Strip mine mining, loading and transporting integrated intelligent scheduling method and device and medium

Abstract

The invention relates to the technical field of intelligent scheduling, and discloses a strip mine mining, loading and transporting integrated intelligent scheduling method and device and a medium. The method comprises the following steps: carrying out layered decoupling processing on equipment group operation efficiency indexes and mining area mining planning data to obtain an energy efficiency-oriented collaborative task sequence; performing group intelligent matching processing on the energy efficiency-oriented collaborative task sequence and the equipment group space-time distribution data to obtain a collection, loading and transportation integrated resource allocation scheme; performing dynamic coordination processing on the acquisition, loading and transportation integrated resource allocation scheme and the road traffic constraint data to obtain an equipment group adaptive scheduling strategy; and performing closed-loop iteration processing on the adaptive scheduling strategy of the equipment group and the feedback data of the production process to obtain an acquisition, loading and transportation collaborative optimization instruction. The problems that in a traditional scheduling method, mining, loading and transporting links are separated, energy consumption control is insufficient, and adaptability is poor are solved.

Description

Technical Field

[0001] The present application relates to the field of intelligent scheduling, and in particular to an open-pit mine integrated intelligent scheduling method, device and medium. Background Art

[0002] The operating environment of open-pit mines is complex, and the production process involves multiple links such as mining, loading, and transportation, which puts forward high requirements for equipment scheduling and management. At present, the scheduling and management of open-pit mines mainly rely on manual experience to make decisions, and use technical means such as GPS positioning and wireless communication to monitor and dispatch mining and loading equipment in real time. At the same time, some mines have begun to introduce computer-aided scheduling systems, which optimize the scheduling of equipment by establishing mathematical models, and have improved production efficiency to a certain extent. With the development of technologies such as the Internet of Things and big data, mine scheduling systems have gradually developed in the direction of intelligence and automation, and have begun to try to use new technologies such as artificial intelligence to solve scheduling problems.

[0003] However, the existing open-pit mine scheduling methods still have some shortcomings: first, the traditional scheduling methods often separate the various links of mining and loading and optimize them separately, resulting in poor overall synergy; second, the existing scheduling system does not take into account the energy consumption characteristics of equipment, making it difficult to achieve a comprehensive balance between energy efficiency and production efficiency; third, the scheduling strategy is not adaptable, and it is difficult to adjust in time under complex working conditions and emergencies; finally, there is a lack of effective closed-loop feedback mechanism, and it is impossible to dynamically optimize the scheduling plan according to the actual situation in the production process. These problems have seriously restricted the production efficiency and economic benefits of open-pit mines. Summary of the invention

[0004] The present application provides an open-pit mine integrated intelligent scheduling method, device and medium for constructing an energy efficiency-oriented integrated intelligent scheduling method for mining, loading and transportation, realizing coordinated scheduling and dynamic optimization of equipment groups, and solving problems such as the separation of mining and loading and transportation links, insufficient energy consumption control, and poor adaptability in traditional scheduling methods.

[0005] In the first aspect, the present application provides an open-pit mine integrated intelligent scheduling method for mining and loading, which comprises: performing multi-source information fusion processing on the topographic and geomorphic feature data of the open-pit mine area and the real-time operating condition data of the mining and loading equipment group to obtain the mining area collaborative operation scene data; performing intelligent feature extraction processing on the mining area collaborative operation scene data and the energy consumption characteristic data of the mining and loading equipment group to obtain the equipment group operation efficiency index; performing hierarchical decoupling processing on the equipment group operation efficiency index and the mining area mining planning data to obtain an energy efficiency-oriented collaborative task sequence; performing group intelligent matching processing on the energy efficiency-oriented collaborative task sequence and the equipment group spatiotemporal distribution data to obtain a mining and loading integrated resource allocation plan; performing dynamic coordination processing on the mining and loading integrated resource allocation plan and road traffic constraint data to obtain an equipment group adaptive scheduling strategy; performing closed-loop iterative processing on the equipment group adaptive scheduling strategy and production process feedback data to obtain mining and loading collaborative optimization instructions.

[0006] In a second aspect, the present application provides an open-pit mine mining and loading integrated intelligent scheduling device, the open-pit mine mining and loading integrated intelligent scheduling device comprising: The fusion module is used to perform multi-source information fusion processing on the topographic and geomorphic feature data of the open-pit mine area and the real-time working condition data of the mining and loading equipment group to obtain the collaborative operation scene data of the mine area; An extraction module is used to perform intelligent feature extraction processing on the mining area collaborative operation scene data and the energy consumption characteristic data of the mining and loading equipment group to obtain the equipment group operation efficiency index; A decoupling module, used for performing hierarchical decoupling processing on the equipment group operation efficiency index and the mining area mining planning data to obtain a collaborative task sequence oriented to energy efficiency; A matching module, used to perform group intelligent matching processing on the energy efficiency-oriented collaborative task sequence and the spatiotemporal distribution data of the equipment group to obtain a resource allocation plan for integrated procurement, loading and transportation; A coordination module is used to dynamically coordinate the integrated resource allocation scheme for procurement, loading and transportation and the road traffic constraint data to obtain an adaptive scheduling strategy for the equipment group; The iteration module is used to perform closed-loop iterative processing on the adaptive scheduling strategy of the equipment group and the feedback data of the production process to obtain the procurement, loading and transportation collaborative optimization instructions.

[0007] The third aspect of the present application provides a computer-readable storage medium, in which instructions are stored. When the computer-readable storage medium is run on a computer, the computer executes the above-mentioned open-pit mine mining, loading and transportation integrated intelligent scheduling method.

[0008] In the technical solution provided by this application, by performing multi-source information fusion processing on the terrain and geomorphic feature data of the open-pit mine area and the real-time working condition data of the mining and loading equipment group, a comprehensive perception of the terrain features and equipment operating status of the mining area is achieved, providing a reliable data basis for subsequent scheduling decisions. At the same time, intelligent feature extraction and processing are performed on the mining area collaborative operation scene data and the energy consumption characteristic data of the mining and loading equipment group, and the energy consumption law in the equipment operation process is deeply excavated, providing a basis for energy-saving optimization. Based on the hierarchical decoupling processing of the equipment group operation efficiency indicators and the mining planning data of the mining area, the reasonable decomposition and scientific organization of the mining and loading operation tasks are achieved, and the feasibility of task execution is effectively improved. By performing group intelligent matching processing on the energy-efficiency-oriented collaborative task sequence and the spatiotemporal distribution data of the equipment group, the optimal configuration of equipment resources is achieved, and the equipment utilization efficiency is significantly improved. Combined with the dynamic coordination processing of the mining and loading integrated resource allocation scheme and the road traffic constraint data, a flexible scheduling strategy adjustment mechanism is established to effectively deal with various uncertain factors in the production process. Finally, the adaptive scheduling strategy of the equipment group and the feedback data of the production process are processed in a closed loop iteration, forming a complete closed-loop control system of optimization-execution-feedback-adjustment, which ensures the continuous optimization of the scheduling plan and the stable operation of the production process. Through the organic combination of these technical features, the present invention not only improves the production efficiency and equipment utilization rate of the open-pit mine area, but also reduces energy consumption and saves production costs, while enhancing the adaptability of the scheduling system to complex working conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0010] Figure 1 This is a schematic diagram of an embodiment of an intelligent scheduling method for integrated mining, loading and transportation of open-pit mines in an embodiment of the present application; Figure 2 It is a schematic diagram of an embodiment of an intelligent dispatching device for integrated mining, loading and transportation in an open-pit mine in an embodiment of the present application. DETAILED DESCRIPTION

[0011] The embodiments of the present application provide an open-pit mine integrated intelligent scheduling method, device and medium. The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "including" or "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0012] For ease of understanding, the specific process of the embodiment of the present application is described below. Figure 1 , an embodiment of the open-pit mine mining and loading integrated intelligent scheduling method in the embodiment of the present application includes: Step S101, performing multi-source information fusion processing on the topographic and geomorphic feature data of the open-pit mine area and the real-time working condition data of the mining and loading equipment group to obtain the mining area collaborative operation scene data; Step S102: Perform intelligent feature extraction processing on the mining area collaborative operation scene data and the energy consumption characteristic data of the mining and loading equipment group to obtain the equipment group operation efficiency index; Step S103, performing hierarchical decoupling processing on the equipment group operation efficiency index and the mining area mining planning data to obtain a collaborative task sequence oriented to energy efficiency; Step S104: Perform group intelligent matching processing on the energy efficiency-oriented collaborative task sequence and the equipment group spatiotemporal distribution data to obtain a procurement, loading and transportation integrated resource allocation plan; Step S105: dynamically coordinate and process the integrated resource allocation plan for procurement, loading and transportation and the road traffic constraint data to obtain an adaptive scheduling strategy for the equipment group; Step S106: Perform closed-loop iterative processing on the equipment group adaptive scheduling strategy and production process feedback data to obtain procurement, loading and transportation collaborative optimization instructions.

[0013] It is understandable that the execution subject of the present application can be an open-pit mine integrated intelligent dispatching device, or a terminal or a server, which is not limited here. The present application embodiment is described by taking a server as the execution subject as an example.

[0014] Specifically, the terrain and geomorphic feature data of the open-pit mine area and the real-time working condition data of the mining and loading equipment group are processed. The terrain and geomorphic feature data include information such as the terrain slope, regional elevation, and geological structure of the mining area, and the real-time working condition data of the mining and loading equipment group include real-time operating parameters such as the position, speed, and engine speed of the equipment. The mining area operation level data is obtained through hierarchical and partitioning processing, and then the boundary feature extraction is performed to obtain the boundary point set of the operation area, and then the boundary tracking algorithm is used to determine the passable area. The spatial distribution characteristics of the equipment group are analyzed through density clustering processing, and the energy consumption status of the equipment is evaluated in combination with the engine working condition data, and finally the mining area collaborative operation scene data is formed. After obtaining the mining area collaborative operation scene data, intelligent feature extraction is performed in combination with the energy consumption characteristic data of the mining and loading equipment group. First, the scene data is regionally classified to obtain the feature types of different operation areas, such as mining area, transportation area, unloading area, etc. The motion trajectory of the equipment group is analyzed through sequence pattern mining to identify the typical operation mode. Combined with the instantaneous fuel consumption data of the equipment, the energy consumption change rate under different operation modes is calculated to establish the energy consumption gradient data. Multi-dimensional correlation analysis is performed on parameters such as equipment load, engine speed, and hydraulic system pressure. Dimensionality reduction processing is performed through principal component analysis to finally obtain a comprehensive index reflecting the equipment operating efficiency. Based on the equipment group operating efficiency indicators, hierarchical decoupling processing is performed in combination with mining planning data in the mining area. Equipment is graded according to energy efficiency levels through grade quantification, and energy consumption interval thresholds are determined. Spatial matching is performed on the mining working face, and the corresponding relationship between the working face and equipment energy consumption is established. Weighted calculation is performed considering the transportation distance. The basic operating unit is split according to the monthly mining plan, and the feasible task set is determined under the energy consumption constraint. Finally, spatiotemporal coordination is performed to obtain a collaborative task sequence oriented to energy efficiency.

[0015] Swarm intelligent matching is carried out for collaborative task sequences. First, task constraints are extracted and standardized. The distance matrix between equipment and tasks is calculated to estimate the scheduling cost. The Hungarian algorithm is used to optimize task allocation, which achieves the optimal allocation of tasks by constructing the minimum weight matching of a bipartite graph. Conflict detection and time window adjustment are performed on the initial allocation plan to ensure the feasibility of task execution, and finally form an integrated resource allocation plan for procurement, loading and transportation. On the basis of the resource allocation plan, dynamic coordination is carried out considering road traffic constraints. The equipment movement sequence is analyzed, the intersection position is predicted, and the safe distance is calculated in combination with the road bearing capacity. The road traffic capacity is evaluated in different time periods, and a congestion warning mechanism is established. Peak-shifting scheduling is achieved through time window adjustment, and dynamic correction is made according to the equipment trajectory deviation to form an adaptive scheduling strategy.

[0016] Finally, scheduling optimization is achieved through closed-loop iterative processing. Timeliness analysis and progress monitoring of strategy execution are carried out, and deviations are found and attribution analysis is performed by comparing real-time production data. Typical deviation patterns are identified, and adjustment suggestions are made based on historical disposal experience. Scenario adaptation and feasibility verification are carried out, and the optimal instruction scheme is selected after multi-objective weighing, and finally the procurement, loading and transportation collaborative optimization instructions are generated.

[0017] Take an open-pit mine as an example. The mine covers an area of ​​about 5 square kilometers and has 30 mining and loading equipment. Through multi-source information fusion processing, the mine is divided into 8 operating levels and 120 boundary feature points are identified. The distribution of equipment groups shows 3 main operating clusters, and the average equipment energy consumption is 45L / h. Feature extraction analysis shows that the energy consumption difference under different regional operating modes reaches 30%, and the load and energy consumption show a significant correlation. In the process of task decoupling, the monthly mining volume of 2.4 million tons is decomposed into 2,400 basic operating units. The Hungarian algorithm is used for task matching, and the calculated optimal scheduling plan saves 15% of energy consumption compared with the original scheduling plan. In the road traffic coordination, 4 sections prone to congestion are identified, and the congestion time is reduced by 40% through dynamic adjustment. Closed-loop iterative optimization improves the operating efficiency of mining and loading equipment by 20%, and the overall operation of the system is more stable and reliable.

[0018] In the embodiment of the present application, by performing multi-source information fusion processing on the terrain and geomorphic feature data of the open-pit mine area and the real-time working condition data of the mining and loading equipment group, a comprehensive perception of the terrain features of the mining area and the equipment operation status is achieved, providing a reliable data basis for subsequent scheduling decisions. At the same time, the intelligent feature extraction and processing of the mining area collaborative operation scene data and the energy consumption characteristic data of the mining and loading equipment group are carried out, and the energy consumption law in the equipment operation process is deeply excavated, providing a basis for energy-saving optimization. Based on the hierarchical decoupling processing of the equipment group operation efficiency indicators and the mining planning data of the mining area, the reasonable decomposition and scientific organization of the mining and loading operation tasks are achieved, and the feasibility of task execution is effectively improved. By performing group intelligent matching processing on the energy-efficiency-oriented collaborative task sequence and the spatiotemporal distribution data of the equipment group, the optimal configuration of equipment resources is achieved, and the equipment utilization efficiency is significantly improved. Combined with the dynamic coordination processing of the mining and loading integrated resource allocation plan and the road traffic constraint data, a flexible scheduling strategy adjustment mechanism is established to effectively deal with various uncertain factors in the production process. Finally, the adaptive scheduling strategy of the equipment group and the feedback data of the production process are processed in a closed loop iteration, forming a complete closed-loop control system of optimization-execution-feedback-adjustment, which ensures the continuous optimization of the scheduling plan and the stable operation of the production process. Through the organic combination of these technical features, the present invention not only improves the production efficiency and equipment utilization rate of the open-pit mine area, but also reduces energy consumption and saves production costs, while enhancing the adaptability of the scheduling system to complex working conditions.

[0019] In a specific embodiment, the process of executing step S101 may specifically include the following steps: (1) The topographic and geomorphic feature data of the open-pit mine area are processed by stratification and zoning to obtain the mining area operation level data, and the boundary feature extraction processing of the mining area operation level data is performed to obtain the boundary point set of the operation area; (2) Perform regional connectivity analysis on the boundary point set of the operating area to obtain data on the passable area for mining and loading equipment, and perform contour extraction on the passable area data through a boundary tracking algorithm to obtain data on the mine area’s passable range; (3) Perform spatiotemporal correlation processing on the location data and speed data of the collection and transportation equipment group to obtain the dynamic distribution data of the equipment group, and perform density clustering processing on the dynamic distribution data of the equipment group to obtain the operation cluster area data of the equipment group; (4) Perform correlation analysis on the equipment group operation cluster data and the equipment engine speed data to obtain the equipment group working condition characteristic data, and perform energy consumption evaluation on the equipment group working condition characteristic data to obtain the equipment group energy consumption status data; (5) Performing regional division processing on the mine area traffic range data and the equipment group energy consumption status data to obtain energy consumption partition data, and performing grade division processing on the energy consumption partition data to obtain grade data on energy consumption; (6) Perform frequency statistics on the energy consumption level data of each zone and the historical operation records to obtain regional operation frequency data, and perform weight calculation on the regional operation frequency data to obtain regional operation intensity data; (7) Match and correlate the regional operation intensity data and real-time equipment status data to obtain the mining area collaborative operation scenario data.

[0020] Specifically, the processing of the topographic and geomorphic feature data of the open-pit mine area is first carried out in layers and zones. This process is based on the elevation data of the mine area. The topographic and geomorphic feature data includes information such as the slope, altitude, and geological structure of the mine area. By analyzing these data, the mine area is divided into different operating levels. Each operating level represents a specific mining platform with similar topographic features and operating conditions. After obtaining the mine area operating level data, the boundary features of each level are extracted to identify the key boundary points of the operating area. These boundary points constitute the operating area boundary point set, which is used to describe the spatial range of each operating area. After obtaining the operating area boundary point set, regional connectivity analysis is required to determine the passable area of ​​the mining and loading equipment. Connectivity analysis identifies the area where the equipment can pass by judging the relationship between adjacent boundary points. The boundary tracking algorithm is an important method for extracting the regional contour. The algorithm starts from a starting boundary point and searches for adjacent boundary points in sequence according to specific rules to finally form a complete regional contour. The passable area data is processed by the boundary tracking algorithm to obtain accurate mine area pass range data. For the management of the equipment group, it is necessary to perform spatiotemporal correlation processing on the location data and speed data of the equipment. The location data comes from the GPS positioning system, and the speed data comes from the driving record of the equipment. The spatiotemporal correlation processing corresponds the location and speed information in the time dimension to form the dynamic distribution data of the equipment group. Density clustering processing is a density-based clustering analysis method. By analyzing the distribution density of equipment in space, the operation clustering area of ​​the equipment group is identified. The correlation analysis of the equipment group operation clustering area data and the equipment engine speed data is aimed at evaluating the working status of the equipment. The engine speed data reflects the load condition of the equipment, and the equipment group working condition characteristic data is obtained through correlation analysis. The working condition characteristics include indicators such as engine load rate, running time, and idling time. Energy consumption evaluation is performed on the working condition characteristic data, and parameters such as fuel consumption and energy utilization efficiency per unit time are calculated to obtain the energy consumption status data of the equipment group.

[0021] The combination of the mine area access range data and the equipment group energy consumption status data is processed through regional division to form energy consumption zoning data. Regional division takes into account the impact of factors such as road slope and road surface conditions on energy consumption. The energy consumption zoning data is graded, and the regions are divided into different levels according to the energy consumption level per unit distance to obtain the zoning energy consumption grade data. The zoning energy consumption grade data is combined with historical operation records for frequency statistics to analyze the operation frequency of each area. Frequency statistics include information such as the number of equipment passes and operation duration, and regional operation frequency data is obtained through statistics. The operation frequency data is weighted, and the operation intensity weight of each area is determined by considering factors such as operation type, equipment type, and operation time to obtain regional operation intensity data.

[0022] Finally, the regional operation intensity data is matched and associated with the real-time equipment status data. The real-time equipment status data includes information such as equipment location, working status, and remaining oil volume. Through matching and association, the corresponding relationship between regional operation intensity and equipment status is established, and finally a complete mining area collaborative operation scenario data is formed.

[0023] Take an open-pit mine as an example. The mining area covers an area of ​​4 square kilometers and has a vertical height difference of 300 meters. Through hierarchical zoning processing, the mining area is divided into 12 operating levels, each with a height difference of 25 meters. Boundary feature extraction identifies an average of 80 boundary feature points for each level. Regional connectivity analysis identified 10 main transportation channels, and the boundary tracking algorithm outlined a traffic range with a total length of 15 kilometers. The mining area is equipped with 20 mining and loading equipment. Through density clustering analysis, three main operating clusters are identified, which are located in the mining area, loading area and unloading area. The equipment engine speed data shows that the average speed is 1800rpm in the uphill section, and the energy consumption is about 50L / h; the average speed is 1500rpm in the flat section, and the energy consumption is about 35L / h. The energy consumption zoning is divided into 5 levels, among which the first-level area (slope less than 3%) has the lowest energy consumption, and the fifth-level area (slope greater than 15%) has the highest energy consumption. Historical operation records show that the average daily frequency of passing through the main roads is 200 times, and the average daily frequency of passing through the branch roads is 50 times. The weight calculation takes into account the operation type (loading weight 1.5, transportation weight 1.0) and time factors (peak period weight 1.2, off-peak period weight 1.0).

[0024] In a specific embodiment, the process of executing step S102 may specifically include the following steps: (1) Perform regional classification processing on the mining area collaborative operation scene data to obtain scene feature type set data, and perform statistical aggregation processing on the scene feature type set data to obtain regional operation mode data; (2) Perform spatiotemporal correlation processing on the regional operation mode data and the GPS trajectory data of the equipment group to obtain the motion feature data of the equipment group, and perform frequent pattern extraction processing on the motion feature data of the equipment group through sequence pattern mining to obtain typical operation sequence data; (3) Perform time-series matching processing on typical operation sequence data and instantaneous fuel consumption data of equipment groups to obtain corresponding data of operation energy consumption, and calculate the change rate of corresponding data of operation energy consumption to obtain energy consumption gradient data of equipment groups; (4) Perform multi-dimensional correlation processing on the energy consumption gradient data of the equipment group and the equipment load data to obtain the energy consumption data per unit load, and perform outlier detection processing on the energy consumption data per unit load to obtain the energy consumption threshold interval data; (5) Perform response characteristic analysis on the energy consumption threshold interval data and the equipment engine speed data to obtain the equipment power response data, and perform feature dimension reduction processing on the equipment power response data through principal component analysis to obtain key characteristic factor data; (6) Combine and analyze the key characteristic factor data and the equipment hydraulic system pressure data to obtain the equipment comprehensive performance data, and then conduct quantitative scoring processing on the equipment comprehensive performance data to obtain the equipment group operation efficiency index.

[0025] Specifically, the processing of the mining area collaborative operation scene data begins with regional classification, mainly dividing the mining area into different types such as mining operation area, loading operation area, transportation channel area and unloading operation area. The regional classification processing is based on the terrain features, equipment distribution and operation characteristics in the scene data, and the scene feature type set data is obtained through cluster analysis method. The scene feature type set data is statistically aggregated to analyze the area proportion, equipment distribution density, operation frequency and other characteristics of each type of area, so as to obtain the regional operation mode data. The regional operation mode data and the GPS trajectory data of the equipment group are analyzed in time and space. The GPS trajectory data records the location, speed, direction and other information of the equipment. The time and space association processing analyzes the movement characteristics of the equipment in different areas by matching the equipment trajectory points with the regional operation mode. Sequential pattern mining is a data mining method specifically used to discover frequently occurring patterns in data sequences. The equipment group movement feature data is processed through sequential pattern mining technology to identify the typical movement path and operation sequence of the equipment group between different areas, forming typical operation sequence data.

[0026] In the process of time series matching between typical operation sequence data and instantaneous fuel consumption data of equipment groups, the fuel consumption data corresponding to each operation sequence segment are associated to obtain the energy consumption characteristics of different operation types and paths. The fuel consumption data comes from the real-time monitoring of the equipment oil volume sensor, which records the fuel consumption per unit time. By calculating the fuel consumption change rate at adjacent time points, the energy consumption gradient data of the equipment group is obtained, which reflects the dynamic characteristics of energy consumption changing with the operation type and road conditions. Multi-dimensional correlation analysis is performed on the energy consumption gradient data of the equipment group and the equipment load data, and the load data includes the weight of the equipment and the weight of the load. By calculating the energy consumption level of unit load under different operating conditions, the unit load energy consumption data is obtained. Adopt outlier detection methods, such as the 3σ criterion or the box plot method, to identify abnormal energy consumption data points, determine the reasonable energy consumption change range, and thus obtain energy consumption threshold interval data.

[0027] The response characteristic analysis of energy consumption threshold interval data and equipment engine speed data mainly focuses on the correspondence between engine operating conditions and energy consumption. The engine speed data reflects the power output state of the equipment. Combined with the energy consumption data obtained in the early stage, the energy consumption change characteristics at different speeds are analyzed to obtain the equipment power response data. Principal component analysis is a dimensionality reduction method that retains the main feature information by performing orthogonal transformation on high-dimensional data. Principal component analysis is performed on the equipment power response data to extract key characteristic factors, reduce the data dimension, and obtain key characteristic factor data. The combined analysis of key characteristic factor data and equipment hydraulic system pressure data focuses on the comprehensive working performance of the equipment. The hydraulic system pressure data reflects the load state of the equipment actuator and is closely related to power output and energy consumption. By analyzing the correlation between various indicators, a performance evaluation system is established, and finally the comprehensive performance data of the equipment is obtained. The comprehensive performance data is quantitatively scored to form a standardized equipment group operation efficiency index.

[0028] Taking the actual application of an open-pit mine as an example, the total area of ​​the mining area is 6 square kilometers, with 3 mining working faces, 2 loading areas and 4 unloading platforms. Statistical analysis shows that the mining area occupies 30% of the area, the loading area accounts for 15%, the transportation channel accounts for 40%, and the unloading area accounts for 15%. GPS trajectory analysis shows the daily operation trajectories of 15 mining trucks. Through sequence pattern mining, 4 typical operation paths are found: mining, loading, transport and unloading cycle operations account for 65%, short-distance shuttle transportation accounts for 20%, mining and loading combined operations account for 10%, and maintenance and rescue operations account for 5%. Time series matching analysis shows that under the condition of an average load of 40 tons, the energy consumption of no-load uphill is 45L / h, the energy consumption of full-load uphill is 75L / h, the energy consumption of no-load on flat sections is 25L / h, and the energy consumption of full load is 45L / h. Outlier detection eliminates energy consumption data points that exceed the normal range and determines the energy consumption threshold range under various working conditions. The analysis of the engine response characteristics shows that the energy consumption is linearly related to the speed when the speed is in the range of 1200-1800rpm, and the energy consumption rises sharply after exceeding 1800rpm. The principal component analysis reduces the original 12 performance indicators to 4 key characteristic factors, which can explain 85% of the data variance. Finally, the equipment group operation efficiency index is obtained through comprehensive scoring, with a scoring range of 0-100, where 90 points or more are excellent, 80-90 points are good, 70-80 points are qualified, and below 70 points need to be optimized and improved.

[0029] In a specific embodiment, the process of executing step S103 may specifically include the following steps: (1) Perform graded quantitative processing on the equipment group's operating efficiency indicators to obtain equipment energy efficiency grading data, and perform threshold segmentation processing on the equipment energy efficiency grading data to obtain equipment operation energy consumption interval data; (2) Perform spatial matching processing on the equipment operation energy consumption interval data and the mining face layout data to obtain the working face equipment energy consumption mapping data, and perform hierarchical clustering processing on the working face equipment energy consumption mapping data to obtain the mining and loading operation energy consumption level data; (3) Perform weighted calculation on the energy consumption level data of the collection and loading operation and the transportation route distance data to obtain the transportation energy consumption weight data, and perform segmented accumulation processing on the transportation energy consumption weight data to obtain the segmented transportation cost data; (4) Perform task decomposition processing on the segmented transportation cost data and the monthly mining plan data of the mining area to obtain the basic operation unit data, and then perform energy consumption constraint processing on the basic operation unit data to obtain the energy-efficient feasible task set; (5) Perform boundary constraint processing on the energy efficiency feasible task set and equipment operation range data to obtain task space distribution data, and perform time series sorting on the task space distribution data to obtain initial task time series data; (6) The initial task timing data and the equipment energy efficiency rating data are matched and optimized to obtain the corresponding data of the task equipment, and the corresponding data of the task equipment are coordinated in time and space to obtain a collaborative task sequence oriented to energy efficiency.

[0030] Specifically, the quantification of the level of equipment group operation efficiency indicators involves converting continuous efficiency indicators into discrete level data. The equipment energy efficiency grading data is obtained by dividing different energy efficiency level intervals, and then the equipment energy efficiency grading data is subjected to threshold segmentation processing to determine the energy consumption range corresponding to each level, thereby obtaining the equipment operation energy consumption interval data. In the process of spatial matching of the equipment operation energy consumption interval data and the mining face layout data, the energy consumption characteristics of the equipment are associated with the spatial position of the working face. Spatial matching obtains the working face equipment energy consumption mapping data by calculating the energy consumption level of the equipment at different working face positions. Hierarchical clustering is a bottom-up clustering method that gradually merges working faces according to energy consumption similarity to form hierarchical data of mining and installation operation energy consumption.

[0031] Based on the weighted calculation of the energy consumption level data of the procurement and installation operations and the transportation route distance data, a segmented transportation cost calculation formula is introduced: ; in, is the total transportation cost, is the number of transport sections, is the energy consumption coefficient of the plane transportation in the i-th section, is the transportation distance of the i-th section, is the load factor of the i-th section, is the energy consumption coefficient of the i-th vertical transport, is the elevation difference of the i-th section, is the slope correction coefficient of the i-th section, is the weight coefficient of the i-th section road condition.

[0032] The transport energy consumption weight data is calculated through this formula, and the transport energy consumption weight data is accumulated in sections to obtain the section transport cost data. The section transport cost data is combined with the monthly mining plan data of the mining area to perform task decomposition, and the large-scale mining tasks are divided into basic operation units to obtain the basic operation unit data. The basic operation unit data is processed with energy consumption constraints, and the operation plans that exceed the energy consumption standard are eliminated to obtain the energy-efficient feasible task set.

[0033] The boundary constraint processing of the energy-efficient feasible task set and the equipment operation range data mainly considers the equipment's operation radius and regional restrictions. The spatial distribution characteristics of the task are determined by boundary constraints to obtain the task space distribution data. The task space distribution data is sorted in chronological order to form the initial task time series data. The initial task time series data and the equipment energy efficiency grade data are matched and optimized. According to the task requirements and equipment performance characteristics, the most suitable equipment is selected to perform the corresponding task, and the corresponding data of the task equipment is obtained. Finally, through time and space coordination processing, it is ensured that the operations between multiple devices do not conflict, and a collaborative task sequence oriented to energy efficiency is formed.

[0034] Take an open-pit mine as an example. The mine is equipped with 20 mining and loading equipment. The equipment is divided into three energy efficiency levels: A, B, and C through quantification of performance indicators. The corresponding energy consumption ranges are 30-40L / h, 40-50L / h, and 50-60L / h, respectively. There are 4 mining working faces in the mine. After spatial matching and hierarchical clustering analysis, the working faces are divided into 2 energy consumption levels: the first working face (average energy consumption 35L / h) and the second working face (average energy consumption 45L / h). The total length of the transportation route is 15 kilometers, which is divided into 8 sections. The transportation cost of each section is calculated using the above formula. Take the first section as an example: the plane distance is 1.2 kilometers ( =1.2), elevation difference 50 meters ( =50), full load factor 1.5 ( =1.5), slope correction factor 1.2 ( =1.2), road condition weight 1.1 ( =1.1), and calculate the transportation cost for this section.

[0035] The monthly mining plan is 3 million tons, which is broken down into 2,000 basic operation units, with an average of 1,500 tons per unit. After considering energy consumption constraints, 1,800 feasible tasks were screened out. The equipment operation range is limited to a radius of 2 kilometers. Through boundary constraints and time sequence sorting, an 8-hour shift operation plan is formed. The final matching results show that Class A equipment is mainly assigned to long-distance transportation tasks, Class B equipment performs medium-distance transportation, and Class C equipment is responsible for short-distance transportation, thereby achieving collaborative operations with optimal energy efficiency.

[0036] In a specific embodiment, the process of executing step S104 may specifically include the following steps: (1) Perform pre-requirement extraction on the energy efficiency-oriented collaborative task sequence to obtain task constraint data, and then standardize the task constraint data to obtain normalized constraint indicator data; (2) Perform distance calculation on the normalized constraint index data and the real-time location data of the equipment group to obtain the equipment task distance matrix data, and perform energy consumption estimation on the equipment task distance matrix data to obtain the equipment scheduling cost data; (3) Perform fitness calculation on the equipment scheduling cost data and equipment operation capacity data to obtain equipment task matching data, and then perform optimal allocation processing on the equipment task matching data using the Hungarian algorithm to obtain initial allocation plan data; (4) Perform conflict detection on the initial allocation plan data and historical job record data to obtain task conflict point set data, and perform time window adjustment on the task conflict point set data to obtain feasible scheduling time series data; (5) Dynamically balance the feasible scheduling time series data and the energy consumption status data of the equipment group to obtain the equipment load distribution data, and then perform work face collaborative processing on the equipment load distribution data to obtain an integrated resource allocation plan for procurement, loading and transportation.

[0037] Specifically, in the integrated intelligent scheduling method for open-pit mining, loading and transportation, the pre-demand extraction process is the process of constraint analysis of collaborative task sequences for energy efficiency. Task constraint data includes time constraints (task start time, completion deadline), space constraints (operating area scope, equipment driving route) and resource constraints (equipment type requirements, staffing requirements) and other aspects. Standardization processing converts different types of constraints into indicators of unified dimensions to form standardized constraint indicator data, which provides a basis for subsequent matching calculations. The distance calculation process between the standardized constraint indicator data and the real-time location data of the equipment group adopts the Euclidean distance calculation method. The actual distance between each device and each task point is calculated to construct the equipment task distance matrix data. The energy consumption estimation process is based on the distance matrix, combined with factors such as road slope and road conditions, to calculate the energy required to complete each task and obtain the equipment scheduling cost data.

[0038] In the process of calculating the fitness of equipment scheduling cost data and equipment operating capacity data, the rated load, operating efficiency, energy consumption level and other factors of the equipment are comprehensively considered to calculate the fitness of each equipment to perform various tasks and obtain the equipment task matching data. The Hungarian algorithm is a classic algorithm for solving task allocation problems. This algorithm uses iterative optimization to find the allocation plan with the minimum total cost based on the equipment task matching data and generate the initial allocation plan data. When the initial allocation plan data and the historical operation record data are processed for conflict detection, the conflict of the equipment in the spatial and temporal dimensions is mainly checked. By analyzing the driving route and operation sequence of the equipment, the task points with intersections and overlaps are found to form the task conflict point set data. The time window adjustment process is to arrange the conflicting tasks in a staggered time, adjust the execution sequence of the tasks, and generate feasible scheduling sequence data.

[0039] The dynamic balance processing of feasible scheduling time series data and equipment group energy consumption status data focuses on the balance of equipment load. According to the current energy consumption level and remaining operating capacity of the equipment, the tasks are redistributed to obtain equipment load distribution data. The collaborative processing of the working face coordinates the collection and transportation operations to ensure smooth connection of each link, and finally form an integrated resource allocation plan for collection, loading and transportation.

[0040] Take an open-pit mine as an example. The mine has 25 mining and loading equipment, including 5 excavators, 8 loaders and 12 mining trucks. Through pre-demand extraction, it is determined that the daily mining task of 300,000 tons requires 300 operation units, the time window of each operation unit is 2 hours, and the operation area is limited to 2000 meters × 1500 meters. The normalized constraint index converts the time window into a 0-1 standard score, and the spatial constraint is converted into a relative distance ratio. The distance calculation process constructs a 25×300 distance matrix, and each cell represents the distance from the specific equipment to the task point. For example, the distance from mining truck No. 1 to task point A1 is 1.2 kilometers, and the energy consumption estimate shows that 25 liters of fuel are required to complete the task. Equipment operating capacity data shows that the truck has a rated load of 90 tons, an average operating efficiency of 300 tons / hour, and an energy consumption level of 40 liters / hour.

[0041] Through iterative optimization of the Hungarian algorithm, the initial allocation plan shows that 12 mining trucks each undertake 20-30 transportation tasks. Conflict detection found that there were intersections in three sections, two of which were located near the loading point and one was located on the main transportation road. Through time window adjustment, the interval between conflicting tasks was set to 15 minutes, which effectively avoided equipment congestion. The results of dynamic balance processing showed that through task redistribution, the load difference of each device was controlled within 10%. The final integrated resource allocation plan for mining, loading and transportation achieved close coordination of the three links of mining, loading and transportation. The excavation rhythm of the excavator matched the loading capacity of the loader, the turnover of the transport vehicle was highly coordinated with the loading operation, the overall operation efficiency was improved by 25%, and the equipment utilization rate reached more than 85%.

[0042] In a specific embodiment, the process of executing step S105 may specifically include the following steps: (1) Perform time series analysis on the integrated resource allocation plan for procurement, loading and transportation to obtain equipment movement time series data, and perform cross-detection processing on the equipment movement time series data to obtain equipment intersection prediction data; (2) Calculate the safety distance of the equipment intersection prediction data and the road bearing capacity data to obtain the minimum safety distance data, and perform capacity analysis on the minimum safety distance data and road width data to obtain the road capacity data; (3) Perform status assessment on road capacity data and real-time road condition monitoring data to obtain road traffic grade data, and perform statistical processing on the road traffic grade data by time period to obtain road congestion warning data; (4) Perform time window adjustment processing on the road congestion warning data and the equipment group dispatch sequence data to obtain the peak-shifting dispatch plan data, and perform collaborative optimization processing on the peak-shifting dispatch plan data and the equipment energy consumption status data to obtain the road resource allocation data; (5) Perform deviation analysis on the road resource allocation data and the equipment operation trajectory data to obtain trajectory correction parameter data, and perform comprehensive decision processing on the trajectory correction parameter data and the current operation status data to obtain the equipment group adaptive scheduling strategy.

[0043] Specifically, the time series analysis and processing of the integrated resource allocation plan for procurement, loading and transportation is the first step. This process converts the equipment scheduling tasks into time series, records the expected position and motion state of each equipment at different times, and thus obtains the equipment movement time series data. By performing cross detection processing on the equipment movement time series data, the time and space intersections of the running trajectories of different equipment are analyzed, the intersection time and position of the equipment during operation are predicted, and the equipment intersection prediction data is generated. The equipment intersection prediction data is combined with the road bearing capacity data to calculate the safe distance. The road bearing capacity data contains information such as road strength, road width, and slope. According to the braking performance of the equipment, road conditions, and driving speed, the safe stopping distance is calculated to form the minimum safe spacing data. The traffic capacity analysis of the minimum safe spacing data and road width data is performed. Considering the equipment meeting conditions under the road width limit, the road traffic capacity data is calculated, which represents the maximum number of equipment that can pass through the road in unit time.

[0044] During the status assessment process of road traffic capacity data and real-time road condition monitoring data, the road traffic status is comprehensively analyzed, including factors such as road conditions, weather impacts, and equipment density, to obtain road traffic grade data. The road traffic grade data is statistically processed by time period, and the traffic pressure changes in different time periods are analyzed to form road congestion warning data. The time window of the road congestion warning data and the equipment group scheduling sequence data is adjusted to avoid traffic congestion by adjusting the equipment operation time. The adjusted peak-shifting scheduling plan data and equipment energy consumption status data are collaboratively optimized to calculate the road resource allocation data. Road resource allocation is calculated using the following formula: ; Among them, RD is the road resource allocation index, is the number of road segments, m is the number of devices, is the traffic weight of the i-th road section, is the speed coefficient of the jth device, is the time period correction coefficient, is the road condition influencing factor, is the energy consumption weight of the i-th road section, is the energy consumption coefficient of the jth device. is the energy consumption correction factor for the period, is the proportion of time that equipment j occupies on road segment i.

[0045] Deviation analysis is performed on the road resource allocation data and the equipment operation trajectory data, the deviation between the actual operation trajectory and the planned path is calculated, and trajectory correction parameter data is generated. A comprehensive decision-making process is performed on the trajectory correction parameter data and the current operation status data, and the scheduling strategy is dynamically adjusted according to factors such as operation progress and equipment status, ultimately forming an adaptive scheduling strategy for the equipment group.

[0046] Taking the actual application of an open-pit mine as an example, the mine area is equipped with 35 mining and loading equipment, and the total length of the transportation road is 18 kilometers. Time series analysis shows that each device runs 12 times per shift on average, and 86 potential intersection points are identified through cross detection. Safety distance calculation shows that on the main road (slope 8%, road width 12 meters), heavy-loaded vehicles need to maintain a safety distance of more than 50 meters, and unloaded vehicles need to maintain a safety distance of more than 35 meters. The results of road traffic capacity analysis show that the maximum traffic volume per hour on the main road is 60 vehicles, and that on the secondary road is 40 vehicles. Road condition monitoring data is divided into three states: sunny (traffic level A), light rain (traffic level B), and heavy rain (traffic level C). Statistics show that 9-11 am and 3-5 pm are traffic peaks, and the risk of congestion increases by 50%.

[0047] By adjusting the time window, the 30 devices originally scheduled to be dispatched at the same time were dispersed into three time periods, with 10 devices in each period, reducing the probability of traffic congestion. Energy consumption data showed that after avoiding congested sections, the average fuel consumption of the equipment was reduced from 45L / h to 38L / h. Trajectory deviation analysis found that the average deviation between the actual operation trajectory and the planned path was within the range of 3-5 meters. Based on this deviation feature, the dispatch path of subsequent equipment was dynamically adjusted, which increased the overall transportation efficiency by 20%.

[0048] In a specific embodiment, the process of executing step S106 may specifically include the following steps: (1) Perform timeliness analysis on the adaptive scheduling strategy of the equipment group to obtain strategy execution time limit data, and perform segmented monitoring on the strategy execution time limit data to obtain scheduling execution progress data; (2) Perform difference comparison processing on the scheduling execution progress data and the real-time production data to obtain the production deviation index data, and perform attribution analysis on the production deviation index data to obtain the deviation cause data; (3) Perform pattern recognition processing on the deviation cause data and equipment operation record data to obtain typical deviation pattern data, and perform correlation analysis on the typical deviation pattern data and historical disposal plan data to obtain strategy adjustment recommendation data; (4) Performing scenario adaptation processing on the strategy adjustment suggestion data and the current device status data to obtain the corrected control parameter data, and performing regional collaborative processing on the corrected control parameter data to obtain the device group control instruction data; (5) Perform feasibility verification processing on the equipment group control instruction data and the operating environment constraint data to obtain instruction reliability evaluation data, and perform multi-objective trade-off processing on the instruction reliability evaluation data to obtain alternative instruction solution data; (6) The matching degree of the alternative instruction scheme data and the production target data is calculated and processed to obtain the instruction optimization result data, and the instruction optimization result data is processed for time and space consistency to obtain the procurement, loading and transportation collaborative optimization instructions.

[0049] Specifically, the adaptive scheduling strategy of the equipment group is analyzed and processed for timeliness, focusing on the execution time window and key nodes of the strategy. Timeliness analysis is based on the urgency of the task, the equipment operation cycle time and the production plan requirements to form the strategy execution time limit data. The strategy execution time limit data is monitored in segments, the entire execution cycle is divided into multiple time segments, and the task completion status in each time segment is recorded to obtain the scheduling execution progress data. In the process of comparing the difference between the scheduling execution progress data and the real-time production data, by comparing the planned output with the actual output, the planned time with the actual time and other indicators, the deviation value in the production process is calculated to form the production deviation index data. The production deviation index data is analyzed for attribution, and the causes of the deviation are found from multiple dimensions such as equipment performance, road conditions, and weather conditions to obtain the deviation cause data.

[0050] The pattern recognition processing of deviation cause data and equipment operation record data adopts cluster analysis method to classify similar deviation patterns, extract common features, and form typical deviation pattern data. The typical deviation pattern data and historical disposal plan data are correlated and analyzed to find effective treatment measures taken in similar deviation situations in history, and generate strategy adjustment suggestion data. The scenario adaptation processing of strategy adjustment suggestion data and current equipment status data mainly considers factors such as the real-time working condition of the equipment, remaining energy, and working environment to ensure the feasibility of the adjustment suggestion in the current scenario and obtain the corrected control parameter data. The corrected control parameter data is processed in a regional collaborative manner to ensure that the dispatching instructions between various operating areas are coordinated and consistent, forming equipment group control instruction data.

[0051] During the feasibility verification process of the equipment group control instruction data and the operating environment constraint data, check whether the dispatch instruction meets the constraints such as safe spacing, road conditions, equipment capacity, etc., and generate instruction reliability evaluation data. Perform multi-objective trade-offs on the instruction reliability evaluation data, and form alternative instruction scheme data on the basis of ensuring multiple goals such as production efficiency, energy utilization, and equipment life. Calculate the matching degree of the alternative instruction scheme data and the production target data, evaluate the degree of satisfaction of each scheme with the production target, and obtain the instruction optimization result data. Perform time and space consistency processing on the instruction optimization result data to ensure the coordination and unification of dispatch instructions in different regions and different time periods, and finally generate procurement and loading and transportation collaborative optimization instructions.

[0052] Taking the actual application of an open-pit mine as an example, the mine is equipped with 45 mining and loading equipment, with a daily output plan of 500,000 tons. The timeliness analysis divides the 8-hour work shift into 16 30-minute time segments, and sets a production target of 31,250 tons for each time segment. Segment monitoring shows that in the third time segment in the morning, the actual output was 28,000 tons, resulting in a negative deviation of 32,500 tons. The difference comparison analysis found that the operating time of two transport vehicles exceeded expectations during this period, with an average increase of 4 minutes per trip. Attribution analysis shows that the main reason is that the slippery road surface in a specific section of the road caused the vehicle speed to decrease. Pattern recognition processing extracted 5 typical deviation patterns from 300 recent deviation records, of which transportation delays caused by road conditions accounted for 35%. Historical disposal plans show that a combination of temporary diversion and adjustment of loading capacity can effectively alleviate similar situations.

[0053] During the scene adaptation process, control parameters for adjusting the transport route were generated taking into account the current equipment status (15 transport vehicles are in the best working condition) and the characteristics of the operating area (sufficient carrying capacity of the backup road). Regional collaborative processing ensures balanced traffic after the diversion, and the equipment group control instructions require 8 vehicles to use the backup route. Feasibility verification shows that the new route meets safety requirements, but will increase energy consumption by 5%. After weighing multiple objectives, three alternative plans were formed: complete diversion, partial diversion, and maintaining the original route but reducing the load. Through matching calculation, the complete diversion plan has the highest production target satisfaction, and this plan was finally selected as the optimization instruction. After executing this plan, the output of the subsequent time segment reached 32,000 tons, successfully making up for the previous deviation.

[0054] The above describes the open-pit mine mining and loading integrated intelligent scheduling method in the embodiment of the present application. The following describes the open-pit mine mining and loading integrated intelligent scheduling device in the embodiment of the present application. Figure 2 In the embodiment of the present application, an embodiment of the open-pit mine mining and loading integrated intelligent dispatching device includes: Fusion module 201 is used to perform multi-source information fusion processing on the topographic and geomorphic feature data of the open-pit mine area and the real-time working condition data of the mining and loading equipment group to obtain the collaborative operation scene data of the mine area; Extraction module 202, used to perform intelligent feature extraction processing on the mining area collaborative operation scene data and the energy consumption characteristic data of the mining and loading equipment group to obtain the equipment group operation efficiency index; A decoupling module 203 is used to perform hierarchical decoupling processing on the equipment group operation efficiency index and the mining area mining planning data to obtain a collaborative task sequence oriented to energy efficiency; A matching module 204 is used to perform group intelligent matching processing on the energy efficiency-oriented collaborative task sequence and the spatiotemporal distribution data of the equipment group to obtain a procurement, loading and transportation integrated resource allocation plan; The coordination module 205 is used to dynamically coordinate the procurement, loading and transportation integrated resource allocation plan and the road traffic constraint data to obtain an adaptive scheduling strategy for the equipment group; The iteration module 206 is used to perform closed-loop iterative processing on the adaptive scheduling strategy of the equipment group and the production process feedback data to obtain procurement, loading and transportation collaborative optimization instructions.

[0055] Through the collaborative cooperation of the above components, the terrain characteristics and equipment operating status of the open-pit mine area are fully perceived by multi-source information fusion processing of the terrain and geomorphic feature data of the open-pit mine area and the real-time working condition data of the mining and loading equipment group, providing a reliable data basis for subsequent scheduling decisions. At the same time, the intelligent feature extraction and processing of the mining area collaborative operation scene data and the energy consumption characteristic data of the mining and loading equipment group are carried out, and the energy consumption law in the equipment operation process is deeply excavated, providing a basis for energy saving optimization. Based on the hierarchical decoupling processing of the equipment group operation efficiency indicators and the mining planning data of the mine area, the reasonable decomposition and scientific organization of the mining and loading operation tasks are realized, and the feasibility of task execution is effectively improved. By performing group intelligent matching processing on the energy-efficiency-oriented collaborative task sequence and the spatiotemporal distribution data of the equipment group, the optimal configuration of equipment resources is realized, and the equipment utilization efficiency is significantly improved. Combined with the integrated mining and loading resource allocation plan and the dynamic coordination processing of road traffic constraint data, a flexible scheduling strategy adjustment mechanism is established to effectively deal with various uncertain factors in the production process. Finally, the adaptive scheduling strategy of the equipment group and the feedback data of the production process are processed in a closed loop iteration, forming a complete closed-loop control system of optimization-execution-feedback-adjustment, which ensures the continuous optimization of the scheduling plan and the stable operation of the production process. Through the organic combination of these technical features, the present invention not only improves the production efficiency and equipment utilization rate of the open-pit mine area, but also reduces energy consumption and saves production costs, while enhancing the adaptability of the scheduling system to complex working conditions.

[0056] The present application also provides a computer-readable storage medium, which may be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium. Instructions are stored in the computer-readable storage medium. When the instructions are executed on a computer, the computer executes the steps of the open-pit mine mining, loading and transportation integrated intelligent scheduling method.

[0057] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described systems, systems and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0058] If the integrated unit is implemented in the form of 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 the present application is essentially 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. The computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk and other media that can store program codes.

[0059] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. An integrated intelligent dispatching method for open-pit mining, loading and transportation, characterized in that: The open-pit mine integrated intelligent scheduling method comprises: Multi-source information fusion processing is performed on the topographic and geomorphic feature data of the open-pit mine area and the real-time working condition data of the mining and loading equipment group to obtain the collaborative operation scene data of the mine area; Perform intelligent feature extraction and processing on the mining area collaborative operation scene data and the energy consumption characteristic data of the mining and loading equipment group to obtain the equipment group operation efficiency index; Performing hierarchical decoupling processing on the equipment group operation efficiency index and the mining area mining planning data to obtain a collaborative task sequence oriented to energy efficiency; Performing group intelligent matching processing on the energy efficiency-oriented collaborative task sequence and the spatiotemporal distribution data of the equipment group to obtain a resource allocation plan for integrated procurement, loading and transportation; Dynamically coordinate the integrated resource allocation plan for procurement, loading and transportation and the road traffic constraint data to obtain an adaptive scheduling strategy for the equipment group; The adaptive scheduling strategy of the equipment group and the feedback data of the production process are processed in a closed loop to obtain the coordinated optimization instructions of procurement, loading and transportation.

2. The open-pit mine mining, loading and transportation integrated intelligent scheduling method according to claim 1 is characterized in that: The multi-source information fusion processing of the topographic and geomorphic feature data of the open-pit mine area and the real-time working condition data of the mining and loading equipment group is performed to obtain the mining area collaborative operation scene data, including: Performing layered and partitioned processing on the topographic and geomorphic feature data of the open-pit mine area to obtain mining area operation level data, and performing boundary feature extraction processing on the mining area operation level data to obtain an operation area boundary point set; Performing regional connectivity analysis on the boundary point set of the operation area to obtain data on the passable area for mining and loading equipment, and performing contour extraction on the passable area data through a boundary tracking algorithm to obtain data on the mine area passable range; Performing spatiotemporal correlation processing on the location data and speed data of the collection and transportation equipment group to obtain dynamic distribution data of the equipment group, and performing density clustering processing on the dynamic distribution data of the equipment group to obtain operation cluster area data of the equipment group; Performing correlation analysis on the equipment group operation cluster area data and the equipment engine speed data to obtain equipment group working condition characteristic data, and performing energy consumption evaluation on the equipment group working condition characteristic data to obtain equipment group energy consumption status data; Performing regional division processing on the mine area traffic range data and the equipment group energy consumption status data to obtain energy consumption partition data, and performing grade division processing on the energy consumption partition data to obtain grade data of partition energy consumption; Performing frequency statistics processing on the partition energy consumption level data and historical operation records to obtain regional operation frequency data, and performing weight calculation processing on the regional operation frequency data to obtain regional operation intensity data; The regional operation intensity data and real-time equipment status data are matched and associated to obtain mining area collaborative operation scene data.

3. The open-pit mine mining, loading and transportation integrated intelligent scheduling method according to claim 1 is characterized in that: The intelligent feature extraction and processing of the mining area collaborative operation scene data and the energy consumption characteristic data of the mining and loading equipment group is performed to obtain the equipment group operation efficiency index, including: Performing regional classification processing on the mining area collaborative operation scene data to obtain scene feature type set data, and performing statistical aggregation processing on the scene feature type set data to obtain regional operation mode data; Performing spatiotemporal correlation processing on the regional operation mode data and the equipment group GPS trajectory data to obtain equipment group motion feature data, and performing frequent pattern extraction processing on the equipment group motion feature data through sequence pattern mining to obtain typical operation sequence data; Performing time series matching processing on the typical operation sequence data and the instantaneous fuel consumption data of the equipment group to obtain operation energy consumption corresponding data, and performing change rate calculation processing on the operation energy consumption corresponding data to obtain equipment group energy consumption gradient data; Perform multi-dimensional correlation processing on the energy consumption gradient data of the equipment group and the equipment load data to obtain unit load energy consumption data, and perform outlier detection processing on the unit load energy consumption data to obtain energy consumption threshold interval data; Performing response characteristic analysis on the energy consumption threshold interval data and the equipment engine speed data to obtain equipment power response data, and performing feature dimension reduction processing on the equipment power response data through principal component analysis to obtain key characteristic factor data; The key characteristic factor data and the equipment hydraulic system pressure data are combined and analyzed to obtain equipment comprehensive performance data, and the equipment comprehensive performance data is quantitatively scored to obtain equipment group operation efficiency indicators.

4. The open-pit mine mining, loading and transportation integrated intelligent scheduling method according to claim 1 is characterized in that: The hierarchical decoupling of the equipment group operation efficiency index and the mining area mining planning data is performed to obtain a collaborative task sequence oriented to energy efficiency, including: Performing grade quantification processing on the equipment group operation efficiency indicators to obtain equipment energy efficiency grading data, and performing threshold segmentation processing on the equipment energy efficiency grading data to obtain equipment operation energy consumption interval data; Performing spatial matching processing on the equipment operation energy consumption interval data and the mining working face layout data to obtain working face equipment energy consumption mapping data, and performing hierarchical clustering processing on the working face equipment energy consumption mapping data to obtain mining and loading operation energy consumption level data; Performing weighted calculation processing on the energy consumption level data of the collection and loading operation and the transportation route distance data to obtain transportation energy consumption weight data, and performing segmented accumulation processing on the transportation energy consumption weight data to obtain segmented transportation cost data; Performing task decomposition processing on the segmented transportation cost data and the monthly mining plan data of the mining area to obtain basic operation unit data, and performing energy consumption constraint processing on the basic operation unit data to obtain an energy-efficient feasible task set; Performing boundary constraint processing on the energy efficiency feasible task set and equipment operation range data to obtain task space distribution data, and performing time series sorting processing on the task space distribution data to obtain initial task time series data; The initial task timing data and the equipment energy efficiency level data are matched and optimized to obtain task equipment corresponding data, and the task equipment corresponding data are coordinated in time and space to obtain a collaborative task sequence oriented to energy efficiency.

5. The open-pit mine mining, loading and transportation integrated intelligent scheduling method according to claim 1 is characterized in that: The energy efficiency-oriented collaborative task sequence and the equipment group spatiotemporal distribution data are subjected to group intelligent matching processing to obtain a procurement, loading and transportation integrated resource allocation plan, including: Performing pre-requirement extraction processing on the energy efficiency-oriented collaborative task sequence to obtain task constraint condition data, and performing standardization processing on the task constraint condition data to obtain normalized constraint indicator data; Performing distance calculation processing on the normalized constraint index data and the real-time location data of the equipment group to obtain equipment task distance matrix data, and performing energy consumption estimation processing on the equipment task distance matrix data to obtain equipment scheduling cost data; Performing fitness calculation processing on the equipment scheduling cost data and the equipment operation capacity data to obtain equipment task matching data, and performing optimal allocation processing on the equipment task matching data through the Hungarian algorithm to obtain initial allocation plan data; Performing conflict detection processing on the initial allocation plan data and the historical operation record data to obtain task conflict point set data, and performing time window adjustment processing on the task conflict point set data to obtain feasible scheduling time series data; The feasible scheduling time series data and the energy consumption status data of the equipment group are dynamically balanced to obtain equipment load distribution data, and the equipment load distribution data is collaboratively processed on the working surface to obtain an integrated resource allocation plan for procurement, loading and transportation.

6. The open-pit mine mining, loading and transportation integrated intelligent scheduling method according to claim 1 is characterized in that: The method of dynamically coordinating the integrated resource allocation scheme for procurement, loading and transportation and the road traffic constraint data to obtain an adaptive scheduling strategy for the equipment group includes: Performing time series analysis on the integrated procurement, loading and transportation resource allocation plan to obtain equipment movement time series data, and performing cross detection on the equipment movement time series data to obtain equipment intersection prediction data; Performing safety distance calculation processing on the equipment intersection prediction data and the road bearing capacity data to obtain minimum safety distance data, and performing traffic capacity analysis processing on the minimum safety distance data and the road width data to obtain road traffic capacity data; Performing status evaluation processing on the road traffic capacity data and the real-time road condition monitoring data to obtain road traffic grade data, and performing time-division statistical processing on the road traffic grade data to obtain road congestion warning data; Performing time window adjustment processing on the road congestion warning data and the equipment group scheduling sequence data to obtain peak-shifting scheduling plan data, and performing collaborative optimization processing on the peak-shifting scheduling plan data and the equipment energy consumption status data to obtain road resource allocation data; Deviation analysis is performed on the road resource allocation data and the equipment operation trajectory data to obtain trajectory correction parameter data, and comprehensive decision processing is performed on the trajectory correction parameter data and the current operation status data to obtain an equipment group adaptive scheduling strategy.

7. The open-pit mine mining, loading and transportation integrated intelligent scheduling method according to claim 1 is characterized in that: The closed-loop iterative processing of the equipment group adaptive scheduling strategy and the production process feedback data to obtain procurement, loading and transportation collaborative optimization instructions includes: Performing timeliness analysis on the adaptive scheduling strategy of the equipment group to obtain strategy execution time limit data, and performing segmented monitoring on the strategy execution time limit data to obtain scheduling execution progress data; Performing difference comparison processing on the scheduling execution progress data and the real-time production data to obtain production deviation index data, and performing attribution analysis processing on the production deviation index data to obtain deviation cause data; Performing pattern recognition processing on the deviation cause data and the equipment operation record data to obtain typical deviation pattern data, and performing correlation analysis processing on the typical deviation pattern data and the historical disposal plan data to obtain strategy adjustment suggestion data; Performing scene adaptation processing on the strategy adjustment suggestion data and the current device status data to obtain corrected control parameter data, and performing regional collaborative processing on the corrected control parameter data to obtain device group control instruction data; Performing feasibility verification processing on the equipment group control instruction data and the operating environment constraint data to obtain instruction reliability evaluation data, and performing multi-objective trade-off processing on the instruction reliability evaluation data to obtain alternative instruction solution data; The matching degree of the alternative instruction scheme data and the production target data is calculated and processed to obtain instruction optimization result data, and the instruction optimization result data is processed for time and space consistency to obtain procurement, loading and transportation collaborative optimization instructions.

8. An open-pit mine mining and loading integrated intelligent dispatching device, used to implement the open-pit mine mining and loading integrated intelligent dispatching method as described in any one of claims 1 to 7, characterized in that: The open-pit mine mining, loading and transportation integrated intelligent dispatching device comprises: The fusion module is used to perform multi-source information fusion processing on the topographic and geomorphic feature data of the open-pit mine area and the real-time working condition data of the mining and loading equipment group to obtain the collaborative operation scene data of the mine area; An extraction module is used to perform intelligent feature extraction processing on the mining area collaborative operation scene data and the energy consumption characteristic data of the mining and loading equipment group to obtain the equipment group operation efficiency index; A decoupling module, used for performing hierarchical decoupling processing on the equipment group operation efficiency index and the mining area mining planning data to obtain a collaborative task sequence oriented to energy efficiency; A matching module, used to perform group intelligent matching processing on the energy efficiency-oriented collaborative task sequence and the spatiotemporal distribution data of the equipment group to obtain a resource allocation plan for integrated procurement, loading and transportation; A coordination module is used to dynamically coordinate the integrated resource allocation scheme for procurement, loading and transportation and the road traffic constraint data to obtain an adaptive scheduling strategy for the equipment group; The iteration module is used to perform closed-loop iterative processing on the adaptive scheduling strategy of the equipment group and the feedback data of the production process to obtain the procurement, loading and transportation collaborative optimization instructions.

9. A computer-readable storage medium having instructions stored thereon, characterized in that: When the instructions are executed by the processor, the open-pit mine mining, loading and transportation integrated intelligent scheduling method as described in any one of claims 1-7 is implemented.

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