanner periodically scans the blast pile to obtain three-dimensional morphological data.

[0071] 2) Real-time data acquisition: Electric shovel operation data includes information such as bucket rotation angle, rotation speed, work cycle time, bucket load, and vibration status. This data helps analyze operation efficiency and provides data support for subsequent optimization.

[0072] Burst data includes information on block size distribution, looseness, and three-dimensional morphology. By combining cameras and laser scanners, precise burst morphology can be obtained, helping to analyze the impact of blasting effects on loading efficiency.

[0073] This application enables real-time, high-precision data acquisition and analysis of electric shovel loading efficiency, providing fundamental data for effectively optimizing the synergy between blasting and loading processes. During implementation, the data acquisition device integrates multiple sensors to achieve high-frequency, high-precision data acquisition and real-time analysis. Through advanced data analysis methods, this application provides decision support for optimizing electric shovel operations and front-end blasting parameters.

[0074] (1) Data acquisition and synchronous analysis In the implementation of this application, the intelligent data acquisition device collects data in real time at each stage of the electric shovel operation, providing data support for subsequent efficiency analysis. The main stages of data acquisition include: 1) Data collection during the operation process: The device uses sensors to collect real-time motion data of the electric shovel bucket (such as rotation angle, speed, acceleration, vibration, etc.) and operation cycle time. A high-definition camera and laser scanner simultaneously collect blasting data such as the three-dimensional morphology, block size distribution, and looseness of the blasted pile. The operating frequency and synchronous acquisition of all sensors ensure real-time integration of bucket operation and blasted pile information, providing an accurate timeline for subsequent analysis.

[0075] 2) Real-time data analysis and feedback: The system utilizes a high-efficiency data processing unit to perform real-time analysis of collected operational data. This data includes bucket rotation speed, work cycle time, and loading capacity. The system employs intelligent algorithms to perform detailed analysis of each stage of the work cycle, outputting operational efficiency, time consumption for each stage, and influencing factors. During data analysis, it combines data from camera and laser scanner-collected data on the pile size and looseness of the material to analyze the impact of block size distribution and looseness on loading efficiency, generating a real-time operational efficiency report.

[0076] (2) Detailed operation cycle analysis This application refines the work process, dividing electric shovel operations into four main stages: digging, rotating, unloading the bucket, and returning empty. The time and efficiency of each stage can be accurately statistically analyzed using the collected data.

[0077] 1) Breakdown of the work cycle: Each work cycle is divided into different stages: excavation, rotation, bucket unloading, and empty return. The system monitors and records the time of each stage in real time, provides time consumption data for each stage, and analyzes the coordination between the stages.

[0078] 2) Efficiency bottleneck analysis: By statistically analyzing and comparing time data at each stage, the system can pinpoint efficiency bottlenecks in the operation. For example, by analyzing whether the mining stage is affected by the looseness of the blast pile, and combining blast pile data (such as block size ratio and looseness), the system can accurately analyze the specific impact of blast pile characteristics on operational efficiency and identify bottlenecks in the operation process.

[0079] (3) Optimization of blasting and loading coordination One of the key innovations of this application is that it breaks through the data silos between blasting and loading in traditional mining operations, and realizes data fusion and collaborative optimization between the two.

[0080] 1) Data fusion and collaborative optimization: The system synchronously collects and integrates data from the blasting process (such as the block size distribution and looseness of the blast pile) and data from the loading process (such as the bucket rotation angle and operation time). By analyzing the loading efficiency under different blasting conditions in real time, the system can provide suggestions for optimizing blasting processes (such as orifice design and charge structure) and loading processes (such as bucket rotation speed and loading capacity).

[0081] 2) Collaborative optimization decision support: The system combines a quantitative model of blasting quality and loading efficiency to evaluate the synergistic effect of blasting and loading in real time. For example, a blast pile with high looseness may require adjustments to the bucket rotation angle to reduce loading time. Based on the analysis results, the system automatically generates optimization schemes and provides specific adjustment suggestions to mine managers, such as changing blasting parameters and adjusting the bucket working speed.

[0082] Based on the same inventive concept, this application also provides an electric shovel production efficiency data acquisition and analysis device, which can be used to implement the method described in the above embodiments, as described in the following embodiments. Since the principle of this electric shovel production efficiency data acquisition and analysis device in solving the problem is similar to that of the electric shovel production efficiency data acquisition and analysis method, the implementation of the electric shovel production efficiency data acquisition and analysis device can refer to the implementation of the electric shovel production efficiency data acquisition and analysis method, and repeated details will not be elaborated further. As used below, the terms "unit" or "module" can refer to a combination of software and / or hardware that performs a predetermined function. Although the system described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0083] According to another aspect of this application, an electric shovel production efficiency data acquisition and analysis device is also provided, such as... Figure 5 As shown, it includes: The data acquisition unit 501 is used to acquire electric shovel operation data in real time through the sensors integrated in the data acquisition device; The efficiency index calculation unit 502 is used to process the electric shovel operation data in real time and obtain the efficiency index of the electric shovel in each operation stage. Analysis unit 503 is used to analyze the efficiency bottleneck of electric shovels based on efficiency indicators and identify abnormal time consumption during operation.

[0084] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0085] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0086] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0087] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0088] This application uses specific embodiments to illustrate the principles and implementation methods of this application. The description of the above embodiments is only for the purpose of helping to understand the method and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

[0089] This application also provides a specific implementation of an electronic device capable of implementing all the steps in the methods described above. See [link to implementation details]. Figure 6 The electronic device specifically includes the following: Figure 6 This is a schematic diagram of the physical structure of an electronic device provided in an embodiment of this application. The electronic device includes: a processor 601, a memory 602, and a bus 603.

[0090] The processor 601 and the memory 602 communicate with each other via the bus 603.

[0091] The processor 601 is used to call the computer program in the memory 602. When the processor executes the computer program, it implements all the steps in the method in the above embodiments. For example, when the processor executes the computer program, it implements the following steps: S101: Real-time acquisition of electric shovel operation data via sensors integrated into the data acquisition device; S102: Process the electric shovel's operating data in real time to obtain the efficiency index of the electric shovel at each operating stage; S103: Analyze the efficiency bottleneck of the electric shovel based on efficiency indicators and identify abnormal time consumption during operation.

[0092] Embodiments of this application also provide a computer-readable storage medium capable of implementing all steps of the methods in the above embodiments. The computer-readable storage medium stores a computer program that, when executed by a processor, implements all steps of the methods in the above embodiments. For example, when the processor executes the computer program, it implements the following steps: S101: Real-time acquisition of electric shovel operation data via sensors integrated into the data acquisition device; S102: Process the electric shovel's operating data in real time to obtain the efficiency index of the electric shovel at each operating stage; S103: Analyze the efficiency bottleneck of the electric shovel based on efficiency indicators and identify abnormal time consumption during operation.

[0093] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on its differences from other embodiments. In particular, for hardware + program embodiments, since they are basically similar to method embodiments, the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments. Although the embodiments in this specification provide the method operation steps as shown in the embodiments or flowcharts, more or fewer operation steps may be included based on conventional or non-inventive means. The order of steps listed in the embodiments is merely one possible execution order among many steps and does not represent the only execution order. In actual device or terminal product execution, the methods can be executed in the order shown in the embodiments or drawings or in parallel (e.g., in a parallel processor or multi-threaded processing environment, or even a distributed data processing environment). The terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, product, or apparatus that includes a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, product, or apparatus. Without further limitations, the presence of other identical or equivalent elements in the process, method, product, or apparatus that includes said elements is not excluded. For ease of description, the above devices are described in terms of function, divided into various modules. Of course, in implementing the embodiments of this specification, the functions of each module can be implemented in one or more software and / or hardware, or the module implementing the same function can be implemented by a combination of multiple sub-modules or sub-units, etc. The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division; 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 displayed or discussed mutual couplings or direct couplings or communication connections may be through some interfaces; indirect couplings or communication connections between devices or units may be electrical, mechanical, or other forms. This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing device to produce a machine, such that the instructions, which are executable by the processor of the computer or other programmable data processing device, produce instructions for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0094] Those skilled in the art will understand that the embodiments of this specification can be provided as methods, systems, or computer program products. Therefore, the embodiments of this specification can take the form of entirely hardware embodiments, entirely software embodiments, or embodiments combining software and hardware aspects. Furthermore, the embodiments of this specification can take the form of computer program products implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The various embodiments in this specification are described in a progressive manner, and similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, for system embodiments, since they are basically similar to method embodiments, the description is relatively simple, and relevant parts can be referred to the description of the method embodiments. In the description of this specification, the reference to the terms "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., means that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the embodiments of this specification.

[0095] In this specification, the illustrative expressions of the terms used do not necessarily refer to the same embodiments or examples. Furthermore, those skilled in the art can combine and integrate different embodiments or examples described in this specification, as well as features of different embodiments or examples, without contradiction. The above descriptions are merely embodiments of this specification and are not intended to limit the embodiments of this specification. Various modifications and variations can be made to the embodiments of this specification by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principle of the embodiments of this specification should be included within the scope of the claims of the embodiments of this specification.

Claims

1. A method for collecting and analyzing data on the production efficiency of electric shovels, characterized in that, include: Data on electric shovel operation is collected in real time through sensors integrated into the data acquisition device; The electric shovel's operating data is processed in real time to obtain the efficiency index of the electric shovel at each operating stage; Based on the efficiency indicators, analyze the efficiency bottlenecks of the electric shovel and identify abnormal time consumption during the operation process.

2. The method for collecting and analyzing electric shovel production efficiency data according to claim 1, characterized in that, The real-time processing of the electric shovel's operating data to obtain efficiency indicators for each operational stage includes: The electric shovel operation data is subjected to noise reduction and filtering. The time consumption of each operation step is calculated based on the processed electric shovel operation data, and the efficiency index of each operation step is calculated in combination with the preset theoretical standard efficiency.

3. The method for collecting and analyzing electric shovel production efficiency data according to claim 1, characterized in that, The step of analyzing the efficiency bottleneck of the electric shovel based on the efficiency index and identifying abnormal time consumption during the operation includes: The efficiency indicators are compared with preset standard efficiency indicators to determine the stage where the efficiency bottleneck is located. Identify the factors that cause the efficiency bottleneck based on the stage in which it occurs.

4. The method for collecting and analyzing electric shovel production efficiency data according to claim 2, characterized in that, The calculation of the efficiency index for each work step, based on a preset theoretical standard efficiency, includes: The time consumption is compared with the theoretical standard efficiency to obtain the efficiency index for each work step.

5. The method for collecting and analyzing data on the production efficiency of electric shovels according to claim 3, characterized in that, The step of comparing the efficiency index with a preset standard efficiency index to determine the stage where the efficiency bottleneck is located includes: The efficiency index is compared with the preset standard efficiency index to obtain the comparison result; The bottleneck in efficiency is determined based on the difference between the comparison results and the standard efficiency index.

6. The method for collecting and analyzing data on the production efficiency of electric shovels according to claim 1, characterized in that, Also includes: Based on the analysis results, adjustments were made to the operation of the electric shovel, including adjusting the rotation speed of the electric shovel.

7. A data acquisition and analysis device for the production efficiency of an electric shovel, characterized in that, include: The data acquisition unit is used to collect electric shovel operation data in real time through the sensors integrated into the data acquisition device; The efficiency index calculation unit is used to process the electric shovel operation data in real time to obtain the efficiency index of the electric shovel in each operation stage. The analysis unit is used to analyze the efficiency bottleneck of the electric shovel based on the efficiency indicators and identify abnormal time consumption during the operation process.

8. A data acquisition and analysis device for electric shovel production efficiency, used to execute a method for data acquisition and analysis of electric shovel production efficiency, characterized in that, include: The data acquisition device integrates multiple sensors for real-time acquisition of electric shovel operation data. The sensors include motion sensors, accelerometers, gyroscopes, cameras, and laser scanners. The data processor, connected to the data acquisition device, is used to process the acquired electric shovel operation data in real time, calculate the time consumption of each operation stage, and calculate the efficiency index of each operation stage based on the preset theoretical standard efficiency. A calculator, connected to the data processor, is used to analyze efficiency bottlenecks in the electric shovel operation process based on the efficiency indicators and identify operation segments with abnormal time consumption. The display and controller are used to show the efficiency indicators, bottleneck analysis results, and optimize electric shovel operation, and allow users to adjust operation parameters by inputting commands through the interface.

9. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the electric shovel production efficiency data acquisition and analysis method according to any one of claims 1 to 6.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the electric shovel production efficiency data acquisition and analysis method according to any one of claims 1 to 6.

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