Automatic operation system and method for unmanned cab mining dump truck

Abstract

The present invention relates to the technical field of automatic unloading truck adjustment, and specifically discloses an automatic operation system and method for an unmanned cab mining dump truck. The system collects performance parameters and environmental parameters of various data collection devices belonging to the unmanned cab mining dump truck, provides a reliable data basis for data perception feedback of the unmanned cab mining dump truck, collects and analyzes the operating status parameters of the unmanned cab mining dump truck, provides an accurate data basis for the feedback of the operating status of the unmanned cab mining dump truck, and simultaneously obtains and analyzes the automatic unloading process parameters of the unmanned cab mining dump truck, thereby managing and feedbacking the automatic operation of the unmanned cab mining dump truck, ensuring the comprehensive and accurate capture of various key parameters of the unmanned cab mining dump truck during the operation process, making the judgment of the automatic operation status of the unmanned cab mining dump truck, problem identification and formulation of optimization strategies more accurate and effective.

Description

Technical Field

[0001] The invention relates to the technical field of automatic dump truck adjustment, and in particular to an automatic operation system and method for an unmanned cab mining dump truck. Background Art

[0002] With the rapid development of the mining industry and the increasingly complex mining environment, traditional manually driven mining dump trucks can no longer meet the production needs of high efficiency, safety and low cost. Against this background, an automatic operation system and method for unmanned cab mining dump trucks came into being. The system integrates advanced sensor technology, artificial intelligence algorithms, high-precision positioning technology, wireless communication technology and automatic control technology, aiming to realize unmanned driving and automatic operation of mining dump trucks. Through automation, it can significantly improve the safety and efficiency of mining operations, reduce labor costs, reduce accident risks, and help achieve green mining and sustainable development of mines.

[0003] For example, the invention patent with the announcement number CN114815850B announces an unmanned mine car autonomous loading and unloading method and system; after the first mine car completes autonomous loading and unloading, it can analyze the environmental scanning information and the first loading and unloading position information of the first mine car to obtain the basic docking position, and then plan the driving route of the second mine car, control the second mine car to drive to the basic docking position, and make real-time adjustment planning, control the second mine car to drive to the adjusted loading and unloading position, and perform autonomous loading and unloading and feedback control of the second mine car at the adjusted loading and unloading position, so that effective cooperation can be carried out between the first mine car and the second mine car, and the route planning and control of the second mine car can be carried out based on the experience of the first mine car, avoiding unnecessary large-scale calculations and processing, and after the second mine car reaches the basic docking position, reasonable and safe adjustments can be made according to actual conditions, and it can quickly reach the adjusted loading and unloading position for autonomous loading and unloading.

[0004] For example, the invention patent with publication number CN116088497A discloses an automatic operation system and a full-process operation method for a cab-less mining dump truck. The automatic operation system includes a decision-making layer, a planning layer, and a control layer. On this basis, a full-process operation method for a mining dump truck is disclosed. It takes into account the driving state and obstacle avoidance while covering the entire operation scene of the loading and unloading area and the road driving area. Compared with the traditional unmanned driving solution for mining transport vehicles, it meets the special operating mechanism of cab-less vehicles, is more efficient and safe, and improves the unmanned transportation efficiency of cab-less mining dump trucks.

[0005] However, in the process of implementing the embodiments of the present application, it was found that the above-mentioned technology has at least the following technical problems: currently, when tracking and analyzing the operation of the unmanned cab mining dump truck, the data collected by the various data collection devices belonging to the unmanned cab mining dump truck are directly analyzed, and too much reliance is placed on the accuracy performance of each data collection device, which results in the adaptability and flexibility of the unmanned cab mining dump truck being limited, and it is difficult to quickly adjust to adapt to new environments or task requirements. Summary of the invention

[0006] In view of the deficiencies in the prior art, the present invention provides an automatic operation system and method for an unmanned cab mining dump truck, which can effectively solve the problems involved in the above-mentioned background technology.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: In the first aspect, the present invention provides an automatic operation system for an unmanned cab mining dump truck, including: a data perception evaluation module, which is used to collect the performance parameters of each data collection device belonging to the unmanned cab mining dump truck and the environmental parameters of the unmanned cab mining dump truck, and comprehensively evaluate the data perception energy efficiency factor of the unmanned cab mining dump truck; a data perception feedback module, which is used to compare the data perception energy efficiency factor of the unmanned cab mining dump truck with the data perception energy efficiency threshold. If the data perception energy efficiency factor of the unmanned cab mining dump truck is less than the data perception energy efficiency threshold, the data perception of the unmanned cab mining dump truck is fed back, otherwise , then the operating status parameters of the unmanned cab mining dump truck are collected; the operating status determination module is used to determine the operating accuracy index of the unmanned cab mining dump truck based on the operating status parameters of the unmanned cab mining dump truck, and compare it with the operating accuracy threshold. If the operating accuracy index of the unmanned cab mining dump truck is less than the operating accuracy threshold, the operating status of the unmanned cab mining dump truck is fed back, otherwise, the automatic unloading process parameters of the unmanned cab mining dump truck are obtained; the unloading process analysis module is used to analyze the unloading accuracy of the unmanned cab mining dump truck based on the automatic unloading process parameters of the unmanned cab mining dump truck, thereby providing management feedback on the automatic operation of the unmanned cab mining dump truck.

[0008] As a further solution, the data perception of the unmanned cab mining dump truck is fed back, and the specific feedback process is: the data perception energy efficiency threshold is processed with the data perception energy efficiency factor of the unmanned cab mining dump truck to obtain the data perception energy efficiency difference of the unmanned cab mining dump truck, and the data perception energy efficiency difference is obtained by matching the signal gain parameters corresponding to each data perception energy efficiency difference interval stored in the adjustment database to obtain the matching result, and the data perception of the unmanned cab mining dump truck is fed back, and based on the feedback result, the data perception energy efficiency factor of the unmanned cab mining dump truck is updated, and at the same time, the unmanned cab mining dump truck receives and executes automatic operation instructions.

[0009] As a further solution, the collection of operating status parameters of the unmanned cab mining dump truck specifically refers to the unmanned cab mining dump truck executing the cargo loading instructions in the automatic operation instructions and cooperating with the loaded minerals, thereby collecting the status parameters of the unmanned cab mining dump truck in the process of coordinated operation with the loaded cargo, which are the operating status parameters of the unmanned cab mining dump truck.

[0010] As a further solution, the automatic unloading process parameters of the unmanned cab mining dump truck are obtained, and the specific acquisition process is: obtaining the driving speed reference curve of the unmanned cab mining dump truck during the operation detection cycle, and overlapping and comparing it with the driving speed curve of the unmanned cab mining dump truck during the operation detection cycle, analyzing and obtaining the driving speed curve offset value of the unmanned cab mining dump truck during the operation detection cycle, matching the driving speed curve offset value with the adjustment speed instruction set corresponding to each driving speed curve offset value interval stored in the adjustment database, thereby the unmanned cab mining dump truck obtains and executes the adjustment speed instruction set, completes the feedback on the operating status of the unmanned cab mining dump truck, and updates the operating accuracy index of the unmanned cab mining dump truck, and the unmanned cab mining dump truck continues to execute the automatic operation instruction, thereby obtaining the automatic unloading process parameters of the unmanned cab mining dump truck.

[0011] As a further solution, the automatic operation of the unmanned mining dump truck is managed and fed back. The specific management feedback process is: the unloading accuracy of the unmanned mining dump truck is compared with the unloading accuracy threshold. If the unloading accuracy of the unmanned mining dump truck is greater than or equal to the unloading accuracy threshold, a success log of the automatic operation of the unmanned mining dump truck is generated and feedback is provided, thereby completing the automatic operation of the unmanned mining dump truck; if the unloading accuracy of the unmanned mining dump truck is less than the unloading accuracy threshold, the unloading accuracy threshold is differenced with the unloading accuracy of the unmanned mining dump truck to obtain the unloading accuracy difference of the unmanned mining dump truck, and the difference is matched with the unloading adjustment instruction set corresponding to each unloading accuracy difference interval stored in the adjustment database, thereby obtaining the unloading adjustment instruction set of the unmanned mining dump truck, and managing the automatic operation of the unmanned mining dump truck.

[0012] The second aspect of the present invention provides a method for an automatic operation system of an unmanned cab mining dump truck, comprising: S1. collecting performance parameters of each data collection device belonging to the unmanned cab mining dump truck and environmental parameters of the unmanned cab mining dump truck, and comprehensively evaluating the data perception energy efficiency factor of the unmanned cab mining dump truck; S2. comparing the data perception energy efficiency factor of the unmanned cab mining dump truck with the data perception energy efficiency threshold. If the data perception energy efficiency factor of the unmanned cab mining dump truck is less than the data perception energy efficiency threshold, the data perception of the unmanned cab mining dump truck is fed back. Otherwise, the data perception of the unmanned cab mining dump truck is collected. Operation status parameters of the mining dump truck; S3. Based on the operation status parameters of the unmanned cab mining dump truck, determine the operation accuracy index of the unmanned cab mining dump truck, and compare it with the operation accuracy threshold. If the operation accuracy index of the unmanned cab mining dump truck is less than the operation accuracy threshold, then the operation status of the unmanned cab mining dump truck is fed back. Otherwise, the automatic unloading process parameters of the unmanned cab mining dump truck are obtained; S4. Based on the automatic unloading process parameters of the unmanned cab mining dump truck, analyze the unloading accuracy of the unmanned cab mining dump truck, and thereby manage and feedback the automatic operation of the unmanned cab mining dump truck.

[0013] Compared with the prior art, the embodiments of the present invention have at least the following advantages or beneficial effects:

[0014] (1) The present invention provides an automatic operation system and method for an unmanned mining dump truck. By collecting the performance parameters of each data collection device of the unmanned mining dump truck and the environmental parameters of the unmanned mining dump truck, a reliable data basis is provided for the data perception feedback of the unmanned mining dump truck. The operating status parameters of the unmanned mining dump truck are collected, and the operating accuracy index of the unmanned mining dump truck is determined, thereby providing an accurate data basis for the feedback of the operating status of the unmanned mining dump truck. At the same time, the automatic unloading process parameters of the unmanned mining dump truck are obtained, and the unloading accuracy of the unmanned mining dump truck is analyzed. Thus, management feedback is provided for the automatic operation of the unmanned mining dump truck, ensuring the comprehensive and accurate capture of various key parameters of the unmanned mining dump truck during the operation process. High-quality data constitutes a solid foundation for intelligent decision-making and analysis, making the judgment of the automatic operation status of the unmanned mining dump truck, problem identification and formulation of optimization strategies more accurate and effective.

[0015] (2) The present invention focuses on analyzing the operating status parameters of the unmanned cab mining dump truck after loading minerals, determines the operating accuracy index of the unmanned cab mining dump truck, can accurately measure the performance of each unmanned vehicle in a specific loading scenario, and can also customize the personalized feedback on its operating characteristics, thereby optimizing the operating performance of the unmanned cab mining dump truck in a targeted manner.

[0016] (3) The present invention obtains the unloading accuracy of the unmanned cab mining dump truck by analyzing the automatic unloading process parameters of the unmanned cab mining dump truck and the operation accuracy index of the unmanned cab mining dump truck. This not only effectively simplifies the complexity of collecting automatic unloading process parameters, but also realizes the refined control of the unloading process, significantly improving the efficiency and safety of automated operations. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The present invention is further described using the accompanying drawings, but the embodiments in the accompanying drawings do not constitute any limitation to the present invention. A person skilled in the art can obtain other drawings based on the following drawings without creative work.

[0018] Figure 1 It is a schematic diagram of system module connection of the present invention.

[0019] Figure 2 The figure is a schematic flow chart of the method steps of the present invention.

[0020] Figure 3 It is a schematic diagram of the driving speed curve involved in the present invention.

[0021] Reference numerals: 1. Acceleration sample position point; 2. Fixed operation detection time interval. DETAILED DESCRIPTION

[0022] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0023] Reference Figure 1 As shown, the first aspect of the present invention provides an automatic operation system for an unmanned cab mining dump truck, comprising: a data perception evaluation module, a data perception feedback module, an operation status determination module and an unloading process analysis module.

[0024] The first aspect of the present invention provides an automatic operation system for an unmanned cab mining dump truck, which also includes an adjustment database, wherein the adjustment database is used to store reference ambient temperature values, reference light intensity, echo signal boundary intensity, signal transmission boundary duration, influence factors corresponding to unit values ​​of average dust concentration, weight factors corresponding to first data perception energy efficiency factors, weight factors corresponding to second data perception energy efficiency factors, signal gain parameters corresponding to each data perception energy efficiency difference interval, reference fuel efficiency ratio, driving reference acceleration, tire reference pressure, weights corresponding to data perception energy efficiency factors, speed adjustment instruction sets corresponding to each driving speed curve offset value interval, weight factors corresponding to operation accuracy indexes, influence factors corresponding to unit values ​​of discrete values ​​of inclination angle changes, influence factors corresponding to unit values ​​of discrete values ​​of unloading speed, unloading adjustment instruction sets corresponding to each unloading precision difference interval, data perception energy efficiency thresholds, operation accuracy thresholds, and unloading accuracy thresholds.

[0025] The data perception evaluation module is connected to the data perception feedback module, the data perception feedback module is connected to the operating status determination module, the operating status determination module is connected to the unloading process analysis module, and the data perception evaluation module, the data perception feedback module, the operating status determination module and the unloading process analysis module are all connected to the adjustment database.

[0026] The data perception evaluation module is used to collect the performance parameters of each data collection device belonging to the unmanned cab mining dump truck and the environmental parameters of the unmanned cab mining dump truck, and comprehensively evaluate the data perception energy efficiency factor of the unmanned cab mining dump truck.

[0027] In this embodiment, the above-mentioned data collection devices refer to devices that collect data using radar signals, including but not limited to millimeter wave radar, laser radar (LiDAR) and ultrasonic radar.

[0028] The performance parameters of the data collection devices of the unmanned cab mining dump truck include the average strength of the echo signal and the average signal transmission time. The environmental parameters of the unmanned cab mining dump truck include the average dust concentration, the average ambient temperature and the average light intensity. In addition to the above parameters, this example can also be used for other parameters.

[0029] Specifically, the data perception energy efficiency factor of the unmanned cab mining dump truck is evaluated, and the specific evaluation process is as follows:

[0030] The average dust concentration of the environmental area to which the unmanned cab mining dump truck belongs during the environmental detection period, the average environmental temperature value of the environmental area to which the unmanned cab mining dump truck belongs during the environmental detection period, and the average light intensity of the environmental area to which the unmanned cab mining dump truck belongs during the environmental detection period are extracted from the environmental parameters of the unmanned cab mining dump truck, and the reference environmental temperature value and the reference light intensity are extracted from the adjustment database, thereby comprehensively evaluating the first data perception energy efficiency factor of the unmanned cab mining dump truck; the above-mentioned environmental detection period represents the time period for detecting the environment to which the unmanned cab mining dump truck belongs, and the specific duration is determined by the system performance optimization consultant; the above-mentioned average dust concentration, average environmental temperature value and average light intensity can all be extracted from the environmental monitoring station of the environment to which the unmanned cab mining dump truck belongs.

[0031] The average echo signal strength of each data collection device of the unmanned cab mining dump truck during the environmental detection period and the average signal transmission duration of each data collection device of the unmanned cab mining dump truck during the environmental detection period are extracted from the performance parameters of each data collection device of the unmanned cab mining dump truck. The average echo signal strength of each data collection device of the unmanned cab mining dump truck during the environmental detection period refers to analyzing the strength of the echo signal corresponding to each signal emitted by each data collection device during the environmental detection period through the built-in signal receiving and processing function of each data collection device. The average strength of the echo signal is obtained by mean processing; the average signal transmission duration of each data collection device belonging to the above-mentioned unmanned cab mining dump truck within the environmental detection period specifically refers to the emission time point of each signal emitted by each data collection device within the environmental detection period and the reception time point of the echo signal corresponding to each emitted signal obtained through the built-in signal receiving and processing function of each data collection device, and the signal transmission duration of each data collection device within the environmental detection period is obtained by subtracting the emission time point from the reception time point. The average signal transmission duration of each data collection device within the environmental detection period is obtained by mean processing.

[0032] And match the echo signal definition strength and signal transmission definition time corresponding to the first data perception energy efficiency factor of the unmanned cab mining dump truck from the adjustment database, thereby comprehensively evaluating the second data perception energy efficiency factor of the unmanned cab mining dump truck; the above-mentioned echo signal definition strength and signal transmission definition time, the specific matching process is: the echo signal definition strength and signal transmission definition time corresponding to each first data perception energy efficiency factor interval stored in the adjustment database, the specific matching rules and values ​​are formulated by the technical research and development team according to the application scenarios and functional requirements of the unmanned cab mining dump truck, and the first data perception energy efficiency factor interval in the adjustment database corresponding to the first data perception energy efficiency factor of the unmanned cab mining dump truck is queried. The echo signal definition strength and signal transmission definition time corresponding to the first data perception energy efficiency factor interval are the echo signal definition strength and signal transmission definition time of the unmanned cab mining dump truck.

[0033] The first data perception energy efficiency factor of the unmanned cab mining dump truck and the second data perception energy efficiency factor of the unmanned cab mining dump truck are combined to obtain an evaluation method for the data perception energy efficiency factor of the unmanned cab mining dump truck. In this embodiment, the data perception energy efficiency factor of the unmanned cab mining dump truck is obtained by comprehensive analysis of the first data perception energy efficiency factor and the second data perception energy efficiency factor of the unmanned cab mining dump truck, and is used to evaluate the data perception collection capability of the unmanned cab mining dump truck. The specific evaluation method is:

[0034] ;

[0035] in,

[0036] ;

[0037] It is the first data perception energy efficiency factor of the unmanned cab mining dump truck, which refers to the value obtained by comprehensive analysis of the average dust concentration, average ambient temperature and average light intensity in the environmental area to which the unmanned cab mining dump truck belongs during the environmental detection period. It is used to evaluate the data perception and collection capabilities of the unmanned cab mining dump truck under the influence of the environment.

[0038] It is the second data perception energy efficiency factor of the unmanned cab mining dump truck, which refers to the value obtained by comprehensive analysis of the average echo signal strength and the average signal transmission time of each data collection device of the unmanned cab mining dump truck during the environmental detection period, and is used to evaluate the data perception and collection capability of the unmanned cab mining dump truck under the performance parameters (i.e., the average echo signal strength and the average signal transmission time).

[0039] It is the average dust concentration in the environmental area of ​​the unmanned cab mining dump truck during the environmental monitoring period, where dust refers to particles with a diameter less than or equal to 100 microns. The measurement of dust concentration is very important for evaluating the impact of the environment on the operation of the unmanned cab mining dump truck (such as sensor contamination and reduced visibility).

[0040] It is the average ambient temperature of the environmental area where the unmanned cab mining dump truck is located during the environmental detection period. The ambient temperature is one of the key factors affecting the performance of the unmanned cab mining dump truck (such as battery life and mechanical component operating efficiency) and sensor accuracy.

[0041] It is the average light intensity of the environment area where the unmanned cab mining dump truck is located during the environmental detection period. Monitoring the light intensity is helpful to adjust the sensor parameters and evaluate the operating conditions of the unmanned cab mining dump truck under different lighting conditions.

[0042] It is the average intensity of the echo signal of the cth data collection device belonging to the unmanned cab mining dump truck during the environmental detection period, which refers to the average intensity of each echo signal received after each signal sent by the data collection device during the environmental detection period encounters the target and is reflected back.

[0043] It is the average signal transmission duration of the cth data collection device belonging to the unmanned cab mining dump truck during the environmental detection cycle, which refers to the average duration between the time point of each signal sent by the data collection device during the environmental detection cycle and the corresponding echo signal received.

[0044] To adjust the reference ambient temperature value preset in the database, it represents the reference value for analyzing the average ambient temperature value of the environmental area to which the unmanned cab mining dump truck belongs during the environmental detection period.

[0045] In order to adjust the reference light intensity preset in the database, it represents the reference value for analyzing the average light intensity of the environmental area to which the unmanned cab mining dump truck belongs during the environmental detection period.

[0046] To adjust the preset echo signal boundary strength in the database, it represents analyzing the minimum value of the average echo signal strength of the cth data collection device belonging to the unmanned cab mining dump truck during the environmental detection cycle.

[0047] In order to adjust the preset signal transmission limit time in the database, it is indicated to analyze the maximum value of the average signal transmission time of the cth data collection device belonging to the unmanned cab mining dump truck during the environmental detection cycle.

[0048] To adjust the impact factor corresponding to the average dust concentration unit value preset in the database, which represents the numerical value of the impact degree of the average dust concentration unit value on the first data perception energy efficiency factor of the unmanned cab mining dump truck, the impact factor corresponding to the average dust concentration unit value can be directly obtained from the adjustment database when used, and the corresponding relationship can be a preset mapping relationship. For example, the average dust concentration and the impact factor corresponding to the average dust concentration unit value preset in the adjustment database form a mapping set, and the real-time average dust concentration is input into the mapping set to obtain the impact factor corresponding to the average dust concentration unit value. The mapping relationship can be one-to-one or many-to-one. In this example, its value range is [0, 1].

[0049] To adjust the weight factor corresponding to the first data-perceived energy efficiency factor preset in the database, it represents the proportion of the first data-perceived energy efficiency factor in the data-perceived energy efficiency factor of the unmanned cab mining dump truck. The weight factor corresponding to the first data-perceived energy efficiency factor can be directly obtained from the adjustment database when used. The corresponding relationship can be a preset mapping relationship. For example, the average dust concentration, the average ambient temperature value, and the average light intensity form a mapping set with the weight factor corresponding to the first data-perceived energy efficiency factor preset in the adjustment database. The real-time average dust concentration, the average ambient temperature value, and the average light intensity are input into the mapping set to obtain the weight factor corresponding to the first data-perceived energy efficiency factor. The mapping relationship can be one-to-one or many-to-one. In this example, its value range is [0, 1].

[0050] To adjust the weight factor corresponding to the second data-perception energy efficiency factor preset in the database, it represents the proportion of the second data-perception energy efficiency factor in the data-perception energy efficiency factor of the unmanned cab mining dump truck. The weight factor corresponding to the second data-perception energy efficiency factor can be directly obtained from the adjustment database when used. The corresponding relationship can be a preset mapping relationship. For example, the average strength of the echo signal and the average signal transmission time form a mapping set with the weight factor corresponding to the second data-perception energy efficiency factor preset in the adjustment database. The real-time average echo signal strength and the average signal transmission time are input into the mapping set to obtain the weight factor corresponding to the second data-perception energy efficiency factor. The mapping relationship can be one-to-one or many-to-one. In this example, its value range is [0, 1].

[0051] e is a natural constant, c is the number of each data collection device, , d is the total number of data collection devices.

[0052] In the formula, is the data perception energy efficiency factor of the unmanned cab mining dump truck. In the operating environment of the unmanned cab mining dump truck, the increase in the average dust concentration directly leads to a significant decrease in light intensity, which then indirectly affects the ambient temperature through the change in light intensity, forming a closely connected causal chain among the three. These three key parameters together constitute the core elements for evaluating the first data perception energy efficiency factor of the unmanned cab mining dump truck. This factor reflects the direct impact of the external environment on the data collection capability of the unmanned cab mining dump truck. In addition, environmental factors, as the driving force of the second data perception energy efficiency factor, directly act on the data collection device of the unmanned cab mining dump truck and affect its performance. Based on the above first data perception energy efficiency factor, the environmental factor directly affects the data collection device of the unmanned cab mining dump truck and affects its performance. The evaluation results of the efficiency factor can further define the performance of the unmanned cab mining dump truck in a specific environment. For example, the echo signal definition strength and signal transmission definition time can be matched through the first data perception energy efficiency factor of the unmanned cab mining dump truck. When the echo signal strength is low and the ratio to the echo signal definition strength is small, it indicates that the environment has a significant weakening effect on signal transmission, that is, the negative impact of the environment on the signal is high, thereby increasing the uncertainty and delay of data transmission, resulting in a longer average signal transmission time. Therefore, the above parameters jointly construct an indicator system for comprehensively evaluating the data collection capability of unmanned cab mining dump trucks, which provides an important basis for optimizing vehicle design, improving environmental adaptability and ensuring safe and efficient automatic operation.

[0053] The data perception feedback module is used to compare the data perception energy efficiency factor of the unmanned cab mining dump truck with the data perception energy efficiency threshold. If the data perception energy efficiency factor of the unmanned cab mining dump truck is less than the data perception energy efficiency threshold, the data perception of the unmanned cab mining dump truck is fed back; otherwise, the operating status parameters of the unmanned cab mining dump truck are collected.

[0054] The operating status parameters of the unmanned cab mining dump truck include fuel consumption, mileage, driving speed curve and average tire pressure. In addition to being applied to the above parameters, this example can also be used for other parameters; the above data perception energy efficiency threshold represents the minimum value of the reasonable range of the data perception energy efficiency factor of the unmanned cab mining dump truck, which is extracted from the adjustment database.

[0055] Specifically, the collection of the operating status parameters of the unmanned cab mining dump truck specifically refers to the unmanned cab mining dump truck executing the loading cargo instruction in the automatic operation instruction, and cooperating with the loaded minerals, thereby collecting the status parameters of the unmanned cab mining dump truck and the loaded cargo in the process of coordinated operation, that is, the operating status parameters of the unmanned cab mining dump truck; it should be explained that the operating status in this embodiment refers to the status parameters of the unmanned cab mining dump truck and the loaded cargo in the process of coordinated operation; the above-mentioned automatic execution of task instructions is issued by the mine control center, and the loading cargo instruction includes: a unique task number, the precise location of the loading point, and the loading status of the unmanned cab mining dump truck. The task instructions include location information (including longitude and latitude coordinates, mine or stockpile number, and specific loading area or berth), cargo type and specification (indicating the type of mineral to be loaded, such as coal, ore, sand and gravel, etc., and the estimated loading volume), loading strategy (including loading sequence, loading height and loading speed), collaborative operation information (collaborative equipment number, operation time window and collaborative operation process), task completion standards (such as the loading volume reaches the preset value and the loading time does not exceed the limit, etc.) and subsequent task instructions (speed and route of coordinated operation with the loaded minerals, etc.). The task instructions cover the overall steps from the start of loading to the completion of unloading for the unmanned cab mining dump truck.

[0056] Furthermore, the data perception of the unmanned cab mining dump truck is fed back, and the specific feedback process is:

[0057] The data perception energy efficiency threshold and the data perception energy efficiency factor of the unmanned cab mining dump truck are processed by difference to obtain the data perception energy efficiency difference of the unmanned cab mining dump truck, and matched with the signal gain parameters corresponding to each data perception energy efficiency difference interval stored in the adjustment database, thereby obtaining the matching result, and the data perception of the unmanned cab mining dump truck is fed back. Based on the feedback result, the data perception energy efficiency factor of the unmanned cab mining dump truck is updated, and the unmanned cab mining dump truck receives and executes automatic operation instructions; the signal gain parameters corresponding to the above-mentioned data perception energy efficiency difference intervals, the specific matching rules and values ​​are formulated by the communication administrator according to the characteristics and requirements of the wireless communication system of the unmanned cab mining dump truck; if the data perception energy efficiency factor of the unmanned cab mining dump truck is less than the data perception energy efficiency threshold, it indicates that the data perception energy efficiency of the unmanned cab mining dump truck does not meet the requirements of the data perception energy efficiency threshold, and feedback adjustment is required; if the data perception energy efficiency factor of the unmanned cab mining dump truck is greater than or equal to the data perception energy efficiency threshold, it indicates that the data perception energy efficiency of the unmanned cab mining dump truck meets the requirements of the data perception energy efficiency threshold, and no adjustment is required. Feedback adjustment; In an example embodiment, the specific feedback process is: assuming that the data perception energy efficiency difference of the unmanned cab mining dump truck is 60%, in the data perception energy efficiency difference interval [30%, 60%] stored in the adjustment database, the signal gain parameter corresponding to the data perception energy efficiency difference interval [30%, 60%] stored in the adjustment database includes the gain value plus 6 decibels, then the signal gain parameter of the unmanned cab mining dump truck is the gain value plus 6 decibels, that is, the signal gain value of each data collection device belonging to the unmanned cab mining dump truck is added by 6 decibels, thereby re-evaluating the unmanned cab. It needs to be explained that, given that each data collection device has the characteristics of high precision, it means that these devices can maintain extremely high accuracy and stability when capturing, processing and transmitting data. Therefore, when faced with a situation where the data perception energy efficiency may be insufficient, there is no need to make major adjustments or replace equipment. Instead, it can be optimized by fine-tuning the signal gain parameters. This makes the data perception energy efficiency factor of the unmanned cab mining dump truck greater than or equal to the data perception energy efficiency threshold, thereby completing the feedback on the data perception of the unmanned cab mining dump truck.

[0058] The operating status determination module is used to determine the operating accuracy index of the unmanned cab mining dump truck based on the operating status parameters of the unmanned cab mining dump truck, and compare it with the operating accuracy threshold. If the operating accuracy index of the unmanned cab mining dump truck is less than the operating accuracy threshold, the operating status of the unmanned cab mining dump truck is fed back; otherwise, the automatic unloading process parameters of the unmanned cab mining dump truck are obtained.

[0059] The automatic unloading process parameters of the unmanned cab mining dump truck include the tilt angle and the unloading quality. In addition to being applicable to the above parameters, this example can also be used for other parameters; the above operating accuracy threshold represents the minimum value of the reasonable range of the operating accuracy index of the unmanned cab mining dump truck, which is extracted from the adjustment database.

[0060] In a specific embodiment, the present invention determines the operating accuracy index of the unmanned cab mining dump truck by focusing on analyzing the operating status parameters of the unmanned cab mining dump truck after loading minerals. It can accurately measure the performance of each unmanned vehicle in a specific loading scenario, and can also customize personalized feedback on its operating characteristics, thereby optimizing the operating performance of the unmanned cab mining dump truck in a targeted manner.

[0061] Specifically, the automatic unloading process parameters of the unmanned mining dump truck are obtained by:

[0062] A driving speed reference curve of an unmanned cab mining dump truck during an operation detection cycle is obtained, and the driving speed curve of the unmanned cab mining dump truck during the operation detection cycle is overlapped and compared, and the driving speed curve offset value of the unmanned cab mining dump truck during the operation detection cycle is analyzed; the driving speed reference curve of the unmanned cab mining dump truck during the operation detection cycle is extracted from the decision-making planning target of the automatic operation instruction; the overlap comparison refers to comparing the driving speed reference curve with the corresponding points on the driving speed curve (i.e., points with the same horizontal coordinate), if the points with the same horizontal coordinate have the same vertical coordinate, the point is marked as an overlap point, the total number of overlap points is counted, the total number of coordinate points on the driving speed curve is differenced with the total number of overlap points, and the difference processing result is ratioed with the total number of coordinate points on the driving speed curve, which is the driving speed curve offset value.

[0063] The driving speed curve offset value is matched with the speed adjustment instruction set corresponding to each driving speed curve offset value interval stored in the adjustment database, so that the unmanned cab mining dump truck obtains and executes the speed adjustment instruction set, completes the feedback of the operating status of the unmanned cab mining dump truck, and updates the operating accuracy index of the unmanned cab mining dump truck. The unmanned cab mining dump truck continues to execute the automatic operation instruction, thereby obtaining the automatic unloading process parameters of the unmanned cab mining dump truck; the above-mentioned unmanned cab mining dump truck obtains and executes the speed adjustment instruction set, wherein the specific matching rule is: the speed adjustment instruction set corresponding to each driving speed curve offset value interval is formulated by the vehicle operation specialist according to the operating characteristics and requirements of the unmanned cab mining dump truck; if the operating accuracy index of the unmanned cab mining dump truck is less than the operating accuracy threshold, it indicates that the operating accuracy of the unmanned cab mining dump truck does not meet the requirements of the operating accuracy threshold, and feedback management is required; if the operating accuracy index of the unmanned cab mining dump truck is greater than or equal to the operating accuracy threshold, it indicates that the operating accuracy of the unmanned cab mining dump truck meets the requirements of the operating accuracy threshold. In an example embodiment, it is assumed that the driving speed curve offset value of the unmanned cab mining dump truck in the operation detection cycle is 50%. In the driving speed curve offset value interval [50%, 55%] stored in the adjustment database, the speed adjustment instruction set corresponding to the driving speed curve offset value interval [50%, 55%] includes: gradually reducing the speed to 60% of the current speed within 5 seconds, and maintaining 60% of the current speed for uniform speed transportation. The unmanned cab mining dump truck obtains and executes the speed adjustment instruction set, thereby completing the feedback on the operating state of the unmanned cab mining dump truck, and the operating accuracy index of the unmanned cab mining dump truck can be obtained again. It should be explained that speed is one of the most intuitive manifestations of the operating state of the unmanned cab mining dump truck, and is directly related to the operating accuracy. By adjusting the speed, the motion state of the unmanned cab mining dump truck can be quickly and effectively changed to approach the expected or safe range. Therefore, by adjusting the speed instruction set, the operating accuracy index of the unmanned cab mining dump truck can meet the operating accuracy threshold requirements.

[0064] Furthermore, the specific analysis process of determining the operation accuracy index of the unmanned cab mining dump truck is as follows:

[0065] The fuel consumption and mileage of the unmanned cab mining dump truck during an operation detection cycle are extracted from the operation status parameters of the unmanned cab mining dump truck, and ratio processing is performed to obtain the fuel efficiency ratio of the unmanned cab mining dump truck during the operation detection cycle; the above-mentioned operation detection cycle refers to a time period for detecting the coordinated operation process of the unmanned cab mining dump truck and the loaded goods, and its specific duration is determined by the vehicle operation specialist according to the actual operation conditions of the vehicle; the fuel consumption and mileage of the unmanned cab mining dump truck during the operation detection cycle can be directly extracted from the data recorded by the built-in on-board diagnostic system (OBD) and the fuel management system of the unmanned cab mining dump truck.

[0066] The driving speed curve of the unmanned cab mining dump truck during the operation detection cycle is extracted from the operating status parameters of the unmanned cab mining dump truck, and the driving acceleration curve of the unmanned cab mining dump truck during the operation detection cycle is obtained through data processing, and each acceleration sample position point is located on the driving acceleration curve to obtain the driving acceleration of each acceleration sample position point, thereby performing mean processing, and marking the result of the mean processing as the average driving acceleration of the unmanned cab mining dump truck during the operation detection cycle; the driving speed curve of the unmanned cab mining dump truck during the operation detection cycle is obtained from the built-in on-board diagnostic system (OBD) of the unmanned cab mining dump truck. The data is denoised by a median filter using the matrix laboratory software, and the denoised data is plotted into a driving speed curve using the plot function, such as Figure 3 The driving speed curve shows the driving speed of the unmanned cab mining dump truck during the operation detection cycle. The horizontal axis is the operation detection time point in seconds. The vertical axis is the driving speed in meters per second, where the acceleration sample position point 1 is evenly distributed according to the fixed operation detection time interval 2; the driving acceleration curve of the unmanned cab mining dump truck during the operation detection cycle, the specific data processing process is: use the diff function of the matrix laboratory software to perform differential operations on adjacent data on the driving speed curve to calculate the speed change rate between adjacent speed points, that is, acceleration, and use the plot function to plot the calculated acceleration data into a driving acceleration curve.

[0067] The average tire pressure of the unmanned cab mining dump truck during the operation detection cycle is extracted from the operation status parameters of the unmanned cab mining dump truck. The average tire pressure of the unmanned cab mining dump truck during the operation detection cycle is obtained by using the built-in pressure sensor of the tire to obtain the real-time pressure of each tire, and the average tire pressure is obtained by averaging.

[0068] The data perception energy efficiency factor of the unmanned cab mining dump truck is integrated to obtain a method for determining the operation accuracy index of the unmanned cab mining dump truck. In this embodiment, the operation accuracy index of the unmanned cab mining dump truck is obtained by comprehensively analyzing the fuel efficiency ratio, the average driving acceleration and the average tire pressure of the unmanned cab mining dump truck during the operation detection cycle, and is used to determine the value of the operation accuracy of the unmanned cab mining dump truck. The specific determination method is:

[0069] ;

[0070] It is the data perception energy efficiency factor of the unmanned cab mining dump truck, which refers to the value obtained by comprehensive analysis of the first data perception energy efficiency factor and the second data perception energy efficiency factor of the unmanned cab mining dump truck, and is used to evaluate the data perception and collection capability of the unmanned cab mining dump truck.

[0071] It is the fuel efficiency ratio of the unmanned cab mining dump truck during the operation test cycle, which refers to the ratio between the amount of fuel consumed and the amount of work completed (such as mileage) during the operation test cycle of the unmanned cab mining dump truck.

[0072] It is the average acceleration of the unmanned cab mining dump truck during the operation detection cycle, which refers to the average value of the speed change rate of the unmanned cab mining dump truck during the operation detection cycle.

[0073] It is the average tire pressure of the unmanned cab mining dump truck during the operation and inspection cycle, which refers to the average value of the internal air pressure of the tire of the unmanned cab mining dump truck during the operation and inspection cycle.

[0074] e is a natural constant, To adjust the reference fuel efficiency ratio preset in the database, it represents the reference value for analyzing the fuel efficiency ratio of the unmanned cab mining dump truck during the operation detection cycle.

[0075] In order to adjust the preset driving reference acceleration in the database, it represents the reference value for analyzing the average driving acceleration of the unmanned cab mining dump truck during the operation detection cycle.

[0076] To adjust the preset tire reference pressure in the database, it represents the reference value for analyzing the average tire pressure of the unmanned cab mining dump truck during the operation detection cycle.

[0077] To adjust the weight corresponding to the data-aware energy efficiency factor preset in the database, it represents the proportion of the data-aware energy efficiency factor to the operating accuracy index of the unmanned cab mining dump truck. The weight corresponding to the data-aware energy efficiency factor can be directly obtained from the adjustment database when used. The corresponding relationship can be a preset mapping relationship. For example, the average dust concentration, the average ambient temperature value, the average light intensity, the average intensity of the echo signal, and the average signal transmission time form a mapping set with the weight corresponding to the data-aware energy efficiency factor preset in the adjustment database. The real-time average dust concentration, the average ambient temperature value, the average light intensity, the average intensity of the echo signal, and the average signal transmission time are input into the mapping set to obtain the weight corresponding to the data-aware energy efficiency factor. The mapping relationship can be one-to-one or many-to-one. In this example, its value range is [0, 1].

[0078] in, is the operation accuracy index of the unmanned cab mining dump truck. The data collection accuracy of the unmanned cab mining dump truck is low, that is, when the data perception energy efficiency factor is small, it directly reflects that the vehicle's information collection ability is limited, which may lead to inaccurate and incomplete perception of the external environment, vehicle status and task execution. Under this condition, after the unmanned cab mining dump truck is loaded with minerals, its overall mass increases and its flexibility decreases. Due to the limited data collection accuracy, the power system of the unmanned cab mining dump truck does not work efficiently in the expected manner, which may cause the fuel efficiency ratio to deviate significantly from the expected reference fuel efficiency ratio. The decrease in the fuel efficiency ratio not only increases the operating cost, but also affects the speed stability and driving efficiency of the unmanned cab mining dump truck. Due to the unstable speed, the average driving speed of the unmanned cab mining dump truck is The acceleration will deviate from the preset reference acceleration by a large margin. This fluctuation in acceleration affects the stability of the unmanned cab mining dump truck, increasing irregular operations during driving, such as sharp turns, sudden acceleration or deceleration, etc., which all produce abnormal pressure on the wheels, causing the average tire pressure to deviate from the tire reference pressure by a large margin, seriously affecting the controllability, stability and operation accuracy of the unmanned cab mining dump truck. In summary, the mutual influence between the data perception energy efficiency factor, fuel efficiency ratio, average driving acceleration and average tire pressure of the unmanned cab mining dump truck constitutes an important indicator system for evaluating the vehicle's operating efficiency and accuracy. Therefore, by analyzing the above parameters, timely measures can be taken to adjust and optimize, so as to ensure that the unmanned cab mining dump truck can automatically complete various task instructions safely and efficiently.

[0079] The unloading process analysis module is used to analyze the unloading accuracy of the unmanned cab mining dump truck based on the automatic unloading process parameters of the unmanned cab mining dump truck, thereby providing management feedback on the automatic operation of the unmanned cab mining dump truck.

[0080] In a specific embodiment, the present invention obtains the unloading accuracy of an unmanned cab mining dump truck by analyzing the automatic unloading process parameters of the unmanned cab mining dump truck and the operation accuracy index of the unmanned cab mining dump truck. This not only effectively simplifies the complexity of collecting automatic unloading process parameters, but also realizes the refined control of the unloading process, significantly improving the efficiency and safety of automated operations.

[0081] Specifically, the management feedback of the automatic operation of the unmanned cab mining dump truck is performed as follows:

[0082] The unloading accuracy of the unmanned cab mining dump truck is compared with the unloading accuracy threshold. If the unloading accuracy of the unmanned cab mining dump truck is greater than or equal to the unloading accuracy threshold, a successful log of automatic operation of the unmanned cab mining dump truck is generated and feedback is given, thereby completing the automatic operation of the unmanned cab mining dump truck; the above-mentioned unloading accuracy threshold represents the minimum value of the reasonable range of the unloading accuracy of the unmanned cab mining dump truck, which is extracted from the adjustment database; if the unloading accuracy of the unmanned cab mining dump truck is greater than or equal to the unloading accuracy threshold, it indicates that the unloading accuracy of the unmanned cab mining dump truck meets the expectations of the unloading accuracy threshold, thereby continuing to perform the unloading task according to the task instruction. When the unloading task is completed, a successful log of automatic operation of the unmanned cab mining dump truck is generated and uploaded to the mine area control center, thereby completing the automatic operation of the unmanned cab mining dump truck, wherein the successful log of automatic operation of the unmanned cab mining dump truck includes the overall process of automatic operation of the unmanned cab mining dump truck and the environmental data and operation data perceived by the unmanned cab mining dump truck.

[0083] If the unloading accuracy of the unmanned cab mining dump truck is less than the unloading accuracy threshold, the unloading accuracy threshold is difference processed with the unloading accuracy of the unmanned cab mining dump truck to obtain the unloading accuracy difference of the unmanned cab mining dump truck, and the difference is matched with the unloading adjustment instruction set corresponding to each unloading accuracy difference interval stored in the adjustment database, thereby obtaining the unloading adjustment instruction set of the unmanned cab mining dump truck, and managing the automatic operation of the unmanned cab mining dump truck; if the unloading accuracy of the unmanned cab mining dump truck is less than the unloading accuracy threshold, it indicates that the unmanned cab mining dump truck The unloading accuracy of the unmanned mining dump truck does not meet the expectation of the unloading accuracy threshold, and feedback management is required; the unloading adjustment instruction set of the unmanned mining dump truck mentioned above, the specific matching process is: the unloading adjustment instruction set corresponding to each unloading accuracy difference, the specific matching rules and values ​​are formulated by the vehicle operation specialist according to the operation characteristics and needs of the unmanned mining dump truck. In an example embodiment, assuming that the unloading accuracy difference of the unmanned mining dump truck is 92%, in the unloading accuracy difference interval [80%, 100%] stored in the adjustment database, the unloading accuracy difference interval [80 %, 100%] The corresponding unloading adjustment instruction set includes: reducing the speed of the tilt angle change to 50% of the current speed and maintaining it, so that the unmanned cab mining dump truck executes the unloading adjustment instruction set, and completes the unloading operation according to this unloading adjustment instruction set. After the unloading operation is completed, the unmanned cab mining dump truck automatic operation success log is generated and feedback is given, thereby completing the unmanned cab mining dump truck automatic operation. It should be explained that unmanned cab mining dump trucks can usually maintain extremely high unloading accuracy, but the occasional small reduction in unloading accuracy is often due to the process of mineral loading. Inconsistent quality, etc., which is a phenomenon that is difficult to completely avoid in the actual operating environment. The unloading adjustment instruction set is formulated based on the vehicle operation specialist's in-depth understanding of the operating characteristics of unmanned cab mining dump trucks and long-term experience accumulation. It aims to optimize the unloading process through subtle and precise adjustments. Therefore, it is only necessary to adjust the unloading process of the manned cab mining dump truck through the unloading adjustment instruction set to make the unloading accuracy of the unmanned cab mining dump truck greater than or equal to the unloading accuracy threshold. Therefore, after executing the unloading adjustment instruction set, the unmanned cab mining dump truck can continue to unload until the unloading is completed.

[0084] Specifically, the unloading accuracy of the unmanned mining dump truck, in this embodiment, is obtained by comprehensive analysis of the operation accuracy index of the unmanned mining dump truck, the discrete value of the tilt angle change of the unmanned mining dump truck during the unloading monitoring period, and the discrete value of the unloading speed, and is used to determine the value of the unloading accuracy of the unmanned mining dump truck. The specific analysis method is:

[0085] ;

[0086] It is the operating accuracy index of an unmanned cab mining dump truck, which is obtained by comprehensive analysis of the fuel efficiency ratio, average driving acceleration and average tire pressure of the unmanned cab mining dump truck during the operating detection cycle, and is used to determine the operating accuracy of the unmanned cab mining dump truck.

[0087] It is the discrete value of the tilt angle change of the unmanned cab mining dump truck during the unloading monitoring period, which refers to the discrete degree of the change in the tilt angle of the cargo compartment during the entire monitoring period of the unmanned cab mining dump truck performing unloading operations. It reflects the stability of the tilt angle of the cargo compartment during the unloading process. If the discrete value of the tilt angle change is small, it means that the unmanned cab mining dump truck can maintain a relatively stable tilt angle during the unloading process, which is conducive to the smooth unloading of minerals and reduces the risk of damage to the vehicle and cargo.

[0088] It is the discrete value of the unloading speed of the unmanned mining dump truck during the unloading monitoring period. It refers to the discrete degree of change in the unloading speed during the unloading process of the unmanned mining dump truck. If the discrete value of the unloading speed is small, it means that the unmanned mining dump truck can maintain a relatively stable unloading speed during the unloading process, which is beneficial to improve unloading efficiency and reduce energy consumption.

[0089] e is a natural constant, The weight factor corresponding to the operation accuracy index preset in the adjustment database represents the proportion of the operation accuracy index in the unloading accuracy of the unmanned cab mining dump truck. The weight factor corresponding to the operation accuracy index can be directly obtained from the adjustment database when used, and the corresponding relationship can be a preset mapping relationship. For example, the fuel efficiency ratio, the average driving acceleration and the average tire pressure form a mapping set with the weight factor corresponding to the operation accuracy index preset in the adjustment database. The real-time fuel efficiency ratio, the average driving acceleration and the average tire pressure are input into the mapping set to obtain the weight factor corresponding to the operation accuracy index. The mapping relationship can be one-to-one or many-to-one. In this example, its value range is [0, 1].

[0090] To adjust the influencing factor corresponding to the discrete value unit value of the tilt angle change preset in the database, which represents the numerical value of the influence of the discrete value unit value of the tilt angle change on the unloading accuracy of the unmanned cab mining dump truck, the influencing factor corresponding to the discrete value unit value of the tilt angle change can be directly obtained from the adjustment database when used, and the corresponding relationship can be a preset mapping relationship. For example, the discrete value of the tilt angle change and the influencing factor corresponding to the discrete value unit value of the tilt angle change preset in the adjustment database form a mapping set, and the real-time discrete value of the tilt angle change is input into the mapping set to obtain the influencing factor corresponding to the discrete value unit value of the tilt angle change. The mapping relationship can be one-to-one or many-to-one. In this example, its value range is [0, 1].

[0091] To adjust the influence factor corresponding to the unit value of the discrete value of the unloading speed preset in the database, it represents the influence degree of the unit value of the discrete value of the unloading speed on the unloading accuracy of the unmanned cab mining dump truck. When used, the influence factor corresponding to the unit value of the discrete value of the unloading speed can be directly obtained from the adjustment database. The corresponding relationship can be a preset mapping relationship. For example, the discrete value of the unloading speed and the influence factor corresponding to the unit value of the discrete value of the unloading speed preset in the adjustment database form a mapping set. The real-time discrete value of the unloading speed is input into the mapping set to obtain the influence factor corresponding to the unit value of the discrete value of the unloading speed. The mapping relationship can be one-to-one or many-to-one. In this example, its value range is [0, 1].

[0092] in, The index is the unloading accuracy of the unmanned mining dump truck. When the operating accuracy index of the unmanned mining dump truck is high, it indicates that it can strictly follow the preset automation instructions and achieve highly precise operation execution, thereby ensuring the accuracy of the unloading operation. The high-precision unloading process further reflects that the change of the inclination angle of the unmanned mining dump truck during the unloading monitoring cycle presents a smooth and gradual trend, that is, the change of the inclination angle is maintained within a very small discrete range, avoiding sudden or unpredictable angle changes. This smooth change in the inclination angle directly promotes the uniformity of the unloading process, allowing the mineral to be unloaded in a stable manner. The materials are unloaded from the car at a constant and continuous rate, which effectively reduces the discreteness of the unloading speed. This shows that the unloading process is more controllable and smooth, reduces the potential safety hazards and material scattering problems caused by speed fluctuations, and significantly improves the efficiency, safety and accuracy of automatic operations. It also reflects the mature application of unmanned driving technology in the field of mining dump trucks. Therefore, the analysis of the above parameters provides strong data support for monitoring the accuracy of unmanned cab mining dump trucks during the unloading process, and can accurately diagnose potential performance deviations or operational abnormalities in the unloading process, so as to formulate targeted adjustment plans to ensure the accuracy and stability of the unloading operation.

[0093] In this example embodiment, the change table of the unloading accuracy of the above-mentioned unmanned cab mining dump truck and its corresponding parameters is shown in Table 1:

[0094] Table 1 Unloading accuracy of unmanned mining dump truck and its corresponding parameter changes

[0095]

[0096] In this example embodiment, the weight factor corresponding to the operation accuracy index is set to 0.5, and the influence factor corresponding to the discrete value unit value of the inclination angle change and the influence factor corresponding to the discrete value unit value of the unloading speed are both set to 0.25. It can be seen from Table 1 that when the operation accuracy index is large (90%), the discrete value of the inclination angle change and the discrete value of the unloading speed are small, which are 0.85 radians and 0.3 tons / minute respectively. At this time, the unloading accuracy is large (42.6%). On the contrary, when the operation accuracy index is small (79%), the discrete value of the inclination angle change and the discrete value of the unloading speed are large, which are 1.1 radians and 0.6 tons / minute respectively. At this time, the unloading accuracy is small (38.5%).

[0097] Furthermore, the unloading accuracy of the unmanned mining dump truck is analyzed, and the specific analysis process is as follows:

[0098] The tilt angle of the unmanned cab mining dump truck in each unloading monitoring sub-cycle is extracted from the automatic unloading process parameters of the unmanned cab mining dump truck, the tilt angles in adjacent unloading monitoring sub-cycles are differenced to obtain the tilt angle change differences of the unmanned cab mining dump truck in the unloading monitoring cycle, and the standard deviation is processed to obtain the discrete value of the tilt angle change of the unmanned cab mining dump truck in the unloading monitoring cycle; the above-mentioned unloading monitoring sub-cycles refer to adjacent equal-length time periods for detecting the automatic unloading process of the unmanned cab mining dump truck, and the specific duration is determined according to the actual unloading situation of the unmanned cab mining dump truck, and the above-mentioned unloading monitoring cycle refers to the time period including each unloading monitoring sub-cycle; the tilt angle of the unmanned cab mining dump truck in each unloading monitoring sub-cycle can be measured by a tilt sensor The tilt angle of the carriage of the unmanned cab mining dump truck in each unloading monitoring sub-cycle is marked as the tilt angle of the unmanned cab mining dump truck in each unloading monitoring sub-cycle.

[0099] The unloading mass of the unmanned mining dump truck in each unloading monitoring sub-period is extracted from the automatic unloading process parameters of the unmanned mining dump truck, and the ratio processing is performed with the time length corresponding to the unloading monitoring sub-period to obtain the unloading speed of the unmanned mining dump truck in each unloading monitoring sub-period, and the standard deviation processing is performed to obtain the discrete value of the unloading speed of the unmanned mining dump truck in the unloading monitoring period; the unloading mass of the unmanned mining dump truck in each unloading monitoring sub-period is monitored by the built-in on-board weighing system of the unmanned mining dump truck, and the mineral mass at the start time point of each unloading monitoring sub-period is subtracted from the mineral mass at the corresponding end time point, which is the unloading mass in each unloading monitoring sub-period.

[0100] At the same time, the operation accuracy index of the unmanned cab mining dump truck is integrated to obtain the analysis method of the unloading accuracy of the unmanned cab mining dump truck.

[0101] In a specific embodiment, the present invention provides an automatic operation system and method for an unmanned cab mining dump truck. By collecting the performance parameters of each data collection device belonging to the unmanned cab mining dump truck and the environmental parameters of the unmanned cab mining dump truck, a reliable data basis is provided for the data perception feedback of the unmanned cab mining dump truck, the operating status parameters of the unmanned cab mining dump truck are collected, and the operating accuracy index of the unmanned cab mining dump truck is determined, providing an accurate data basis for the feedback of the operating status of the unmanned cab mining dump truck. At the same time, the automatic unloading process parameters of the unmanned cab mining dump truck are obtained, and the unloading accuracy of the unmanned cab mining dump truck is analyzed, thereby managing and feedbacking the automatic operation of the unmanned cab mining dump truck, ensuring the comprehensive and accurate capture of various key parameters of the unmanned cab mining dump truck during the operation process. The high-quality data constitutes a solid foundation for intelligent decision-making and analysis, making the judgment of the automated operation status of the unmanned cab mining dump truck, problem identification and optimization strategy formulation more accurate and effective.

[0102] Reference Figure 2As shown, the second aspect of the present invention provides a method for an automatic operation system of an unmanned cab mining dump truck, comprising: S1. collecting performance parameters of each data collection device belonging to the unmanned cab mining dump truck and environmental parameters of the unmanned cab mining dump truck, and comprehensively evaluating the data perception energy efficiency factor of the unmanned cab mining dump truck; S2. comparing the data perception energy efficiency factor of the unmanned cab mining dump truck with the data perception energy efficiency threshold. If the data perception energy efficiency factor of the unmanned cab mining dump truck is less than the data perception energy efficiency threshold, then feedback is given to the data perception of the unmanned cab mining dump truck; otherwise, the data perception of the unmanned cab mining dump truck is collected. Based on the operating status parameters of the unmanned cab mining dump truck, the operating accuracy index of the unmanned cab mining dump truck is determined, and compared with the operating accuracy threshold. If the operating accuracy index of the unmanned cab mining dump truck is less than the operating accuracy threshold, the operating status of the unmanned cab mining dump truck is fed back. Otherwise, the automatic unloading process parameters of the unmanned cab mining dump truck are obtained. S4. Based on the automatic unloading process parameters of the unmanned cab mining dump truck, the unloading accuracy of the unmanned cab mining dump truck is analyzed, and the automatic operation of the unmanned cab mining dump truck is managed and fed back.

[0103] The above contents are merely examples and explanations of the structure of the present invention. The technicians in this technical field may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the structure of the invention or exceed the scope defined by the present invention, they should all fall within the protection scope of the present invention.

Claims

1. An automatic operation system for an unmanned cab mining dump truck, characterized in that: include: The data perception evaluation module is used to collect the performance parameters of each data collection device of the unmanned cab mining dump truck and the environmental parameters of the unmanned cab mining dump truck, and comprehensively evaluate the data perception energy efficiency factor of the unmanned cab mining dump truck. The data perception energy efficiency factor of the unmanned cab mining dump truck is obtained by comprehensive analysis of the first data perception energy efficiency factor and the second data perception energy efficiency factor of the unmanned cab mining dump truck, and is used to evaluate the data perception collection capability of the unmanned cab mining dump truck. The specific evaluation method is: ； in, ； Data perception energy efficiency factor for unmanned mining dump trucks. The first data perception energy efficiency factor for unmanned mining dump trucks. The second data perception energy efficiency factor of the unmanned cab mining dump truck, is the average dust concentration in the environment area of ​​the unmanned cab mining dump truck during the environmental detection period. It is the average temperature value of the environment area of ​​the unmanned cab mining dump truck during the environmental detection period. is the average light intensity of the environment area of ​​the unmanned mining dump truck during the environmental detection period. is the average strength of the echo signal of the cth data collection device of the unmanned mining dump truck during the environmental detection period, is the average signal transmission duration of the cth data collection device of the unmanned mining dump truck during the environmental detection cycle, To adjust the reference ambient temperature value preset in the database, To adjust the reference light intensity preset in the database, To adjust the echo signal definition strength preset in the database, To adjust the signal transmission time defined in the database, To adjust the impact factor corresponding to the average dust concentration unit value preset in the database, To adjust the weight factor corresponding to the first data-aware energy efficiency factor preset in the database, To adjust the weight factor corresponding to the second data perception energy efficiency factor preset in the database, e is a natural constant, c is the number of each data collection device, , d is the total number of data collection devices; The data perception feedback module is used to compare the data perception energy efficiency factor of the unmanned cab mining dump truck with the data perception energy efficiency threshold. If the data perception energy efficiency factor of the unmanned cab mining dump truck is less than the data perception energy efficiency threshold, the data perception of the unmanned cab mining dump truck is fed back. Otherwise, the operating status parameters of the unmanned cab mining dump truck are collected. The operation state determination module is used to determine the operation accuracy index of the unmanned cab mining dump truck based on the operation state parameters of the unmanned cab mining dump truck, and compare it with the operation accuracy threshold. If the operation accuracy index of the unmanned cab mining dump truck is less than the operation accuracy threshold, the operation state of the unmanned cab mining dump truck is fed back. Otherwise, the automatic unloading process parameters of the unmanned cab mining dump truck are obtained. The operation accuracy of the unmanned cab mining dump truck is obtained by comprehensive analysis of the fuel efficiency ratio, average driving acceleration and average tire pressure of the unmanned cab mining dump truck during the operation detection cycle. The value used to determine the operation accuracy of the unmanned cab mining dump truck is as follows: ； is the operating accuracy index of the unmanned cab mining dump truck, Data perception energy efficiency factor for unmanned mining dump trucks. The fuel efficiency ratio of the unmanned cab mining dump truck during the operation test cycle, is the average acceleration of the unmanned mining dump truck during the operation test cycle, is the average tire pressure of the unmanned mining dump truck during the operation test cycle, e is a natural constant, To adjust the reference fuel efficiency ratio preset in the database, To adjust the driving reference acceleration preset in the database, To adjust the tire reference pressure preset in the database, To adjust the weight corresponding to the data-aware energy efficiency factor preset in the database; The unloading process analysis module is used to analyze the unloading accuracy of the unmanned cab mining dump truck based on the automatic unloading process parameters of the unmanned cab mining dump truck, thereby providing management feedback for the automatic operation of the unmanned cab mining dump truck. The unloading accuracy of the unmanned cab mining dump truck is obtained by comprehensive analysis of the operation accuracy index of the unmanned cab mining dump truck, the discrete value of the tilt angle change of the unmanned cab mining dump truck during the unloading monitoring period, and the discrete value of the unloading speed. The value used to determine the unloading accuracy of the unmanned cab mining dump truck is obtained by comprehensive analysis of the operation accuracy index of the unmanned cab mining dump truck, the discrete value of the tilt angle change of the unmanned cab mining dump truck during the unloading monitoring period, and the discrete value of the unloading speed. The specific determination method is: ； To improve the unloading accuracy of unmanned mining dump trucks. is the operating accuracy index of the unmanned cab mining dump truck, is the discrete value of the tilt angle change of the unmanned mining dump truck during the unloading monitoring period. is the discrete value of the unloading speed of the unmanned mining dump truck during the unloading monitoring period, e is a natural constant, To adjust the weight factor corresponding to the running accuracy index preset in the database, To adjust the influence factor corresponding to the discrete value unit value of the tilt angle change preset in the database, To adjust the influence factor corresponding to the unit value of the discrete value of the unloading speed preset in the database.

2. According to claim 1, the automatic operation system of an unmanned cab mining dump truck is characterized in that: The specific evaluation process of evaluating the data perception energy efficiency factor of the unmanned cab mining dump truck is as follows: The average dust concentration of the environmental area to which the unmanned cab mining dump truck belongs during the environmental detection period, the average ambient temperature value of the environmental area to which the unmanned cab mining dump truck belongs during the environmental detection period, and the average light intensity of the environmental area to which the unmanned cab mining dump truck belongs during the environmental detection period are extracted from the environmental parameters of the unmanned cab mining dump truck, and the reference ambient temperature value and the reference light intensity are extracted from the adjustment database, thereby comprehensively evaluating the first data perception energy efficiency factor of the unmanned cab mining dump truck; Extract the average echo signal strength of each data collection device of the unmanned cab mining dump truck during the environmental detection period and the average signal transmission duration of each data collection device of the unmanned cab mining dump truck during the environmental detection period from the performance parameters of each data collection device of the unmanned cab mining dump truck, and match the echo signal boundary strength and signal transmission boundary duration corresponding to the first data perception energy efficiency factor of the unmanned cab mining dump truck from the adjustment database, thereby comprehensively evaluating the second data perception energy efficiency factor of the unmanned cab mining dump truck; Based on the first data perception energy efficiency factor of the unmanned cab mining dump truck and the second data perception energy efficiency factor of the unmanned cab mining dump truck, an evaluation method for the data perception energy efficiency factor of the unmanned cab mining dump truck is obtained.

3. According to claim 1, the automatic operation system of an unmanned cab mining dump truck is characterized in that: The data perception of the unmanned cab mining dump truck is fed back, and the specific feedback process is as follows: The data perception energy efficiency threshold is processed by difference with the data perception energy efficiency factor of the unmanned cab mining dump truck to obtain the data perception energy efficiency difference of the unmanned cab mining dump truck, and the difference is matched with the signal gain parameters corresponding to each data perception energy efficiency difference interval stored in the adjustment database to obtain the matching result, and the data perception of the unmanned cab mining dump truck is fed back. Based on the feedback result, the data perception energy efficiency factor of the unmanned cab mining dump truck is updated, and the unmanned cab mining dump truck receives and executes automatic operation instructions.

4. According to claim 1, the automatic operation system of an unmanned cab mining dump truck is characterized in that: The collection of operating status parameters of the unmanned cab mining dump truck specifically refers to the unmanned cab mining dump truck executing the cargo loading instructions in the automatic operation instructions and cooperating with the loaded minerals, thereby collecting the status parameters of the unmanned cab mining dump truck in the process of coordinated operation with the loaded cargo, which are the operating status parameters of the unmanned cab mining dump truck.

5. According to claim 1, the automatic operation system of an unmanned cab mining dump truck is characterized in that: The specific analysis process of determining the operation accuracy index of the unmanned cab mining dump truck is as follows: The fuel consumption and mileage of the unmanned cab mining dump truck during the operation detection cycle are extracted from the operation status parameters of the unmanned cab mining dump truck, and the ratio processing is performed to obtain the fuel efficiency ratio of the unmanned cab mining dump truck during the operation detection cycle; A driving speed curve of the unmanned cab mining dump truck during an operation detection cycle is extracted from the operation state parameters of the unmanned cab mining dump truck, and a driving acceleration curve of the unmanned cab mining dump truck during the operation detection cycle is obtained through data processing, each acceleration sample position point is located on the driving acceleration curve, and the driving acceleration of each acceleration sample position point is obtained, and average processing is performed, and the result of the average processing is marked as the average driving acceleration of the unmanned cab mining dump truck during the operation detection cycle; The average tire pressure of the unmanned cab mining dump truck during the operation detection cycle was extracted from the operating status parameters of the unmanned cab mining dump truck. The data perception energy efficiency factor of the unmanned cab mining dump truck was combined to obtain the determination method of the operation accuracy index of the unmanned cab mining dump truck.

6. The automatic operation system of an unmanned cab mining dump truck according to claim 1, characterized in that: The automatic unloading process parameters of the unmanned cab mining dump truck are obtained by: A reference speed curve of the unmanned cab mining dump truck during the operation detection cycle is obtained, and the reference speed curve is overlapped and compared with the driving speed curve of the unmanned cab mining dump truck during the operation detection cycle. The driving speed curve offset value of the unmanned cab mining dump truck during the operation detection cycle is analyzed, and the driving speed curve offset value is matched with the adjustment speed instruction set corresponding to each driving speed curve offset value interval stored in the adjustment database. The unmanned cab mining dump truck obtains and executes the adjustment speed instruction set, completes the feedback of the operation status of the unmanned cab mining dump truck, and updates the operation accuracy index of the unmanned cab mining dump truck. The unmanned cab mining dump truck continues to execute the automatic operation instruction, thereby obtaining the automatic unloading process parameters of the unmanned cab mining dump truck.

7. The automatic operation system of an unmanned cab mining dump truck according to claim 1, characterized in that: The analysis of the unloading accuracy of the unmanned cab mining dump truck is as follows: The tilt angle of the unmanned mining dump truck in each unloading monitoring sub-period is extracted from the automatic unloading process parameters of the unmanned mining dump truck, the tilt angles in adjacent unloading monitoring sub-periods are subjected to difference processing to obtain the change difference of each tilt angle of the unmanned mining dump truck in the unloading monitoring period, and the standard deviation processing is performed to obtain the discrete value of the tilt angle change of the unmanned mining dump truck in the unloading monitoring period; The unloading mass of the unmanned mining dump truck in each unloading monitoring sub-cycle is extracted from the automatic unloading process parameters of the unmanned mining dump truck, and the ratio processing is performed with the duration corresponding to the unloading monitoring sub-cycle to obtain the unloading speed of the unmanned mining dump truck in each unloading monitoring sub-cycle, and the standard deviation processing is performed to obtain the discrete value of the unloading speed of the unmanned mining dump truck in the unloading monitoring cycle; At the same time, the operation accuracy index of the unmanned cab mining dump truck is integrated to obtain the analysis method of the unloading accuracy of the unmanned cab mining dump truck.

8. The automatic operation system of an unmanned cab mining dump truck according to claim 1, characterized in that: The management feedback of the automatic operation of the unmanned cab mining dump truck is specifically conducted as follows: The unloading accuracy of the unmanned mining dump truck is compared with the unloading accuracy threshold. If the unloading accuracy of the unmanned mining dump truck is greater than or equal to the unloading accuracy threshold, a successful automatic operation log of the unmanned mining dump truck is generated and feedback is given, thereby completing the automatic operation of the unmanned mining dump truck; If the unloading accuracy of the unmanned mining dump truck is less than the unloading accuracy threshold, the unloading accuracy threshold is differenced with the unloading accuracy of the unmanned mining dump truck to obtain the unloading accuracy difference of the unmanned mining dump truck, and the difference is matched with the unloading adjustment instruction set corresponding to each unloading accuracy difference interval stored in the adjustment database, thereby obtaining the unloading adjustment instruction set of the unmanned mining dump truck and managing the automatic operation of the unmanned mining dump truck.

9. A method for applying the automatic operation system of an unmanned cab mining dump truck according to any one of claims 1 to 8, characterized in that: include: S1. Collect the performance parameters of each data collection device of the unmanned cab mining dump truck and the environmental parameters of the unmanned cab mining dump truck, and comprehensively evaluate the data perception energy efficiency factor of the unmanned cab mining dump truck; S2. Compare the data perception energy efficiency factor of the unmanned cab mining dump truck with the data perception energy efficiency threshold. If the data perception energy efficiency factor of the unmanned cab mining dump truck is less than the data perception energy efficiency threshold, then feedback is given to the data perception of the unmanned cab mining dump truck. Otherwise, the operating status parameters of the unmanned cab mining dump truck are collected. S3. Based on the operating status parameters of the unmanned cab mining dump truck, the operating accuracy index of the unmanned cab mining dump truck is determined and compared with the operating accuracy threshold. If the operating accuracy index of the unmanned cab mining dump truck is less than the operating accuracy threshold, the operating status of the unmanned cab mining dump truck is fed back. Otherwise, the automatic unloading process parameters of the unmanned cab mining dump truck are obtained; S4. Based on the automatic unloading process parameters of the unmanned cab mining dump truck, the unloading accuracy of the unmanned cab mining dump truck is analyzed, and management feedback is provided for the automatic operation of the unmanned cab mining dump truck.

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