Fuel metering method and system for coal yard

Abstract

The invention relates to the technical field of coal yard fuel metering, and relates to a coal yard fuel metering method and system, the method comprises the following steps: obtaining real-time information collected by a first unmanned aerial vehicle, the real-time information comprising obstacle position, dust concentration and wind speed information; establishing a three-dimensional risk map according to the real-time information collected by the unmanned aerial vehicle; dividing the three-dimensional risk map to obtain a plurality of sub three-dimensional risk maps; matching the to-be-detected sub-three-dimensional risk map of the second unmanned aerial vehicle by using an auction algorithm to obtain a matching result; determining the type of the corresponding fuel stack according to a matching result to obtain type information; the scanning track of the second unmanned aerial vehicle for scanning the fuel stack is planned according to the type information, the weight of the fuel stack is calculated according to the point cloud data acquired through scanning, the fuel does not need to be transported to a wagon balance by a vehicle for metering, and rapid, accurate and non-contact metering of the fuel stack of the large coal yard is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of fuel metering in coal yards, and in particular, to a fuel metering method and system for coal yards. Background Art

[0002] In the current technical system, for the metering of fuel piles in coal yards, the relatively common method is the static weighing method. The operating principle of this method is relatively simple. It mainly uses a weighbridge to perform two weighing operations on transport vehicles. The first weighing is carried out when the vehicle is in an empty state to obtain the weight data of the empty vehicle. The second weighing is carried out again after the vehicle is loaded with fuel to the full-load state. Finally, by calculating the difference between the two weighing data, the weight value of the transported fuel can be obtained. However, this seemingly simple metering method exposes significant drawbacks of extremely low efficiency when actually applied to large coal yards. A large coal yard is a place for storing a large number of fuel piles, and there are many daily fuel transport trips. If the static weighing method is used, each transport vehicle needs to go through a series of cumbersome processes such as empty vehicle weighing, loading, and full-load weighing, which will undoubtedly consume a lot of time. Moreover, during the entire weighing process, it is necessary to arrange special personnel to be responsible for operating the weighbridge, recording data, guiding vehicles, etc., which will undoubtedly waste a great deal of manpower. At the same time, the purchase, maintenance of the weighbridge equipment, and the occupation of the site all require a large amount of capital investment, and the material cost cannot be underestimated. Summary of the Invention

[0003] The purpose of the present invention is to provide a fuel metering method and system for coal yards to improve the above problems.

[0004] To achieve the above purpose, the embodiments of the present application provide the following technical solutions:

[0005] On the one hand, the embodiments of the present application provide a fuel metering method for a coal yard, and the method includes:

[0006] Obtain real-time information collected by a first unmanned aerial vehicle (UAV), where the real-time information includes the position of obstacles, dust concentration, and wind speed information;

[0007] Establish a three-dimensional risk map according to the real-time information collected by the UAV, where the three-dimensional risk map includes all fuel piles that need to be weighed in the coal yard;

[0008] Divide the three-dimensional risk map to obtain a number of sub-three-dimensional risk maps;

[0009] Use an auction algorithm to match the sub-three-dimensional risk maps to be detected by a second UAV to obtain a matching result;

[0010] Determine the type of the corresponding fuel pile according to the matching result to obtain type information;

[0011] Plan the scanning trajectory of the second UAV to scan the fuel pile according to the type information, and calculate the weight of the fuel pile based on the point cloud data collected by the scanning.

[0012] In a second aspect, an embodiment of the present application provides a fuel metering system for a coal yard, and the system includes:

[0013] An acquisition module, configured to acquire real-time information collected by a first UAV, where the real-time information includes the position of an obstacle, the dust concentration, and the wind speed information;

[0014] A first processing module, configured to establish a three-dimensional risk map according to the real-time information collected by the UAV, where the three-dimensional risk map includes all fuel piles in the coal yard that need to be weighed;

[0015] A second processing module, configured to divide the three-dimensional risk map to obtain a plurality of sub-three-dimensional risk maps;

[0016] A third processing module, configured to use an auction algorithm to match the sub-three-dimensional risk maps to be detected by the second UAV to obtain a matching result;

[0017] A fourth processing module, configured to determine the type of the corresponding fuel pile according to the matching result to obtain type information;

[0018] A fifth processing module, configured to plan the scanning trajectory of the second UAV to scan the fuel pile according to the type information, and calculate the weight of the fuel pile based on the point cloud data collected by the scanning.

[0019] In a third aspect, an embodiment of the present application provides a fuel metering device for a coal yard, and the device includes a memory and a processor. The memory is used to store a computer program; the processor is used to implement the steps of the above-mentioned fuel metering method for the coal yard when executing the computer program.

[0020] In a fourth aspect, an embodiment of the present application provides a readable storage medium, on which a computer program is stored, and the computer program implements the steps of the above-mentioned fuel metering method for the coal yard when being executed by a processor.

[0021] The beneficial effects of the present invention are as follows:

[0022] After constructing a three-dimensional risk map through the real-time information collected by the first UAV, the present invention divides the three-dimensional risk map to obtain a plurality of sub-three-dimensional risk maps, and uses an auction algorithm to match the second UAV with the sub-three-dimensional risk maps, assigns corresponding areas for each second UAV to detect, enables multiple UAVs to work collaboratively, improves the metering efficiency in a large coal yard, then determines the scanning trajectory according to the type of the fuel pile, avoids the existence of scanning blind areas, improves the quality of the collected point cloud data, and thus improves the fuel metering accuracy.

[0023] Other features and advantages of the present invention will be described in the following specification, and in part will become apparent from the specification, or can be understood by implementing the embodiments of the present invention. The objectives and other advantages of the present invention can be achieved and obtained by the structures specifically pointed out in the written specification and the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the accompanying drawings required for use in the embodiments. It should be understood that the following drawings only show some embodiments of the present invention, and therefore should not be regarded as a limitation of the scope. For those of ordinary skill in the art, without creative efforts, other related drawings can also be obtained based on these drawings.

[0025] Figure 1 It is a schematic flowchart of the fuel metering method for the coal yard described in the embodiments of the present invention.

[0026] Figure 2 It is a schematic structural diagram of the fuel metering system for the coal yard described in the embodiments of the present invention.

[0027] Figure 3 It is a schematic structural diagram of the fuel metering equipment for the coal yard described in the embodiments of the present invention.

[0028] Reference numerals in the figure: 800, fuel metering equipment for the coal yard; 801, processor; 802, memory; 803, multimedia component; 804, I / O interface; 805, communication component; 901, acquisition module; 902, first processing module; 903, second processing module; 904, third processing module; 905, fourth processing module; 906, fifth processing module. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0029] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. Usually, the components of the embodiments of the present invention described and shown in the accompanying drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed present invention, but merely represents the selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of the present invention.

[0030] It should be noted that similar reference numerals and letters denote similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. At the same time, in the description of the present invention, terms such as "first" and "second" are only used for differential description and cannot be construed as indicating or implying relative importance.

[0031] Embodiment 1:

[0032] This embodiment provides a fuel metering method for a coal yard. It can be understood that in this embodiment, a scenario can be set up, for example: in a large coal yard, a scenario of metering all fuel piles in the coal yard.

[0033] Refer to Figure 1 , the figure shows that this method includes step S1, step S2, step S3, step S4, step S5 and step S6, which specifically include:

[0034] Step S1: Obtain real-time information collected by a first unmanned aerial vehicle (UAV), where the real-time information includes the positions of obstacles, dust concentration, and wind speed information;

[0035] In this step, sensors are deployed at key positions in the coal yard. For example, lidar is used to obtain the positions of obstacles, a dust concentration sensor monitors the dust concentration, and a wind speed sensor measures the wind speed.

[0036] Step S2: Establish a three-dimensional risk map according to the real-time information collected by the UAV, where the three-dimensional risk map includes all fuel piles that need to be weighed in the coal yard;

[0037] In this step, using lidar scanning technology, a basic three-dimensional model of the coal yard is constructed to present the overall layout of the coal yard, the contours of coal piles, and the terrain undulations. Then, the collected real-time information is quantitatively evaluated. Specifically: the dust concentration is divided into different levels. It is set that 0 - 50 mg / m 3 is a low risk, 51 - 100 mg / m 3 is a medium risk, and 100 mg / m 3 and above is a high risk, and numerical values 1, 2, and 3 are respectively assigned; the wind speed is also classified similarly. 0 - 3 m / s is a low risk, 3.1 - 6 m / s is a medium risk, and above 6.1 m / s is a high risk, corresponding to numerical values 1, 2, and 3. The risk factors are converted into operable quantitative indicators, and the quantitative indicator of the risk factor is mapped to the corresponding coordinate points of the three-dimensional basic model, and the risk level is visually displayed using colors. For example, the low-risk area is shown in green, the medium risk is shown in yellow, and the high risk is shown in red.

[0038] Step S3: Divide the three-dimensional risk map to obtain several sub-three-dimensional risk maps;

[0039] Step S4: Use the auction algorithm to match the sub-three-dimensional risk map to be detected by the second unmanned aerial vehicle (UAV) to obtain a matching result;

[0040] In this step, since the coverage area of a large coal yard is relatively large, using a single UAV for operation requires multiple recharges to complete the metering of the entire large coal yard, which instead reduces the operation efficiency. Therefore, the three-dimensional risk map is divided, and multiple UAVs are used to work collaboratively. A corresponding sub-three-dimensional risk map is assigned to each UAV for detection, effectively improving the metering efficiency of the fuel piles in the large coal yard.

[0041] In step S4, there are also steps S41, S42, and S43, which specifically include:

[0042] Step S41: Obtain the attribute information of each second UAV, where the attribute information includes position information, power information, and sensor type;

[0043] Step S42: Determine the bid price of each second UAV according to the attribute information of each second UAV to obtain bid price information;

[0044] In this step, the specific calculation process of the bid price information is as follows:

[0045]

[0046] In the above formula, B represents the bid price, P represents the remaining power of the second UAV, D represents the distance to the target, M represents the sensor matching degree, and the one with the highest price wins the task to ensure load balancing and optimal resource matching.

[0047] Step S43: Match the sub-three-dimensional risk map to be detected by each UAV according to the bid price information.

[0048] After step S43, there are also steps S44, S45, S46, S47, and S48, which specifically include:

[0049] Step S44: Obtain the type information of the obstacles;

[0050] In this step, the type information of the obstacles includes vehicles and fixed equipment in the coal yard.

[0051] Step S45: Determine the repulsion coefficient of the obstacles according to the type information of the obstacles;

[0052] Step S46: Generate a repulsion field around the obstacles according to the repulsion coefficient of the obstacles;

[0053] In this step, the calculation formula of the repulsion force is:

[0054]

[0055] In the above formula, F1 represents the repulsive force, d and d0 respectively represent the distance between the second unmanned aerial vehicle (UAV) and the repulsive force field and the influence radius of the repulsive force, and K1 represents the repulsive force coefficient, which will be adjusted according to the type of obstacle (the repulsive force coefficient of a vehicle is greater than that of a fixed device).

[0056] Step S47: Generate an attractive force field at the corresponding fuel pile in the sub-three-dimensional risk map;

[0057] In this step, the calculation formula for the attractive force is:

[0058] F2 = K2·d goal

[0059] In the above formula, F2 represents the attractive force, K2 represents the attractive force coefficient, and d goal represents the distance to the target point, and in the present invention, the target point is the fuel pile.

[0060] Step S48: Plan the flight path of the second UAV according to the repulsive force field and the attractive force field.

[0061] In this embodiment, since multiple second UAVs work cooperatively and there are a large number of vehicle flows in and out of the large coal yard, it is necessary to enable the second UAV to effectively avoid obstacles. The present invention can effectively model and express the environmental information around the UAV by constructing a dynamic potential energy field. By considering factors such as the position, shape, and size of the obstacles, a virtual force field environment is constructed for the UAV. The UAV can make real-time obstacle avoidance decisions according to the potential field situation at the current position. For example, in the coal yard, if a working vehicle suddenly appears, the vehicle will generate a new repulsive force area in the potential field, and the UAV can sense this change and timely adjust the flight path to avoid the vehicle, so as to achieve local real-time obstacle avoidance and avoid interfering with the metering operation.

[0062] Step S5: Determine the type of the corresponding fuel pile according to the matching result to obtain type information;

[0063] In this step, after the matching of the second UAV is completed, the second UAV can determine the type of the fuel pile by initially scanning the height of the fuel pile.

[0064] Step S6: Plan the scanning trajectory of the second UAV to scan the fuel pile according to the type information, and calculate the weight of the fuel pile according to the point cloud data collected by the scanning.

[0065] In step S6, it further includes steps S61, S62, and S63, which specifically include:

[0066] Step S61: When the type information is for a tall fuel pile, obtain the surface slope information of the fuel pile, where the tall fuel pile is a fuel pile with a height greater than the height threshold.

[0067] Step S62: Determine whether the surface slope information of the fuel pile is greater than the angle threshold to obtain a judgment result.

[0068] In this step, the angle threshold is set to 45°.

[0069] Step S63: Plan the scanning trajectory of the fuel pile according to the judgment result.

[0070] In this step, for a tall fuel pile, starting from the vertex of the coal pile, the scanning trajectory is expanded according to the Archimedes spiral equation to ensure no blind area at the top of the coal pile. When the surface slope of the coal pile is greater than 45°, the number of scanning circles will be increased, that is, the pitch will be reduced, to prevent data loss on the side of the coal pile, ensure the integrity of the scanning data, thereby improving the quality of the point cloud data and ensuring the metering accuracy.

[0071] It can be understood that when the surface slope information of the fuel pile is less than the angle threshold, the parallel flight path mode is adopted, and the surface of the coal pile is covered in an "S" - shaped path. The flight path spacing is calculated according to the field - of - view angle of the lidar, specifically:

[0072]

[0073] In the above formula, X represents the flight path spacing, H represents the flight altitude of the second unmanned aerial vehicle, and θ represents the field - of - view angle of the lidar. By reasonably calculating the flight path spacing, effective coverage scanning of the low - lying coal pile can be achieved.

[0074] In this embodiment, adopting different scanning strategies to adapt to different forms of coal piles can effectively improve the efficiency, safety and scanning quality of the unmanned aerial vehicle in the coal yard operation, thereby further improving the metering accuracy.

[0075] In step S6, there are also steps S64, S65, S66, S67 and S68, which specifically include:

[0076] Step S64: Obtain the multi - spectral image of the fuel pile.

[0077] Step S65: Extract features from the multi - spectral image of the fuel pile to obtain multi - spectral texture features.

[0078] In this step, the method of extracting features from the multi - spectral image is not limited.

[0079] Step S66: Segment the fuel pile according to the multi - spectral texture features and the point cloud data to obtain the segmented fuel pile.

[0080] It is understandable that the point cloud data needs to be preprocessed before being utilized, which includes: first, performing noise reduction processing to remove outliers through statistical filtering (removing the point cloud with a distance from the mean greater than 3σ); then, performing registration to splice the point clouds of multiple drones into a complete coal yard model based on the NDT algorithm. After preprocessing the point cloud data, the processed point cloud data and multi-spectral texture features are sent to the segmentation model. An attention mechanism layer is added to the network structure to strengthen the extraction of coal pile boundary features and output the belonging fuel pile ID for each point. The present invention combines spectral texture features to segment the adhered fuel piles to improve the accuracy of establishing the three-dimensional density field. It should be noted that the PointNet++ model is selected as the segmentation model.

[0081] Step S67: Establish a three-dimensional density field based on the segmented fuel piles;

[0082] In step S67, it further includes step S671, step S672, step S673, step S674, and step S675, which specifically include:

[0083] Step S671: Obtain the multi-spectral image corresponding to the segmented fuel piles;

[0084] Step S672: Send the multi-spectral image corresponding to the segmented fuel piles to the partial least squares regression model to obtain the ash content information;

[0085] In this step, the partial least squares regression model can be used to establish the mapping relationship between the multi-spectral image and the actual ash content, so that the ash content information can be quickly obtained according to the mapping relationship.

[0086] Step S673: Obtain the moisture content information corresponding to the segmented fuel piles;

[0087] Step S674: Calculate based on the moisture content information, the ash content information, and the reference density of the fuel pile to obtain the density information;

[0088] In this step, the calculation process of the density information is as follows:

[0089] ρ = ρ0×(1 - αM)×(1 - βN)

[0090] In the above formula, ρ is the density information, ρ0 is the reference density of the fuel pile, α and β are empirical correction coefficients, M represents the moisture content information, N represents the ash content information, and ρ0 can be obtained from the historical database.

[0091] Step S675: Establish a three-dimensional density field based on the density information.

[0092] In this step, a three-dimensional density field is generated based on the Kriging algorithm. Generating a three-dimensional density field using the Kriging algorithm is a well-known technical solution to those skilled in the art, so it will not be elaborated here.

[0093] Step S68: Calculate the weight of the fuel stack according to the three-dimensional density field.

[0094] It can be understood that before calculating the weight of the fuel stack using the three-dimensional density field, actual sampling is used to calibrate and correct the three-dimensional density field to improve the accuracy of weight calculation. The specific process is as follows: Calculate the confidence level of each point in the three-dimensional density field to obtain confidence level information; Map the confidence level information to the three-dimensional space of the fuel stack to generate a density field confidence map; Use the Monte Carlo sampling algorithm to screen the sampling points in the density field confidence map to obtain a screening result, where the screening result includes at least three sampling points, and the sampling positions of the sampling points include the surface layer, the middle layer, and the core layer; Use a sonic probe to sample the sampling points included in the screening result to obtain a sample density; Correct the three-dimensional density field according to the sample density to obtain a corrected three-dimensional density field. The sonic probe is carried on a robot, and the robot can plan a collision-free path according to the generated three-dimensional risk map and the obstacle information obtained in real time using the A* algorithm to ensure that the robot can safely and efficiently reach the target coal pile, realizing the automation of the entire flow field. It should be noted that the method of Kalman filter fusion is used to compare the measured density with the density in the previous three-dimensional density field, thereby updating the density field model parameters. The specific process is as follows:

[0095] ρ new = ρ old + K(ρ m - ρ)

[0096] In the above formula, ρ new and ρ old respectively represent the density of this iteration and the density of the previous iteration. K represents the Kalman gain, which can be dynamically adjusted according to historical errors to continuously optimize the density field model. ρ m represents the measured sample density, and ρ represents the density in the three-dimensional density field. Through continuous iteration of the model, the accuracy of the generated density is improved, thereby generating a corrected three-dimensional density field to make the measurement of the fuel stack more accurate.

[0097] After step S6, there are also steps S7, S8, S9, and S10, which specifically include:

[0098] Step S7: Obtain environmental parameters and fuel stack attribute parameters;

[0099] Step S8: Obtain a preset weathering loss model, and the preset weathering loss model is used to predict the weight loss at a future time;

[0100] In this step, the preset weathering loss model includes a physical model and a machine learning model. Among them, the physical model is:

[0101]

[0102] In the above formula, k(T) represents the oxidation rate at temperature T, A represents the pre-exponential factor, which reflects the possibility of effective collisions of molecules per unit time; E represents the activation energy; R represents the gas constant; γ represents the humidity correction coefficient; L represents the humidity.

[0103] The machine learning model uses LSTM (Long Short-Term Memory Network) to capture complex non-linear relationships. The input features include CHMI, wind speed, coal type code, stacking time, and initial density, etc. The output is the density decay rate in the next 7 days or 30 days. It should be noted that CHMI represents the cumulative temperature-humidity index, which comprehensively reflects the long-term impact of temperature and humidity on fuel weathering. The physical model and the machine learning model are fused to construct the weathering loss model, specifically:

[0104] Δρ = ω·Δρ 物理 +(1 - ω)·Δρ LSTM

[0105] In the above formula, Δρ represents the density decay rate output by the weathering loss model, Δρ 物理 represents the density decay rate output by the physical model, Δρ LSTM represents the density decay rate output by the machine learning model, and ω represents the weight parameter, which is adjusted according to the environmental stability. When the environment is stable, ω takes a value that emphasizes the physical model; when the environment undergoes a sudden change, the value emphasizes the machine learning model.

[0106] It can be understood that when training the weathering loss model, at least 1 year of historical coal yard monitoring data is used, and these data cover the weathering conditions of different seasons and different coal types; at the same time, laboratory accelerated aging data is used to simulate the coal aging process under different conditions through a constant temperature and humidity chamber to enrich the training data.

[0107] Step S9: Send the environmental parameters and the fuel pile attribute parameters to the preset weathering loss model to obtain a prediction result;

[0108] In this step, the environmental parameters are collected once an hour, and the coal pile morphology data is updated daily through the fuel pile attribute parameters obtained by drone scanning. According to the input data, the weight loss curve at future moments is output, so as to predict the future weight loss of the coal pile.

[0109] Step S10: Calculate the weight of the fuel pile at future moments according to the prediction result.

[0110] In this embodiment, during the storage process, the weight and quality will decrease due to factors such as weathering. By accurately predicting the weathering loss, the actual inventory of the fuel pile can be grasped in real time and accurately. This can avoid the deviation of inventory data caused by inaccurate calculation of weathering loss, enabling managers to have a clear understanding of the actual available resources in the coal yard, so as to reasonably arrange production plans and resource allocation, and avoid affecting production due to insufficient inventory or causing waste of resources due to inventory backlog.

[0111] Embodiment 2:

[0112] As Figure 2 shown, this embodiment provides a fuel metering system for a coal yard. The system includes an acquisition module 901, a first processing module 902, a second processing module 903, a third processing module 904, a fourth processing module 905, and a fifth processing module 906, which specifically include:

[0113] The acquisition module 901 is used to acquire real-time information collected by the first unmanned aerial vehicle (UAV). The real-time information includes the position of obstacles, dust concentration, and wind speed information.

[0114] The first processing module 902 is used to establish a three-dimensional risk map based on the real-time information collected by the UAV. The three-dimensional risk map includes all fuel piles that need to be weighed in the coal yard.

[0115] The second processing module 903 is used to divide the three-dimensional risk map to obtain several sub-three-dimensional risk maps.

[0116] The third processing module 904 is used to match the sub-three-dimensional risk maps to be detected by the second UAV using an auction algorithm to obtain a matching result.

[0117] The fourth processing module 905 is used to determine the type of the corresponding fuel pile according to the matching result to obtain type information.

[0118] The fifth processing module 906 is used to plan the scanning trajectory of the second UAV to scan the fuel pile according to the type information, and calculate the weight of the fuel pile based on the point cloud data collected by the scan.

[0119] In a specific implementation manner of the present disclosure, the third processing module further includes a first acquisition unit, a first processing unit, and a second processing unit, which specifically include:

[0120] The first acquisition unit is used to acquire the attribute information of each second UAV. The attribute information includes position information, power information, and sensor type.

[0121] The first processing unit is used to determine the bidding price of each second UAV according to the attribute information of each second UAV to obtain bidding price information.

[0122] A second processing unit, configured to match each sub-three-dimensional risk map to be detected by the drone according to the auction price information.

[0123] In a specific embodiment of the present disclosure, a third processing unit, a fourth processing unit, a fifth processing unit, a sixth processing unit, and a seventh processing unit are further included after the second processing unit, and specifically include:

[0124] A third processing unit, configured to obtain type information of an obstacle;

[0125] A fourth processing unit, configured to determine a repulsion coefficient of the obstacle according to the type information of the obstacle;

[0126] A fifth processing unit, configured to generate a repulsive force field around the obstacle according to the repulsion coefficient of the obstacle;

[0127] A sixth processing unit, configured to generate an attractive force field at a fuel pile corresponding to the sub-three-dimensional risk map;

[0128] A seventh processing unit, configured to plan a flight path of the second drone according to the repulsive force field and the attractive force field.

[0129] It should be noted that, regarding the system in the above embodiments, the specific manners in which each module performs operations have been described in detail in the embodiments related to the method, and will not be elaborated herein.

[0130] Embodiment 3:

[0131] Corresponding to the above method embodiment, a fuel metering device for a coal yard is further provided in this embodiment. A fuel metering device for a coal yard described below can be mutually corresponding and referred to with a fuel metering method for a coal yard described above.

[0132] Figure 3 is a block diagram of a fuel metering device 800 for a coal yard shown according to an exemplary embodiment. As Figure 3 shown, the fuel metering device 800 for the coal yard may include: a processor 801, a memory 802. The fuel metering device 800 for the coal yard may further include one or more of a multimedia component 803, an I / O interface 804, and a communication component 805.

[0133] Among them, the processor 801 is used to control the overall operation of the fuel metering device 800 in the coal yard to complete all or part of the steps in the above-mentioned fuel metering method for the coal yard. The memory 802 is used to store various types of data to support the operation of the fuel metering device 800 in the coal yard. These data may include, for example, instructions for any application program or method operating on the fuel metering device 800 in the coal yard, as well as application-related data, such as contact data, sent and received messages, pictures, audio, video, and so on. The memory 802 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk. The multimedia component 803 may include a screen and an audio component. Among them, the screen may be a touch screen, and the audio component is used to output and / or input audio signals. For example, the audio component may include a microphone, and the microphone is used to receive external audio signals. The received audio signals may be further stored in the memory 802 or sent through the communication component 805. The audio component also includes at least one speaker for outputting audio signals. The I / O interface 804 provides an interface between the processor 801 and other interface modules, and the above-mentioned other interface modules may be a keyboard, a mouse, buttons, etc. These buttons may be virtual buttons or physical buttons. The communication component 805 is used for wired or wireless communication between the fuel metering device 800 in the coal yard and other devices. Wireless communication, such as Wi-Fi, Bluetooth, near field communication (NFC), 2G, 3G or 4G, or a combination of one or more of them. Therefore, the corresponding communication component 805 may include: a Wi-Fi module, a Bluetooth module, an NFC module.

[0134] In an exemplary embodiment, the fuel metering device 800 in the coal yard can be implemented by one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors or other electronic components, and is used to execute the above-mentioned fuel metering method for the coal yard.

[0135] In another exemplary embodiment, a computer-readable storage medium including program instructions is further provided. When the program instructions are executed by a processor, the steps of the above-mentioned fuel metering method for the coal yard are implemented. For example, the computer-readable storage medium can be the above-mentioned memory 802 including program instructions, and the above-mentioned program instructions can be executed by the processor 801 of the fuel metering device 800 in the coal yard to complete the above-mentioned fuel metering method for the coal yard.

[0136] Embodiment 4:

[0137] Corresponding to the above method embodiment, a readable storage medium is further provided in this embodiment. A readable storage medium described below can be correspondingly referred to with a fuel metering method for a coal yard described above.

[0138] A readable storage medium has a computer program stored thereon. When the computer program is executed by a processor, the steps of the fuel metering method in the above method embodiment are implemented.

[0139] The readable storage medium can specifically be various readable storage media such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disc, etc., which can store program codes.

[0140] The above are only the preferred embodiments of the present invention and are not used to limit the present invention. For those skilled in the art, the present invention can have various changes and modifications. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

[0141] As described above, it is only the specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of changes or substitutions, which should all be covered within the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. A fuel metering method for a coal yard, characterized in that, Including: Obtain the real-time information collected by the first unmanned aerial vehicle (UAV), where the real-time information includes the position of obstacles, dust concentration, and wind speed information; Establish a three-dimensional risk map based on the real-time information collected by the UAV, where the three-dimensional risk map includes all fuel piles that need to be weighed in the coal yard; Divide the three-dimensional risk map to obtain several sub-three-dimensional risk maps; Use the auction algorithm to match the sub-three-dimensional risk maps to be detected by the second UAV to obtain a matching result; Determine the type of the corresponding fuel pile according to the matching result to obtain type information; Plan the scanning trajectory of the second UAV to scan the fuel pile according to the type information, and calculate the weight of the fuel pile based on the point cloud data collected by the scanning.

2. The fuel metering method for a coal yard according to claim 1, characterized in that, Using the auction algorithm to match the sub-three-dimensional risk maps to be detected by the second UAV includes: Obtain the attribute information of each second UAV, where the attribute information includes position information, power information, and sensor type; Determine the bid price of each second UAV according to the attribute information of each second UAV to obtain bid price information; Match the sub-three-dimensional risk maps to be detected by each UAV according to the bid price information.

3. The fuel metering method for a coal yard according to claim 2, characterized in that After matching the sub-three-dimensional risk maps to be detected by each UAV according to the bid price information, it further includes: Obtain the type information of the obstacles; Determine the repulsion coefficient of the obstacles according to the type information of the obstacles; Generate a repulsion field around the obstacles according to the repulsion coefficient of the obstacles; Generate an attraction field at the corresponding fuel pile in the sub-three-dimensional risk map; Plan the flight path of the second UAV according to the repulsion field and the attraction field.

4. The fuel metering method for a coal yard according to claim 1, characterized in that Planning the scanning trajectory of the second UAV to scan the fuel pile according to the type information includes: When the type information is a tall fuel pile, obtain the surface slope information of the fuel pile, where the tall fuel pile is a fuel pile with a height greater than the height threshold; Judge whether the surface slope information of the fuel pile is greater than the angle threshold to obtain a judgment result; Plan the scanning trajectory of the fuel pile according to the judgment result.

5. The fuel metering method for a coal yard according to claim 1, characterized in that Calculating the weight of the fuel pile based on the point cloud data collected by the scanning includes: Obtain the multi-spectral image of the fuel pile; Extract features from the multi-spectral image of the fuel pile to obtain multi-spectral texture features; Segment the fuel pile according to the multi-spectral texture features and the point cloud data to obtain the segmented fuel pile; Establish a three-dimensional density field according to the segmented fuel pile; Calculate the weight of the fuel pile according to the three-dimensional density field.

6. The fuel metering method for a coal yard according to claim 5, wherein, Establishing a three-dimensional density field according to the segmented fuel pile includes: Obtain the multi-spectral image corresponding to the segmented fuel pile; Send the multi-spectral image corresponding to the segmented fuel pile to the partial least squares regression model to obtain ash content information; Obtain the moisture content information corresponding to the segmented fuel pile; Calculate according to the moisture content information, the ash content information, and the reference density of the fuel pile to obtain density information; Establish a three-dimensional density field according to the density information.

7. The fuel metering method for a coal yard according to claim 1, characterized in that, And after calculating the weight of the fuel pile based on the point cloud data collected by the scanning, it includes: Obtain environmental parameters and fuel pile attribute parameters; Obtain a preset weathering loss model, where the preset weathering loss model is used to predict the weight loss at a future time; Send the environmental parameters and the fuel pile attribute parameters to the preset weathering loss model to obtain a prediction result; Calculate the weight of the fuel pile at a future time according to the prediction result.

8. A fuel metering system for a coal yard, characterized in that, It includes: An acquisition module, configured to acquire real-time information collected by a first drone, where the real-time information includes the position of obstacles, dust concentration, and wind speed information; A first processing module, configured to establish a three-dimensional risk map according to the real-time information collected by the drone, where the three-dimensional risk map includes all fuel piles in the coal yard that need to be weighed; A second processing module, configured to divide the three-dimensional risk map to obtain a number of sub-three-dimensional risk maps; A third processing module, configured to use an auction algorithm to match the sub-three-dimensional risk maps to be detected by a second drone to obtain a matching result; A fourth processing module, configured to determine the type of the corresponding fuel pile according to the matching result to obtain type information; A fifth processing module, configured to plan a scanning trajectory for the second drone to scan the fuel pile according to the type information, and calculate the weight of the fuel pile according to the point cloud data collected by the scan.

9. The fuel metering system for a coal yard according to claim 1, characterized in that, The third processing module includes: A first acquisition unit, configured to acquire the attribute information of each second drone, where the attribute information includes position information, power information, and sensor type; A first processing unit, configured to determine the bidding price of each second drone according to the attribute information of each second drone to obtain bidding price information; A second processing unit, configured to match the sub-three-dimensional risk maps to be detected by each drone according to the bidding price information.

10. The fuel metering system for a coal yard according to claim 9, wherein, After the second processing unit, it further includes: A third processing unit, configured to acquire the type information of the obstacle; A fourth processing unit, configured to determine the repulsion coefficient of the obstacle according to the type information of the obstacle; A fifth processing unit, configured to generate a repulsion field around the obstacle according to the repulsion coefficient of the obstacle; A sixth processing unit, configured to generate an attraction field at the corresponding fuel pile in the sub-three-dimensional risk map; A seventh processing unit, configured to plan the flight path of the second drone according to the repulsion field and the attraction field.

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