New energy resource investigation method and system based on unmanned aerial vehicle

Abstract

The invention discloses a new energy resource reconnaissance method and system based on an unmanned aerial vehicle, and relates to the technical field of unmanned aerial vehicle resource reconnaissance, and the method comprises the steps: collecting virtual terrain resource data, constructing an optimal flight path model, training the optimal flight path model, and obtaining a current optimal flight path; the unmanned aerial vehicle platform starts the unmanned aerial vehicle and executes the current optimal flight path; the RTK carrier phase difference positioning technology is adopted to carry out field positioning and investigation, and positioning data and real-time terrain resource data are transmitted to the cloud computing platform; fusing the real-time topographic resource data with the virtual topographic data to obtain fused topographic data, and adjusting the flight path of the unmanned aerial vehicle according to the fused topographic data; potential distribution rules of new energy resources are mined through big data analysis, a visual exploration report is generated, and the whole exploration process of the unmanned aerial vehicle is monitored. According to the invention, the efficient and safe completion of the flight task is ensured, and the efficiency and accuracy of investigation are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicle (UAV) resource exploration, and in particular to a new energy resource exploration method and system based on unmanned aerial vehicle (UAV). Background Art

[0002] Since the new energy resource survey sites are often located in remote mountainous areas, far away from towns, the basic digital conditions of the sites are generally poor. The on-site management of construction personnel and survey personnel during field surveys still relies on traditional means, lacks timely and effective real-time supervision, and cannot upload real-time audio and video and positioning records of personnel. Due to the general inconvenience of transportation in new energy engineering projects, the walking distance of the survey work is long, time-consuming, risky, and the output of the survey results is also limited.

[0003] In order to meet the real-time positioning and transmission needs and on-site voice communication functions in a non-public network environment, and to realize real-time audio and video upload and remote command in the network-accessible area, the application of field survey communication technology based on public-private fusion communication technology is studied. At present, the communication system is mainly based on analog communication system, digital conventional communication system, and digital trunking communication system. Such systems generally use dedicated frequency points to build wireless dedicated voice communication networks. There are generally problems such as difficulty in frequency application, large investment scale, and lack of video and multimedia service capabilities, which cannot meet the current business needs of new energy engineering field surveys. Summary of the invention

[0004] The purpose of this section is to summarize some aspects of embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the specification abstract and the invention title of this application to avoid blurring the purpose of this section, the specification abstract and the invention title, and such simplifications or omissions cannot be used to limit the scope of the present invention.

[0005] In view of the above existing problems, the present invention is proposed.

[0006] Therefore, the present invention provides a new energy resource survey method and system based on unmanned aerial vehicle, which can solve the problems mentioned in the background technology.

[0007] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0008] In a first aspect, the present invention provides a new energy resource survey method based on a drone, which includes collecting virtual terrain resource data, constructing an optimal flight path model and training the optimal flight path model to obtain a current optimal flight path, and a drone platform starts the drone and executes the current optimal flight path;

[0009] Use RTK carrier phase differential positioning technology for on-site positioning and survey, and transmit positioning data and real-time terrain resource data to the cloud computing platform;

[0010] The real-time terrain resource data is integrated with the virtual terrain data to obtain integrated terrain data, and the flight path of the UAV is adjusted according to the integrated terrain data;

[0011] Through big data analysis, we can explore the potential distribution patterns of new energy resources, generate visual survey reports, and monitor the entire drone exploration process.

[0012] As a preferred solution of the new energy resource survey method based on drones of the present invention, wherein: the virtual terrain resource data includes virtual wind speed data, virtual temperature data, digital elevation model data and digital surface model data;

[0013] The virtual wind speed data includes a wind speed sensitivity coefficient and a virtual wind speed;

[0014] The constructing of the optimal flight path model comprises:

[0015] The optimal flight path is automatically planned using machine learning algorithms combined with virtual terrain data. The path calculation formula is as follows:

[0016]

[0017] Among them, Path opt is the optimal flight path, To find the path P with the minimum path cost, dist(P) is the actual flight distance of path P, β is the wind speed sensitivity coefficient, T e (t) is the temperature at time t, λ1, λ2, λ3 are weight coefficients, V w (t) is the wind speed at time t, and T1 is the planned time period.

[0018] As a preferred solution of the new energy resource exploration method based on unmanned aerial vehicle of the present invention, the training of the optimal flight path model includes:

[0019] Calculate the actual flight distance dist(P) of the path based on the virtual terrain data and real-time weather data;

[0020] According to the wind speed sensitivity coefficient β and the wind speed V at time t w (t) Calculate the path cost;

[0021] Temperature T according to time t e (t) and adjust the flight path with the planned time period T1;

[0022] Use optimization algorithm to solve the optimal flight path Pathopt , until it fits the actual path and the training is completed;

[0023] The executing the optimal flight path comprises:

[0024] Start the UAV platform and activate the control unit and path planning unit;

[0025] The optimal flight path is imported into the drone navigation system, and the drone flies according to the optimal flight path to start flight survey;

[0026] Monitor the flight status in real time, collect terrain and resource data through the sensor integration module, and upload the flight data and resource data to the cloud computing platform.

[0027] As a preferred solution of the new energy resource exploration method based on unmanned aerial vehicles of the present invention, the on-site positioning and exploration includes:

[0028] Start the UAV platform and activate the RTK carrier phase differential positioning unit. The UAV performs initial positioning and obtains RTK positioning data.

[0029] The drone begins to fly and survey along the optimal flight path;

[0030] The drone collects terrain resource data in real time through the sensor integration unit;

[0031] The drone uploads the collected real-time terrain resource data and RTK positioning data to the cloud computing platform through the multimedia communication unit;

[0032] In areas covered by the public network, the drone communicates with the cloud computing platform in real time via voice, video and multimedia text messages through the multimedia communication unit, and regularly uploads flight data and resource data to the cloud computing platform;

[0033] In areas that are not covered by the public network, the drone maintains multimedia communication with the cloud computing platform through a narrowband private network communication unit;

[0034] The drone monitors the signal strength of all networks in the current area in real time through the signal strength monitoring unit;

[0035] When the signal strength is lower than the signal threshold and there is a backup network with higher signal strength, the network switching unit automatically switches to the network with better signal.

[0036] As a preferred solution of the new energy resource survey method based on unmanned aerial vehicle of the present invention, the fusion of the real-time terrain resource data and the virtual terrain data includes:

[0037] The cloud computing platform fuses real-time terrain resource data with virtual terrain data. The data fusion calculation formula is as follows:

[0038]

[0039] Among them, D RT (t) is the value of real-time terrain resource data at time t, D VT (t) is the value of the virtual terrain data at time t, w i is the kernel function weight coefficient, f i is the kernel function for fusion of different types of data, F fusion (t) is the fusion data result;

[0040] The data fused by the kernel function includes virtual wind speed data, virtual temperature data, digital elevation model data and digital surface model data. The calculation formula is as follows:

[0041] f1(D RT (t),D VT (t),β,T e (t)) = β·N(D RT (t))·D VT (t)

[0042]

[0043] Where N(x) is the normalization function and σ is the standard deviation;

[0044] The adjusting of the flight path of the drone includes:

[0045] Based on the fused terrain data, the cloud computing platform dynamically adjusts the flight path to further optimize the UAV’s flight path;

[0046] The adjusted flight path is fed back to the UAV navigation system through the communication module. The UAV continues to fly according to the new path and continues to collect and transmit data.

[0047] As a preferred solution of the new energy resource exploration method based on drones of the present invention, the mining of the potential distribution law of new energy resources includes:

[0048] Use big data analysis technology to conduct in-depth mining of processed new energy resource data, identify the potential distribution patterns of new energy resources, and analyze the distribution trends and hot spots of new energy resources;

[0049] Extract important features from the data and reduce the data dimension through principal component analysis PCA;

[0050] PolyDBSCAN is used to identify patterns and outliers in the data and determine the potential distribution areas of new energy resources;

[0051] Kriging interpolation is used to analyze the distribution of new energy resources in geographic space, and spatiotemporal scanning statistics are used to analyze the temporal and spatial variation of resource distribution, identifying hot spots and variation trends;

[0052] Use visualization tools to transform analysis results into intuitive information;

[0053] The intuitive information includes resource distribution forecast maps and decision support information;

[0054] The resource distribution prediction map includes a resource distribution map, a heat map and a trend analysis map;

[0055] The decision support information includes exploration execution strategies, recommended exploration areas and action recommendations;

[0056] Develop new energy resource exploration execution strategies based on the data and analysis results in the visual survey report.

[0057] As a preferred solution of the new energy resource exploration method based on drones of the present invention, the whole process monitoring of the drone exploration process includes:

[0058] Real-time monitoring of the drone’s battery level, signal strength, and equipment operating status;

[0059] When low power, signal loss or equipment failure is detected, the alarm unit immediately triggers a warning and selects a network through the fusion communication unit to send the warning information to the remote terminal;

[0060] A radio frequency unit is used to establish a communication connection between the drone and the cloud computing platform in a narrowband private network, and a security encryption unit is used to encrypt the communication process.

[0061] In a second aspect, the present invention provides a new energy resource exploration system based on a drone, which includes: a model building module, a resource exploration module, a data fusion module, and a strategy formulation module;

[0062] The model building module is used to collect virtual terrain resource data, build an optimal flight path model and train the optimal flight path model to obtain the current optimal flight path, and the UAV platform starts the UAV and executes the current optimal flight path;

[0063] The resource exploration module is used to use RTK carrier phase differential positioning technology to perform on-site positioning and exploration, and transmit positioning data and real-time terrain resource data to the cloud computing platform;

[0064] The data fusion module is used to fuse the real-time terrain resource data with the virtual terrain data to obtain fused terrain data, and adjust the flight path of the UAV according to the fused terrain data;

[0065] The strategy formulation module is used to explore the potential distribution patterns of new energy resources through big data analysis, generate visual survey reports, and monitor the entire drone exploration process.

[0066] In a third aspect, the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the processor executes the computer program, the steps of a new energy resource exploration method based on a drone are implemented.

[0067] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, the steps of a new energy resource exploration method based on a drone are implemented.

[0068] Compared with the prior art, the beneficial effects of the present invention are as follows: by collecting virtual terrain resource data and constructing an optimal flight path model, combining RTK carrier phase differential positioning technology for on-site positioning and real-time data transmission, and then fusing real-time terrain resource data with virtual terrain data, dynamically adjusting the UAV flight path, and finally mining the potential distribution patterns of new energy resources through big data analysis and generating a visual survey report, as well as monitoring the entire UAV exploration process, this method realizes precise positioning, efficient data fusion, intelligent path optimization and comprehensive monitoring in the process of new energy resource exploration, improves the efficiency and accuracy of exploration, and provides a scientific basis for the development and utilization of new energy resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0070] Figure 1 A method flow chart of a new energy resource exploration method and system based on an unmanned aerial vehicle provided in one embodiment of the present invention;

[0071] Figure 2 An internal structural diagram of a computer device of a new energy resource exploration method and system based on a drone provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0072] In order to make the above-mentioned purposes, features and advantages of the present invention more understandable, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0073] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0074] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0075] Example 1

[0076] Reference Figure 1-Figure 2 , which is the first embodiment of the present invention, provides a new energy resource survey method based on a drone, comprising:

[0077] This application provides a method that can effectively solve the above-mentioned problems. Next, we will combine multiple embodiments to explain in detail how to implement the new energy resource exploration method based on drones;

[0078] Figure 1 A method flow chart of a new energy resource survey method and system based on drone is shown, including:

[0079] S1: Collect virtual terrain resource data, build an optimal flight path model and train the optimal flight path model to obtain the current optimal flight path. The UAV platform starts the UAV and executes the current optimal flight path.

[0080] Further, the virtual terrain resource data includes virtual wind speed data, virtual temperature data, digital elevation model data, and digital surface model data;

[0081] The virtual wind speed data includes wind speed sensitivity coefficient and virtual wind speed;

[0082] Furthermore, constructing the optimal flight path model includes:

[0083] The optimal flight path is automatically planned using machine learning algorithms combined with virtual terrain data. The path calculation formula is as follows:

[0084]

[0085] Among them, Path opt is the optimal flight path, To find the path P with the minimum path cost, dist(P) is the actual flight distance of path P, β is the wind speed sensitivity coefficient, T e (t) is the temperature at time t, λ1, λ2, λ3 are weight coefficients, V w (t) is the wind speed at time t, T1 is the planning time period; Path opt The range of is the set of all possible paths P, where the optimal path is the path with the minimum cost. The range of dist(P) is a positive real number, representing the distance of the flight path. exp(-β·V w The value range of (t) is (0,1), which represents the normalized influence factor of wind speed. The range of is the integral result, which represents the cumulative impact of meteorological conditions on the path;

[0086] The path planning module makes dynamic adjustments based on real-time environmental information (such as wind speed and weather conditions) to ensure the optimality and safety of the flight path.

[0087] Training the optimal flight path model includes:

[0088] Calculate the actual flight distance dist(P) of the path based on the virtual terrain data and real-time weather data;

[0089] According to the wind speed sensitivity coefficient β and the wind speed V at time t w (t) Calculate the path cost;

[0090] Temperature T according to time t e (t) and adjust the flight path with the planned time period T1;

[0091] Use optimization algorithm to solve the optimal flight path Path opt , until it fits the actual path and the training is completed.

[0092] Further, executing the optimal flight path includes:

[0093] Start the UAV platform and activate the control unit and path planning unit;

[0094] Import the optimal flight path into the drone navigation system, and the drone takes off according to the planned path and starts the flight survey;

[0095] Monitor flight status in real time, collect terrain and resource data through sensor integration modules, and upload flight data and resource data to the cloud computing platform.

[0096] It should be noted that the present invention collects virtual terrain resource data, combines machine learning algorithms to automatically plan the optimal flight path, and uses virtual data and real-time meteorological data to train the model to achieve dynamic adjustment of the path. Its beneficial effect is to improve the accuracy and safety of UAV flight path planning, ensure the optimality and real-time adaptability of the flight path, and reduce flight time and energy consumption. In practical applications, data collection is carried out through real-time monitoring and sensor integration modules to ensure the efficient and safe completion of flight missions and improve the efficiency and accuracy of resource exploration.

[0097] S2: The UAV uses RTK carrier phase differential positioning technology to perform on-site positioning and survey, and transmits the positioning data and the collected real-time terrain resource data to the cloud computing platform;

[0098] Further, on-site positioning and investigation include:

[0099] Start the UAV platform, activate the RTK carrier phase differential positioning unit, and the UAV performs initial positioning to obtain accurate location information;

[0100] The drone begins to fly and survey along the optimal flight path;

[0101] The drone collects terrain data in real time through the sensor integration module, including terrain elevation, surface model, etc.

[0102] Drones collect new energy resource data, including solar radiation, wind speed, wind direction, etc.

[0103] The drone uploads the collected real-time terrain resource data and RTK positioning data to the cloud computing platform through the multimedia communication unit;

[0104] In areas covered by the public network, the drone communicates with the remote terminal in real time via voice, video and multimedia text messages through the multimedia communication module, and regularly uploads flight data and resource data to the cloud computing platform;

[0105] In areas that are not covered by the public network, the drone maintains multimedia communication with the cloud computing platform through a narrowband private network communication unit;

[0106] The drone uses the signal strength monitoring unit to monitor the signal strength of all networks in the current area in real time;

[0107] When the signal strength is lower than the threshold and there is a backup network with higher signal strength, the network switching unit automatically switches to the better network.

[0108] S3: The cloud computing platform fuses the real-time terrain resource data with the virtual terrain data to form fused terrain data, and adjusts the flight path of the UAV according to the fused terrain data;

[0109] Furthermore, the fusion of the real-time terrain resource data and the virtual terrain data includes:

[0110] The cloud computing platform fuses real-time terrain resource data with virtual terrain data to generate a more accurate terrain model. The data fusion calculation formula is as follows:

[0111]

[0112] Among them, D RT (t) is the value of real-time terrain resource data at time t, D VT (t) is the value of the virtual terrain data at time t, w i is the kernel function weight coefficient, f i is the kernel function for fusion of different types of data, F fusion (t) is the fusion data result;

[0113] The kernel function includes virtual wind speed data, virtual temperature data, digital elevation model data, and digital surface model data. The calculation formula is as follows:

[0114] f1(D RT (t),D VT (t),β,T e (t)) = β·N(D RT (t))·D VT (t)

[0115]

[0116] Where N(x) is the normalization function and σ is the standard deviation.

[0117] Further, adjusting the flight path of the drone includes,

[0118] Based on the fused terrain data, the cloud computing platform dynamically adjusts the flight path to further optimize the UAV’s flight path;

[0119] The adjusted flight path is fed back to the UAV navigation system through the communication module. The UAV continues to fly according to the new path and continues to collect and transmit data.

[0120] S4: The cloud computing platform mines the potential distribution patterns of new energy resources through big data analysis, generates visual survey reports, and monitors the entire drone exploration process.

[0121] Further, exploring the potential distribution patterns of new energy resources includes:

[0122] Use big data analysis technology to conduct in-depth mining of processed new energy resource data, identify the potential distribution patterns of new energy resources, and analyze their distribution trends and hot spots.

[0123] Extract important features from the data through principal component analysis (PCA) to reduce the data dimension;

[0124] And use polyDBSCAN to identify patterns and outliers in the data and distinguish potential distribution areas of new energy resources;

[0125] Kriging interpolation is used to analyze the distribution patterns of new energy resources in geographic space, and spatiotemporal scanning statistics are used to analyze the temporal and spatial changes in resource distribution and identify hot spots and changing trends.

[0126] Use visualization tools to transform analysis results into intuitive information;

[0127] Intuitive information includes resource distribution forecast maps and decision support information;

[0128] Resource distribution prediction diagrams include resource distribution diagrams, heat maps, and trend analysis diagrams;

[0129] Decision support information includes exploration execution strategies, recommended exploration areas, and suggested actions;

[0130] Develop detailed execution strategies for new energy resource exploration based on the data and analysis results in the visual survey report.

[0131] Specifically, when the collected data includes solar energy resources and wind energy resources, the application example analyses include: solar energy resource analysis and wind energy resource analysis;

[0132] Solar resource analysis includes,

[0133] Data collection involves using drones to collect solar radiation intensity, temperature, and weather conditions in different areas;

[0134] Pattern recognition is to identify areas with rich solar energy resources through clustering algorithms;

[0135] Trend analysis is to identify the seasonal trend of solar energy resources using time series analysis;

[0136] Visualization is to generate solar resource distribution maps and solar radiation intensity heat maps to show the resource conditions in different regions;

[0137] Wind energy resource analysis includes:

[0138] Data collection collects wind speed, wind direction and air pressure data at different altitudes and locations for drones;

[0139] Pattern recognition uses clustering algorithms to identify areas with rich wind energy resources;

[0140] Trend analysis is to identify the changing trends and periodic characteristics of wind energy resources using spatiotemporal analysis techniques;

[0141] Visualization generates wind energy resource distribution maps and wind speed intensity heat maps to show resource conditions at different heights and locations.

[0142] Furthermore, the whole process of drone exploration monitoring includes:

[0143] Real-time monitoring of the drone’s battery level, signal strength, and equipment operating status;

[0144] When insufficient power, signal loss or equipment failure is detected, the alarm unit immediately triggers a warning and selects a smooth network through the fusion communication unit to send the warning information to the remote terminal in a timely manner;

[0145] The radio frequency module is used to establish a communication connection between the drone and the cloud computing platform in a narrowband private network, and the security encryption unit is used to encrypt the communication process to ensure the security and integrity of the communication data.

[0146] Furthermore, the present embodiment also provides a new energy resource exploration system based on a drone, comprising: a model building module, a resource exploration module, a data fusion module, and a strategy formulation module;

[0147] The model building module is used to collect virtual terrain resource data, build an optimal flight path model and train the optimal flight path model to obtain the current optimal flight path. The UAV platform starts the UAV and executes the current optimal flight path.

[0148] The resource exploration module is used to conduct on-site positioning and survey using RTK carrier phase differential positioning technology, and transmit positioning data and real-time terrain resource data to the cloud computing platform;

[0149] The data fusion module is used to fuse the real-time terrain resource data with the virtual terrain data to obtain fused terrain data, and adjust the flight path of the UAV according to the fused terrain data;

[0150] The strategy formulation module is used to explore the potential distribution patterns of new energy resources through big data analysis, generate visual survey reports, and monitor the entire drone exploration process.

[0151] This embodiment also provides a computer device, which may be a terminal, and its internal structure diagram may be as shown in FIG. Figure 2As shown. The computer device includes a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, a comprehensive evaluation method suitable for orderly access of large-scale distributed power sources is implemented. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covered on the display screen, or a key, trackball or touchpad set on the computer device housing, or an external keyboard, touchpad or mouse.

[0152] This embodiment further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented:

[0153] Collect virtual terrain resource data, build an optimal flight path model and train the optimal flight path model to obtain a current optimal flight path, and the UAV platform starts the UAV and executes the current optimal flight path;

[0154] Use RTK carrier phase differential positioning technology for on-site positioning and survey, and transmit positioning data and real-time terrain resource data to the cloud computing platform;

[0155] The real-time terrain resource data is integrated with the virtual terrain data to obtain integrated terrain data, and the flight path of the UAV is adjusted according to the integrated terrain data;

[0156] Through big data analysis, we can explore the potential distribution patterns of new energy resources, generate visual survey reports, and monitor the entire drone exploration process.

[0157] Example 2

[0158] Reference Figure 1 - Figure 2 , which is the second embodiment of the present invention, and this embodiment provides a new energy resource exploration method based on unmanned aerial vehicles. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0159] In order to verify the effectiveness of the new energy resource exploration method based on drones, an experiment was designed to conduct new energy resource exploration in a mountainous area. The experimental area has complex terrain and changeable weather conditions, which is highly difficult and challenging. The specific steps of the experiment are as follows:

[0160] Virtual terrain resource data collection: Collect virtual wind speed data, virtual temperature data, digital elevation model data and digital surface model data in the test area. Virtual wind speed data includes wind speed sensitivity coefficient and virtual wind speed to simulate the actual wind speed conditions in the flight area. Virtual temperature data reflects the temperature changes in the test area, and digital elevation model data and digital surface model data provide the terrain characteristics of the test area.

[0161] Build and train the optimal flight path model: Use machine learning algorithms combined with virtual terrain data to automatically plan the optimal flight path. Through the training model, calculate the actual flight distance of the path based on virtual terrain data and real-time meteorological data, adjust the flight path using wind speed sensitivity coefficients and temperature data, and use optimization algorithms to solve the optimal flight path until the model is highly consistent with the actual path.

[0162] Start the drone and execute the optimal flight path: After the drone platform is started, the control unit and path planning unit are activated to import the optimal flight path into the drone navigation system. The drone flies according to the planned path and collects terrain and resource data in real time through the sensor integration module.

[0163] On-site positioning and survey: RTK carrier phase differential positioning technology is used for precise positioning, and positioning data and terrain resource data are transmitted to the cloud computing platform in real time. In areas covered by the public network, the drone communicates with the cloud computing platform in real time through the multimedia communication unit, and regularly uploads flight data and resource data. In areas not covered by the public network, the drone maintains communication with the cloud computing platform through the narrowband private network communication unit to ensure the continuity and stability of data transmission.

[0164] Data fusion and path adjustment: The cloud computing platform integrates real-time terrain resource data with virtual terrain data to obtain fused terrain data. Based on the fused terrain data, the flight path of the drone is dynamically adjusted to further optimize the flight path, and the adjusted path is fed back to the drone navigation system, and the drone continues to fly along the new path.

[0165] Mining the potential distribution patterns of new energy resources: Using big data analysis technology, we can deeply mine the collected new energy resource data, identify the potential distribution patterns of new energy resources, and generate a visual survey report. The report includes resource distribution prediction maps, heat maps, trend analysis maps, and decision support information.

[0166] Full monitoring of the drone exploration process: real-time monitoring of the drone's power, signal strength and equipment operating status. When insufficient power, signal loss or equipment failure is detected, the alarm unit immediately triggers a warning and sends the warning information to the remote terminal through the fusion communication unit to ensure flight safety.

[0167] Table 1 Experimental data table

[0168]

[0169] Through the above data analysis, it can be seen that the method of the present invention can effectively plan and adjust the flight path of the UAV under different terrain and meteorological conditions. The following is a specific analysis:

[0170] Comparison of flight path length and time: The data show that the flight path length and time of the UAV under different terrain conditions are relatively balanced using the method of the present invention. For example, the flight path length of test point 1 is 12.4 km and the flight time is 30 minutes; the flight path length of test point 5 is 14.3 km and the flight time is 33 minutes. Despite the differences in terrain and wind speed, the optimization of the flight path keeps the flight time within a reasonable range.

[0171] Impact of virtual wind speed data: The collection and application of wind speed sensitivity coefficient and virtual wind speed data enable the UAV to make effective path adjustments under different wind speed conditions. For example, the virtual wind speed at test point 3 is 6.0m / s. Although the wind speed is high, the UAV can still complete a 15.2km flight in 35 minutes through the path optimization algorithm.

[0172] Effect of real-time data fusion: Through the integration of real-time terrain resource data and virtual terrain data, the cloud computing platform can dynamically adjust the flight path to ensure the optimality and safety of the flight. For example, the flight path length and time of test point 2 and test point 4 are relatively close, although their terrain data and meteorological conditions are different, which reflects the key role of data fusion technology in path optimization.

[0173] Mining the potential distribution patterns of new energy resources: The application of big data analysis technology enables the present invention to accurately identify the potential distribution patterns of new energy resources and generate visual reports to assist decision-making. For example, through principal component analysis and DBSCAN cluster analysis, the resource distribution trends and hot spots of test points 1 and 5 can be identified, providing reliable resource exploration data support.

[0174] By comparing with the prior art, the creativity and novelty of the present invention are mainly reflected in the following aspects:

[0175] Application of data fusion technology: In the prior art, virtual terrain data or real-time terrain data is often used alone for path planning, while the present invention makes path planning more accurate and flexible by fusing virtual and real-time data.

[0176] Use of optimization algorithm: The optimization algorithm is used to solve the optimal flight path, ensuring the efficient flight of the UAV in complex terrain and weather conditions.

[0177] Innovation of big data analysis: Big data analysis technology is used to conduct in-depth mining of new energy resources, generate visual reports, and provide decision-making support information, which is lacking in existing technologies.

[0178] In summary, the method of the present invention has demonstrated significant innovation and practical value in terms of path planning, real-time data fusion and mining of new energy resource distribution patterns.

[0179] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

[0180] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of complete hardware embodiments, complete software embodiments, or embodiments in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code. The scheme in the embodiments of the present application can be implemented in various computer languages, for example, object-oriented programming language Java and literal scripting language JavaScript, etc.

[0181] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0182] These computer program instructions may also be stored in a computer readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0183] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0184] Although the preferred embodiments of the present application have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0185] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A new energy resource exploration method based on unmanned aerial vehicle, characterized in that: include, Collect virtual terrain resource data, build an optimal flight path model and train the optimal flight path model to obtain a current optimal flight path, and the UAV platform starts the UAV and executes the current optimal flight path; Use RTK carrier phase differential positioning technology for on-site positioning and survey, and transmit positioning data and real-time terrain resource data to the cloud computing platform; The real-time terrain resource data is integrated with the virtual terrain data to obtain integrated terrain data, and the flight path of the UAV is adjusted according to the integrated terrain data; Through big data analysis, we can explore the potential distribution patterns of new energy resources, generate visual survey reports, and monitor the entire drone exploration process.

2. The new energy resource exploration method based on unmanned aerial vehicle according to claim 1, characterized in that: The virtual terrain resource data includes virtual wind speed data, virtual temperature data, digital elevation model data and digital surface model data; The virtual wind speed data includes a wind speed sensitivity coefficient and a virtual wind speed; The constructing of the optimal flight path model comprises: The optimal flight path is automatically planned using machine learning algorithms combined with virtual terrain data. The path calculation formula is as follows: Among them, Path opt is the optimal flight path, To find the path P with the minimum path cost, dist(P) is the actual flight distance of path P, β is the wind speed sensitivity coefficient, T e (t) is the temperature at time t, λ1, λ2, λ3 are weight coefficients, V ω (t) is the wind speed at time t, and T1 is the planned time period.

3. The new energy resource exploration method based on unmanned aerial vehicle according to claim 2, characterized in that: The training of the optimal flight path model comprises: Calculate the actual flight distance dist(P) of the path based on the virtual terrain data and real-time weather data; According to the wind speed sensitivity coefficient β and the wind speed V at time t ω (t) Calculate the path cost; Temperature T according to time t e (t) and adjust the flight path with the planned time period T1; Use optimization algorithm to solve the optimal flight path Path opt , until it fits the actual path and the training is completed; The executing the optimal flight path comprises: Start the drone platform and activate the control unit and path planning unit; The optimal flight path is imported into the UAV navigation system, and the UAV flies according to the optimal flight path to start the flight survey; Monitor the flight status in real time, collect terrain and resource data through the sensor integration module, and upload the flight data and resource data to the cloud computing platform.

4. The new energy resource exploration method based on unmanned aerial vehicle according to claim 3, characterized in that: The on-site positioning and investigation includes: Start the UAV platform and activate the RTK carrier phase differential positioning unit. The UAV performs initial positioning and obtains RTK positioning data. The drone begins to fly and survey along the optimal flight path; The drone collects terrain resource data in real time through the sensor integration unit; The drone uploads the collected real-time terrain resource data and RTK positioning data to the cloud computing platform through the multimedia communication unit; In areas covered by the public network, the drone communicates with the cloud computing platform in real time via voice, video and multimedia text messages through the multimedia communication unit, and regularly uploads flight data and resource data to the cloud computing platform; In areas that are not covered by the public network, the drone maintains multimedia communication with the cloud computing platform through a narrowband private network communication unit; The drone monitors the signal strength of all networks in the current area in real time through the signal strength monitoring unit; When the signal strength is lower than the signal threshold and there is a backup network with higher signal strength, the network switching unit automatically switches to the network with better signal.

5. The new energy resource exploration method based on unmanned aerial vehicle according to claim 4, characterized in that: The fusion of the real-time terrain resource data and the virtual terrain data includes: The cloud computing platform fuses real-time terrain resource data with virtual terrain data. The data fusion calculation formula is as follows: Among them, D RT (t) is the value of real-time terrain resource data at time t, D VT (t) is the value of the virtual terrain data at time t, w i is the kernel function weight coefficient, f i is the kernel function for fusion of different types of data, F fusion (t) is the fusion data result; The data fused by the kernel function includes virtual wind speed data, virtual temperature data, digital elevation model data and digital surface model data. The calculation formula is as follows: f1(D RT (t),D VT (t),β,T e (t))=β·N(D RT (t))·D VT (t) Where N(x) is the normalization function and σ is the standard deviation; The adjusting of the flight path of the drone includes: Based on the fused terrain data, the cloud computing platform dynamically adjusts the flight path to further optimize the UAV’s flight path; The adjusted flight path is fed back to the UAV navigation system through the communication module. The UAV continues to fly according to the new path and continues to collect and transmit data.

6. The new energy resource exploration method based on unmanned aerial vehicle according to claim 5, characterized in that: The mining of potential distribution patterns of new energy resources includes: Use big data analysis technology to conduct in-depth mining of processed new energy resource data, identify the potential distribution patterns of new energy resources, and analyze the distribution trends and hot spots of new energy resources; Extract important features from the data and reduce the data dimension through principal component analysis PCA; PolyDBSCAN is used to identify patterns and outliers in the data and determine the potential distribution areas of new energy resources; Kriging interpolation is used to analyze the distribution of new energy resources in geographic space, and spatiotemporal scanning statistics are used to analyze the temporal and spatial variation of resource distribution, identifying hot spots and variation trends; Use visualization tools to transform analysis results into intuitive information; The intuitive information includes resource distribution forecast maps and decision support information; The resource distribution prediction map includes a resource distribution map, a heat map and a trend analysis map; The decision support information includes exploration execution strategies, recommended exploration areas and action recommendations; Develop new energy resource exploration execution strategies based on the data and analysis results in the visual survey report.

7. The new energy resource exploration method based on unmanned aerial vehicle according to claim 6, characterized in that: The whole process monitoring of the UAV exploration process includes: Real-time monitoring of the drone’s battery level, signal strength, and equipment operating status; When low power, signal loss or equipment failure is detected, the alarm unit immediately triggers a warning and selects a network through the fusion communication unit to send the warning information to the remote terminal; A radio frequency unit is used to establish a communication connection between the drone and the cloud computing platform in a narrowband private network, and a security encryption unit is used to encrypt the communication process.

8. A new energy resource exploration system based on drones, based on the new energy resource exploration method based on drones according to any one of claims 1 to 7, characterized in that: Including model building module, resource exploration module, data fusion module and strategy formulation module; The model building module is used to collect virtual terrain resource data, build an optimal flight path model and train the optimal flight path model to obtain the current optimal flight path, and the UAV platform starts the UAV and executes the current optimal flight path; The resource exploration module is used to use RTK carrier phase differential positioning technology to perform on-site positioning and exploration, and transmit positioning data and real-time terrain resource data to the cloud computing platform; The data fusion module is used to fuse the real-time terrain resource data with the virtual terrain data to obtain fused terrain data, and adjust the flight path of the UAV according to the fused terrain data; The strategy formulation module is used to explore the potential distribution patterns of new energy resources through big data analysis, generate visual survey reports, and monitor the entire drone exploration process.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of any one of the drone-based new energy resource survey methods of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of any one of the new energy resource exploration methods based on drones according to claims 1 to 7 are implemented.

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