A blockchain drone telemetry data processing method

Abstract

The present invention relates to the technical field of geographic survey, and discloses a blockchain drone telemetry data processing method, including multi-point monitoring conditioning and collection of different telemetry object data, and constructing different target member block networks through multi-layer associated topological networks through different telemetry object data, and jointly constructing a surface three-dimensional model with different target member block networks; automatically obtaining other telemetry object data in the target member block network according to different target member block networks in the surface three-dimensional model, and parsing the change state of other telemetry object data at the previous moment and the current moment, designing a mathematical model with other telemetry object data guided by mathematical functions, and outputting the surface change state of the telemetry object. The present invention can infer and predict telemetry data, so as to know the surface conditions in advance.

Description

Technical Field

[0001] The present invention relates to the technical field of geographical surveying, and in particular to a blockchain drone telemetry data processing method. Background Art

[0002] With the development of drone technology, drones can now be used in a variety of scenarios to assist people in completing tasks such as regional security monitoring, geographic information collection, and environmental status data collection, thereby achieving the purpose of rapid collection and improving collection efficiency.

[0003] For example, the existing technology can carry an optoelectronic pod on a drone to capture telemetry data of the target area, such as regional monitoring pictures or regional monitoring videos of the target area. Among them, for some telemetry control point data in the current area, since the telemetry control point data is more important than other data, for example, a regional monitoring network can be built based on the telemetry control point data.

[0004] Therefore, in the related art, these telemetry control point data can be specially saved, such as saving them to a preset address, or encrypting them before saving; however, in the process of saving the telemetry control point data to a preset address, the operator needs to manually configure the saving address, resulting in low saving efficiency.

[0005] In order to solve the above technical problems, the company has its own authorized invention patent "A blockchain-based telemetry data processing method, system and cloud platform" on December 10, 2021. Through the above technical solution, the collected telemetry data can be processed to automatically produce a network of target member blocks, so that the coordinate information of each location can be automatically extracted within the network of the target member block. However, during use, the company found that the telemetry data obtained during the drone survey is prone to the following technical problems:

[0006] 1. Since the telemetry data processing method used by our company collects telemetry data through a single drone, it is easy to have inaccurate data. Secondly, the telemetry data collected by our company's drones in the past is stored in the drone's SD card, and the SD card is taken out and the telemetry data is extracted after the drone returns. This makes it impossible to receive telemetry data in real time. Therefore, our company uses wireless signals to obtain telemetry data collected by drones in real time. However, due to the remote survey environment, it is easy to have weak telemetry data transmission signals, and it is still easy to fail to receive telemetry data in real time.

[0007] 2. Since the telemetry data processing method used by our company cannot fully reveal the laws of spatial surface structural changes, it is difficult to directly understand and feel the three-dimensional underground surface conditions. For example, it is difficult to predict surface disasters. Since the telemetry data processing method cannot infer and predict telemetry data, it is impossible to predict possible address disasters in real time in advance, and the scope of use is limited.

[0008] Therefore, in order to solve the above technical problems, our company has developed a blockchain drone telemetry data processing method based on the previously developed blockchain telemetry data processing method. On this basis, it can receive telemetry data in real time, infer and predict telemetry data, and thus obtain surface conditions in advance. Summary of the invention

[0009] The present invention aims to provide a blockchain UAV telemetry processing data method that can receive telemetry data in real time, and can infer and predict telemetry data, so as to know the surface conditions in advance.

[0010] In order to achieve the above object, the present invention adopts the following technical scheme:

[0011] 1) A blockchain drone telemetry data processing method, comprising:

[0012] The data of different telemetry objects are collected and conditioned by multi-point monitoring, and different target member block networks are constructed through multi-layer associated topological networks through different telemetry object data, and different target member block networks are jointly constructed into a three-dimensional surface model;

[0013] According to the different target member block networks in the three-dimensional surface model, the other telemetry object data in the target member block network are automatically obtained, and the change status of other telemetry object data between the previous moment and the current moment is analyzed. The mathematical model of other telemetry object data is designed with mathematical functions as the guide, and the surface change status of the telemetry object is output externally.

[0014] The present invention uses multi-point monitoring to adjust and collect different telemetry object data, thereby ensuring the accuracy of different telemetry object data; each telemetry object data is constructed through a multi-layer associated topological network to build a target member block network belonging to the telemetry object data. This technology is recorded in the invention patent "A blockchain-based telemetry data processing method, system and cloud platform" authorized by our company. Based on the invention patent technology, our company has developed a three-dimensional surface model jointly constructed by different target member block networks, so that we can directly understand and feel the three-dimensional surface conditions underground.

[0015] Based on the three-dimensional surface model, other telemetry object data in different target member block networks are automatically obtained. Combined with the telemetry object data collected through multi-point monitoring, the change status of the telemetry object data between the previous moment and the current moment is analyzed, and a mathematical model is designed based on mathematical functions to fully obtain the spatial surface structure change law of the telemetry object data. After different telemetry object data are input into the mathematical model, the surface change status of the telemetry object is output to the outside, which can infer and predict the telemetry data, thereby knowing the surface conditions in advance.

[0016] 2) A blockchain drone telemetry data processing method according to 1), wherein multi-point monitoring, conditioning and collection of different telemetry object data include:

[0017] Remote sensing mapping using drones to automatically collect data on remotely sensed objects using optical cameras, LiDAR and multispectral cameras;

[0018] Satellite and UAV integrated remote sensing mapping, using satellite to accurately calibrate the UAV remote sensing object data;

[0019] Integrated remote sensing mapping with ground cameras and drones, using ground cameras to collect remote sensing object data in the area and supplementing the drone remote sensing object data;

[0020] When a drone or ground camera transmits telemetry object data wirelessly, a signal center station is designed in the area, and the signal center station serves as a signal reinforcement point and is used to increase the wireless transmission signal of the drone and ground camera.

[0021] In order to solve the problem of weak telemetry data transmission signals in the prior art, the present invention builds a signal center station in the area of ​​a drone or a ground camera. When the telemetry data transmission signal is weak, the drone or the ground camera transmits the collected telemetry object data to the signal center station. The wireless transmission signal of the signal center station is strengthened to enhance the wireless signal of the telemetry object data, thereby ensuring that the drone and the ground camera can receive the telemetry data in real time, and ensuring the real-time and accurate return of the telemetry object data.

[0022] In addition, in the measurement practice, the present invention uses UAV remote sensing mapping and integrates it with satellite and ground camera remote sensing mapping to form a benign complement and fill the shortcomings of a single technology; at the same time, the compatibility of UAV remote sensing itself also creates conditions for this integration, which can ensure the actual application effect and maximize the value of UAV remote sensing.

[0023] 3) A blockchain drone telemetry data processing method according to 2), wherein:

[0024] The UAV remote sensing mapping uses a low flying altitude combined with precise positioning to adjust the mapping scale height, so as to obtain high-definition mapping image data, and the mapping image data is the remote sensing object data.

[0025] The present invention is highly advanced in the acquisition of surveying and mapping images for UAV remote sensing mapping. In the process of UAV remote sensing mapping, the high adjustment of the surveying and mapping scale is achieved through low flight altitude combined with precise positioning, which means that more detailed and high-resolution surveying and mapping image data can be obtained, effectively improving the surveying and mapping accuracy, and ensuring that the UAV surveying and mapping data meets the requirements of high scales (such as 1:500, 1:1000, etc.).

[0026] Among them, low flight altitude is one of the key factors that determine the resolution of remote sensing data. The lower the flight altitude, the clearer the ground details captured by the camera or sensor, thus enabling larger-scale mapping. For example, with the same camera equipment, lowering the flight altitude will increase the resolution of ground images and obtain more detailed geographic information. Here, combined with the high-resolution sensing equipment of optical cameras, LiDAR and multispectral cameras carried by drones, data resolution can be significantly improved and the scale of mapping can be enlarged.

[0027] At the same time, by using the forward overlap and lateral overlap between surveying and mapping images, more ground details can be captured from different perspectives, improving image clarity and measurement accuracy. By enhancing the redundancy of data collection, a more sophisticated model can be generated, magnifying the scale effect. Among them, the forward overlap is usually set at 70%-80%, which can ensure that the drone captures continuous, high-resolution ground images during flight; and the lateral overlap is set at 60%-70% or higher, ensuring that the drone can cover every corner of the surveying and mapping area to avoid omissions.

[0028] In addition, the present invention combines precise positioning on the basis of low flight altitude, and precise positioning uses RTK / PPK technology. In UAV surveying and mapping, the use of RTK (real-time dynamic differential technology) or PPK (post-processing differential technology) can greatly improve positioning accuracy, making surveying and mapping data more accurate. This can eliminate many geographical errors and achieve high-scale surveying and mapping. Its characteristics are as follows: RTK corrects the UAV GPS signal in real time through the ground base station, which can improve the surveying and mapping accuracy to the centimeter level, thereby achieving accurate measurement of 1:500 or even higher scales. PPK is similar to RTK, but the processing of differential data is carried out after the flight mission is completed, which is suitable for situations where the flight environment is more complex.

[0029] 4) A blockchain UAV telemetry data processing method according to 2), wherein satellite and UAV integrated remote sensing mapping, including coverage integration, temporal resolution integration and data fusion integration, wherein coverage integration is achieved by overlapping the global range of satellite remote sensing with the regional range of UAV remote sensing; temporal resolution integration is achieved by making the satellite remote sensing observation frequency less than the UAV remote sensing observation frequency, so that the data of the UAV remote sensing observation frequency supplements the data of the satellite observation frequency; data fusion integration is achieved by combining the large-scale data of satellite remote sensing with the high-resolution data of UAV and performing multi-scale analysis.

[0030] Coverage integration in the present invention: Satellite remote sensing has the advantage of large-scale coverage and is suitable for large-scale monitoring tasks, such as global climate change monitoring, national land use surveys, etc. UAV remote sensing has the advantages of high resolution and local coverage, and is suitable for detailed mapping and small-area monitoring.

[0031] Temporal resolution integration: Satellite remote sensing observation frequency is relatively low (for example, some satellites only cover the same area every day or every few days), and drones can flexibly supplement data and provide real-time updates in emergency situations.

[0032] Data fusion and integration: Combining large-scale satellite remote sensing data with high-resolution drone data allows for multi-scale analysis. For example, satellite data can be used to monitor large-scale surface changes at a macro level, while drones can be used to conduct detailed mapping and verification in local key areas.

[0033] 5) A blockchain UAV telemetry data processing method according to 2), wherein the remote sensing mapping integrated with the ground camera and the UAV includes precision supplement integration and data verification integration, wherein the precision supplement integration realizes the precision calibration of the UAV data by using the ground control points through the ground camera, and the data verification integration realizes the verification and supplement of the UAV data by using the ground camera to measure the key areas in detail.

[0034] Accuracy supplementary integration in the present invention: UAV remote sensing mapping can achieve high accuracy through technologies such as GPS / RTK, but in some cases where the accuracy requirements are extremely high, ground control points (GCP) are still required for accuracy calibration. Combining traditional ground measurement technologies (such as total stations, GPS RTK) can improve the accuracy of UAV mapping, especially when generating three-dimensional models.

[0035] Data verification and integration: The data collected by drones can be verified and supplemented with ground measurement data to ensure data reliability and consistency. Ground measurement technology is usually used for detailed measurements of key areas, while drones are used for rapid coverage of large areas.

[0036] 6) A blockchain drone telemetry data processing method according to 1), wherein different telemetry object data are respectively used to construct different target member block networks through multi-layer associated topological networks, and different target member block networks are jointly used to construct a three-dimensional surface model, including the following steps:

[0037] S1. parse different telemetry object data to obtain telemetry object data of a first associated topology network included in the current UAV telemetry data, where the first associated topology network is a multi-layer associated topology network used to simulate various telemetry targets in a target area;

[0038] S2, filtering the telemetry object data in the first association topology network by key objects to generate telemetry control point data of the second association topology network, where the second association topology network is a subnetwork of the first association topology network in the multi-layer association topology network;

[0039] S3, determining the target member block network corresponding to the telemetry object data according to the telemetry control point data of the second associated topology network, wherein the member devices in the target member block network correspond one-to-one to the telemetry targets in the sensitive telemetry target queue, and each member device corresponds to a telemetry target in the sensitive telemetry target queue;

[0040] S4. Using the target member block networks corresponding to different telemetry object data obtained in the above steps S1-S3, different target member block networks are combined to construct a three-dimensional surface model of the area.

[0041] The present invention saves the telemetry control point data through the target member block network, automatically generates the target member block network, and refines the target member block network corresponding to different telemetry object data to construct a three-dimensional surface model of the area. It can directly understand and feel the underground three-dimensional surface conditions through the three-dimensional surface model, and realize the maturity of the company's telemetry data processing technology to realize the automatic generation of three-dimensional models.

[0042] 7) A blockchain drone telemetry data processing method according to 1), wherein other telemetry object data in the target member block network is automatically obtained according to different target member block networks in the three-dimensional surface model, including:

[0043] According to the three-dimensional surface model, the telemetry targets in the sensitive telemetry target queue are obtained through the member devices of different target member block networks. The telemetry targets are other telemetry object data in the target member area network and are automatically saved.

[0044] 8) A blockchain drone telemetry data processing method according to 1), wherein:

[0045] The mathematical function includes a linear function and a curve function. The data model is designed with the linear function as a guide so that the automatically obtained telemetry object data automatically predicts the telemetry data generated in the continuous time; the data model is designed with the curve function as a guide so that the automatically obtained telemetry object data automatically predicts the telemetry data generated in the continuous time.

[0046] The mathematical functions in the present invention are not limited to linear functions and curve functions. Different mathematical functions can be used to automatically predict the telemetry data generated in a continuous time based on the regularity of the telemetry data monitored by the telemetry object data over a period of time, thereby knowing the surface changes of the telemetry object data in advance.

[0047] 9) A blockchain drone telemetry data processing method according to 8), wherein:

[0048] The mathematical model of other telemetry object data is designed based on linear functions, including the following steps:

[0049] S1, recording the coordinate position of other remote sensing object data in the three-dimensional model of the earth's surface at the previous moment and the coordinate position at the current moment, performing phase difference calculation between the coordinate position of the remote sensing object data at the previous moment and the current coordinate position to obtain the moving coordinate data of the remote sensing object data and save the data;

[0050] S2, taking a period of time as a reference, repeatedly obtaining the moving coordinate data with the same time difference in step S1, and taking the moving coordinate data as the surface change value;

[0051] S3. Based on step S2, assume that the coordinates of the telemetry object data at the current moment are (x, y, z), the coordinates at the previous moment are (x1, y1, z1), and the moving coordinate data are [(x-x1), (y-y1), (z-z1)]; perform linear changes on the telemetry object data at continuous time t, and the calculation formula is:

[0052] Y=[(x-x1), (y-y1), (z-z1)]t

[0053] Where:

[0054] Y is the coordinate data for inferring and predicting the movement of the telemetry object data in continuous time t.

[0055] The present invention realizes the mature telemetry object data processing technology through the three-dimensional surface model, realizes the automatic acquisition of telemetry object data, obtains the change law of telemetry object data through the three-dimensional surface model, and designs a mathematical model with a linear function for the change law of telemetry object data. The mathematical model is used to process and calculate the telemetry object data of continuous time to infer and predict the telemetry data.

[0056] 10) A blockchain drone telemetry data processing method according to 8), wherein:

[0057] The mathematical model of other telemetry object data is designed based on the curve function, including the following steps:

[0058] S1, recording the coordinate position of other remote sensing object data in the three-dimensional model of the earth's surface at the previous moment and the coordinate position at the current moment, performing phase difference calculation between the coordinate position of the remote sensing object data at the previous moment and the current coordinate position to obtain the moving coordinate data of the remote sensing object data and save the data;

[0059] S2, taking a period of time as a reference, repeatedly obtaining the moving coordinate data with the same time difference in step S1, and taking the moving coordinate data as the surface change value;

[0060] S3. Based on step S2, assume that the coordinates of the telemetry object data at the current moment are (x, y, z), the coordinates at the previous moment are (x1, y1, z1), and the moving coordinate data are [(x-x1), (y-y1), (z-z1)]; perform curve changes on the telemetry object data at continuous time t, and the calculation formula is:

[0061] Y=sin[(x-x1), (y-y1), (z-z1)]

[0062] Where:

[0063] Y is the coordinate data for inferring and predicting the movement of the telemetry object data in continuous time t.

[0064] The present invention realizes the mature telemetry object data processing technology through the three-dimensional surface model, realizes the automatic acquisition of telemetry object data, obtains the telemetry object data change law through the three-dimensional surface model, and designs a mathematical model with a curve function for the telemetry object data change law. The mathematical model is used to process and calculate the telemetry object data of continuous time to infer and predict the telemetry data, and to perform real-time detection of surface landslides, crust deformation and settlement, etc., to provide timely and reliable technical data support for the development of surface engineering projects.

[0065] Compared with the prior art, the present invention also has the following technical effects:

[0066] 1. The present invention achieves the following technical effects through UAV remote sensing mapping, satellite and UAV integrated remote sensing mapping, and ground camera and UAV integrated remote sensing mapping:

[0067] 1) The information processing is remarkable. When the target area is monitored by UAV remote sensing, it has a good information processing speed and a high resolution, which to a large extent guarantees the effect of information collection. We only need 15 minutes to complete the measurement within 20 kilometers.

[0068] 2) The monitoring scale is large. Based on the UAV remote sensing technology, the specific situation of the target area can be displayed in three dimensions, which is conducive to improving the intuitiveness. The operating altitude range is relatively wide, which will be controlled between 100m-750m. Its flight altitude can reach about 75km, with a resolution of 5cm, a typical cruising speed of 2-8km / h, an overlap rate greater than 5, and a measurement can be completed in about 30 minutes.

[0069] 3) High monitoring efficiency. The monitoring efficiency of remote sensing technology itself is relatively ideal. Once an emergency occurs, emergency treatment through drone remote sensing can greatly improve the processing efficiency, comprehensively optimize the processing effect, and effectively avoid adverse situations. In short, in engineering surveying and mapping practice, drone remote sensing can effectively guarantee measurement efficiency.

[0070] 4) Excellent system compatibility. In measurement practice, remote sensing mapping technology can be integrated with other remote sensing systems to form a benign complement and fill the gaps in a single technology. At the same time, the compatibility of drone remote sensing itself also creates conditions for this integration, which can ensure the actual application effect and maximize the value of drone remote sensing.

[0071] 2. The present invention carries out precise mapping of the surface environment around surface engineering. Generally, the main measurement object of surface mapping work refers to the rock cover around fixed engineering projects. With the help of telemetry technology, the surrounding rock cover and rock layer distribution can be accurately mapped. The final mapping results can provide reliable data information for surface workers, help them fully grasp the surrounding surface information, provide comprehensive and reliable data support for the formulation of surface engineering plans, and help surface workers design more scientific and safe engineering plans.

[0072] At the same time, in addition to the need to make accurate measurements of the surrounding rock information, the understanding of the surface structure is also essential in surface surveying and mapping work, especially for construction projects. The surface conditions at the construction site will not only affect the progress of the project, but also have a great impact on the quality of the project. With the help of telemetry technology, multi-spectral, multi-resolution and multi-temporal satellite images are used as surveying and mapping data, the changes in the surface and the surface conditions in different regions can be accurately measured, and even the precise surface deformation of a range, such as landslides, crustal deformation and subsidence, can be detected in real time, providing timely and reliable technical data support for the development of surface engineering projects.

[0073] 3. According to incomplete statistics from the Ministry of Land and Resources, the main reasons for the loss of hundreds of billions of yuan each year are collapse, landslide, mud-rock flow disasters, followed by surface disasters such as soil erosion, land desertification, salinization, and breccia; in addition to natural surface disasters, man-made disasters, such as ground subsidence caused by pumping in many cities in the plains of the country, have also caused huge economic losses. Therefore, the present invention monitors the areas where surface problems occur in real time, collects and analyzes data during the surface change process, and completely and in detail records the specific circumstances of the entire disaster process, shortening the time for search and rescue teams to save people when disasters occur, and improving the survival rate of disaster victims.

[0074] In addition, real-time surface monitoring can predict the occurrence of surface disasters in advance and provide sufficient evacuation opportunities and evacuation routes for people in areas where surface disasters are likely to occur. This also reflects from the side that relevant surface problems cannot be separated from the support and application of remote sensing mapping technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0075] Figure 1 This is a flow chart of a blockchain drone telemetry data processing method of the present invention;

[0076] Figure 2 This is a flow chart of constructing a three-dimensional surface model in a blockchain UAV telemetry processing data method of the present invention. DETAILED DESCRIPTION

[0077] The following is further described in detail through specific implementation methods:

[0078] Reference will now be made in detail to embodiments disclosed herein, examples of which are described herein and illustrated in the accompanying drawings. Although the present disclosure will be described in conjunction with embodiments and / or examples, they do not limit the present disclosure to these embodiments and / or examples. On the contrary, the present disclosure covers alternatives, modifications and equivalents. Example

[0079] See also Figure 1 As shown, a blockchain drone telemetry data processing method in this embodiment includes:

[0080] The data of different telemetry objects are collected and conditioned by multi-point monitoring, and different target member block networks are constructed through multi-layer associated topological networks through different telemetry object data, and different target member block networks are jointly constructed into a three-dimensional surface model;

[0081] According to the different target member block networks in the three-dimensional surface model, the other telemetry object data in the target member block network are automatically obtained, and the change status of other telemetry object data between the previous moment and the current moment is analyzed. The mathematical model of other telemetry object data is designed with mathematical functions as the guide, and the surface change status of the telemetry object is output externally.

[0082] Based on the three-dimensional surface model, other telemetry object data in different target member block networks are automatically obtained. Combined with the telemetry object data collected through multi-point monitoring, conditioning and collection, the change status of the telemetry object data between the previous moment and the current moment is analyzed, and a mathematical model is designed based on mathematical functions, so as to fully obtain the spatial surface structure change law of the telemetry object data. After different telemetry object data are input into the mathematical model, the surface change status of the telemetry object is output to the outside, which can infer and predict the telemetry data, so as to know the surface conditions in advance.

[0083] Among them, mathematical functions include linear functions and curve functions. The data model is designed based on the linear function, so that the automatically obtained telemetry object data can automatically predict the telemetry data generated in the continuous time; the data model is designed based on the curve function, so that the automatically obtained telemetry object data can automatically predict the telemetry data generated in the continuous time.

[0084] In this embodiment, the mathematical function is not limited to linear functions and curve functions. According to the regularity of the telemetry object data monitored over a period of time, different mathematical functions can be used to automatically predict the telemetry data generated in a continuous time based on the automatically obtained telemetry object data, so as to know the surface changes of the telemetry object data in advance.

[0085] The mathematical model of other telemetry object data is designed based on linear functions, including the following steps:

[0086] S1, recording the coordinate position of other remote sensing object data in the three-dimensional model of the earth's surface at the previous moment and the coordinate position at the current moment, performing phase difference calculation between the coordinate position of the remote sensing object data at the previous moment and the current coordinate position to obtain the moving coordinate data of the remote sensing object data and save the data;

[0087] S2, taking a period of time as a reference, repeatedly obtaining the moving coordinate data with the same time difference in step S1, and taking the moving coordinate data as the surface change value;

[0088] S3. Based on step S2, assume that the coordinates of the telemetry object data at the current moment are (x, y, z), the coordinates at the previous moment are (x1, y1, z1), and the moving coordinate data are [(x-x1), (y-y1), (z-z1)]; perform linear changes on the telemetry object data at continuous time t, and the calculation formula is:

[0089] Y=[(x-x1), (y-y1), (z-z1)]t

[0090] Where:

[0091] Y is the coordinate data for inferring and predicting the movement of the telemetry object data in continuous time t.

[0092] Through the three-dimensional surface model, the mature telemetry object data processing technology is realized to realize the automatic acquisition of telemetry object data, and the change law of telemetry object data is obtained through the three-dimensional surface model. The mathematical model is designed with the linear function of the change law of telemetry object data, and the mathematical model is used to process and calculate the telemetry object data in continuous time to infer and predict the telemetry data.

[0093] In another embodiment, the other telemetry object data is designed with the curve function as the guide mathematical model, including the following steps:

[0094] S1, recording the coordinate position of other remote sensing object data in the three-dimensional model of the earth's surface at the previous moment and the coordinate position at the current moment, performing phase difference calculation between the coordinate position of the remote sensing object data at the previous moment and the current coordinate position to obtain the moving coordinate data of the remote sensing object data and save the data;

[0095] S2, taking a period of time as a reference, repeatedly obtaining the moving coordinate data with the same time difference in step S1, and taking the moving coordinate data as the surface change value;

[0096] S3. Based on step S2, assume that the coordinates of the telemetry object data at the current moment are (x, y, z), the coordinates at the previous moment are (x1, y1, z1), and the moving coordinate data are [(x-x1), (y-y1), (z-z1)]; perform curve changes on the telemetry object data at continuous time t, and the calculation formula is:

[0097] Y=sin[(x-x1), (y-y1), (z-z1)]

[0098] Where:

[0099] Y is the coordinate data for inferring and predicting the movement of the telemetry object data in continuous time t.

[0100] Through the three-dimensional surface model, the mature telemetry object data processing technology is realized to realize the automatic acquisition of telemetry object data, and the change law of telemetry object data is obtained through the three-dimensional surface model. The mathematical model is designed with the change law of telemetry object data as a curve function, and the continuous time telemetry object data is processed and calculated by the mathematical model to infer and predict the telemetry data, and the surface landslides, crust deformation and subsidence are detected in real time, providing timely and reliable technical data support for the development of surface engineering projects.

[0101] In addition, in this embodiment, multi-point monitoring, conditioning and collection of different remote sensing object data include drone remote sensing mapping, satellite and drone integrated remote sensing mapping and ground camera and drone integrated remote sensing mapping, among which:

[0102] Remote sensing mapping using drones to automatically collect data on remotely sensed objects using optical cameras, LiDAR and multispectral cameras;

[0103] Satellite and UAV integrated remote sensing mapping, using satellite to accurately calibrate the UAV remote sensing object data;

[0104] Integrated remote sensing mapping with ground cameras and drones, using ground cameras to collect remote sensing object data in the area and supplementing the drone remote sensing object data;

[0105] When a drone or ground camera transmits telemetry object data wirelessly, a signal center station is designed in the area, and the signal center station serves as a signal reinforcement point and is used to increase the wireless transmission signal of the drone and ground camera.

[0106] In order to solve the problem of weak telemetry data transmission signals in the prior art, this embodiment builds a signal center station in the area of ​​the drone or ground camera. When the telemetry data transmission signal is weak, the drone or ground camera transmits the collected telemetry object data to the signal center station. The wireless transmission signal of the signal center station is strengthened to enhance the wireless signal of the telemetry object data, thereby ensuring that the drone and the ground camera can receive the telemetry data in real time and ensure the real-time and accurate return of the telemetry object data.

[0107] In addition, in the measurement practice, the present invention uses UAV remote sensing mapping and integrates it with satellite and ground camera remote sensing mapping to form a benign complement and fill the shortcomings of a single technology; at the same time, the compatibility of UAV remote sensing itself also creates conditions for this integration, which can ensure the actual application effect and maximize the value of UAV remote sensing.

[0108] Drone remote sensing mapping is a technology that uses drones equipped with various sensors to conduct high-precision mapping of the earth's surface. It is usually used to generate two-dimensional maps, three-dimensional terrain models, surface feature analysis, etc. The advantages of drone remote sensing mapping are its flexibility, low cost, and high resolution, which are suitable for small and medium-scale mapping tasks. Drone remote sensing can also be integrated with other remote sensing systems (such as satellite and manned aircraft remote sensing) to obtain more comprehensive and accurate data.

[0109] Before the drone begins remote sensing mapping, the operator first plans the mission through the ground station software to determine the scope of the mapping area, flight altitude, flight path, overlap (forward and lateral overlap), etc. Reasonable planning of the flight path ensures that the drone can cover the entire target area and collect images with the best resolution.

[0110] Drone takeoff and data collection: Drones fly according to the planned route, and the sensors they carry (such as optical cameras, LiDAR, multispectral cameras, etc.) automatically collect data. Optical cameras can take high-resolution images, LiDAR generates high-precision three-dimensional point cloud data through laser ranging, and multispectral cameras can capture different spectral information.

[0111] Data processing: After data collection is completed, the image or point cloud data is transmitted to the ground station or background for processing:

[0112] Image stitching: stitch multiple captured images together to form a complete two-dimensional image (orthophoto).

[0113] 3D modeling: Generate digital elevation models (DEM) or digital surface models (DSM) using multi-view images or LiDAR data.

[0114] Coordinate calibration: Use geographic positioning information (GPS / RTK) to accurately calibrate surveying and mapping data to ensure the accuracy of geographic information.

[0115] Data analysis and output: The processed data can be used for analysis, such as generating high-precision terrain maps, building models, vegetation coverage maps, etc. The final output results may include two-dimensional images, three-dimensional terrain models, contour maps, etc., which are applied to urban planning, environmental monitoring, engineering construction and other fields.

[0116] In more detail, in this embodiment, the UAV remote sensing mapping uses a low flight altitude combined with precise positioning to adjust the mapping scale height to obtain high-definition mapping image data, and the mapping image data is the remote sensing object data.

[0117] The acquisition of surveying and mapping images by UAV remote sensing mapping is very advanced. In the process of UAV remote sensing mapping, the high adjustment of the surveying and mapping scale is achieved through low flight altitude combined with precise positioning, which means that more detailed and high-resolution surveying and mapping image data can be obtained, effectively improving the surveying and mapping accuracy and ensuring that the UAV surveying and mapping data meets the requirements of high scales (such as 1:500, 1:1000, etc.).

[0118] Among them, low flight altitude is one of the key factors that determine the resolution of remote sensing data. The lower the flight altitude, the clearer the ground details captured by the camera or sensor, thus enabling larger-scale mapping. For example, with the same camera equipment, lowering the flight altitude will increase the resolution of ground images and obtain more detailed geographic information. Here, combined with the high-resolution sensing equipment of optical cameras, LiDAR and multispectral cameras carried by drones, data resolution can be significantly improved and the scale of mapping can be enlarged.

[0119] At the same time, by using the forward overlap and lateral overlap between surveying and mapping images, more ground details can be captured from different perspectives, improving image clarity and measurement accuracy. By enhancing the redundancy of data collection, a more sophisticated model can be generated, magnifying the scale effect. Among them, the forward overlap is usually set at 70%-80%, which can ensure that the drone captures continuous, high-resolution ground images during flight; and the lateral overlap is set at 60%-70% or higher, ensuring that the drone can cover every corner of the surveying and mapping area to avoid omissions.

[0120] In addition, the present invention combines precise positioning on the basis of low flight altitude, and precise positioning uses RTK / PPK technology. In UAV surveying and mapping, the use of RTK (real-time dynamic differential technology) or PPK (post-processing differential technology) can greatly improve positioning accuracy, making surveying and mapping data more accurate. This can eliminate many geographical errors and achieve high-scale surveying and mapping. Its characteristics are as follows: RTK corrects the UAV GPS signal in real time through the ground base station, which can improve the surveying and mapping accuracy to the centimeter level, thereby achieving accurate measurement of 1:500 or even higher scales. PPK is similar to RTK, but the processing of differential data is carried out after the flight mission is completed, which is suitable for situations where the flight environment is more complex.

[0121] In this embodiment, satellite and UAV integrated remote sensing mapping includes coverage integration, time resolution integration and data fusion integration, wherein coverage integration is achieved by overlapping the global range of satellite remote sensing with the regional range of UAV remote sensing; time resolution integration is achieved by making the satellite remote sensing observation frequency less than the UAV remote sensing observation frequency, so that the data of the UAV remote sensing observation frequency supplements the data of the satellite observation frequency; data fusion integration is achieved by combining the large-scale data of satellite remote sensing with the high-resolution data of UAV and performing multi-scale analysis.

[0122] Coverage integration in the present invention: Satellite remote sensing has the advantage of large-scale coverage and is suitable for large-scale monitoring tasks, such as global climate change monitoring, national land use surveys, etc. UAV remote sensing has the advantages of high resolution and local coverage, and is suitable for detailed mapping and small-area monitoring.

[0123] Temporal resolution integration: Satellite remote sensing observation frequency is relatively low (for example, some satellites only cover the same area every day or every few days), and drones can flexibly supplement data and provide real-time updates in emergency situations.

[0124] Data fusion and integration: Combining large-scale satellite remote sensing data with high-resolution drone data allows for multi-scale analysis. For example, satellite data can be used to monitor large-scale surface changes at a macro level, while drones can be used to conduct detailed mapping and verification in local key areas.

[0125] In this embodiment, the remote sensing mapping integrated with the ground camera and the UAV includes precision supplement integration and data verification integration, wherein the precision supplement integration realizes the precision calibration of the UAV data by using the ground control points through the ground camera, and the data verification integration realizes the verification and supplement of the UAV data by using the ground camera to perform fine measurements of key areas.

[0126] Accuracy supplementary integration in the present invention: UAV remote sensing mapping can achieve high accuracy through technologies such as GPS / RTK, but in some cases where the accuracy requirements are extremely high, ground control points (GCP) are still required for accuracy calibration. Combining traditional ground measurement technologies (such as total stations, GPS RTK) can improve the accuracy of UAV mapping, especially when generating three-dimensional models.

[0127] Data verification and integration: The data collected by drones can be verified and supplemented with ground measurement data to ensure data reliability and consistency. Ground measurement technology is usually used for detailed measurements of key areas, while drones are used for rapid coverage of large areas.

[0128] In addition, when the UAV remote sensing surveying and mapping occurs, emergency processing can be carried out once an emergency occurs, thereby improving processing efficiency, comprehensively optimizing processing effects, and effectively avoiding adverse situations; therefore, in this embodiment, when an emergency occurs during the surveying and mapping work of the UAV remote sensing, there are several ways to carry out emergency processing:

[0129] 1. Immediately start the automatic return function: When an emergency occurs, the automatic return function of the drone can be immediately started. This will be automatically triggered by the flight control system or manually started by the operator to ensure that the drone returns to a safe area.

[0130] 2. Manually control the drone to avoid obstacles and land: If the automatic return function fails, the operator should immediately switch to manual control mode, manually control the drone to avoid obstacles, and choose a safe landing area to avoid further damage.

[0131] 3. Real-time transmission data backup: In the event of an emergency, try to transmit the current remote sensing data to the ground station in real time through wireless data transmission technology (such as 4G / 5G) to avoid data loss when the drone is damaged. If the connection is interrupted, ensure that the local data has redundant backup.

[0132] 4. Detect and avoid external environmental factors: The sensors equipped by drones (such as anemometers, GPS, etc.) can monitor environmental changes in real time. In the face of emergencies such as extreme weather, strong winds, and strong magnetic interference, the flight control system should actively adjust the flight altitude, speed, or route to avoid risk areas. If necessary, the mission should be terminated and the drone should return or land.

[0133] 5. Enable backup sensors and navigation systems: If a sensor or navigation system of the drone fails (such as GPS signal loss or camera failure), you can enable backup sensors or systems. For example, enable the inertial navigation system (INS) for a short period of GPS-free flight, or switch to other functional sensors to continue to complete some data collection.

[0134] 6. Emergency Landing Procedure: When the drone cannot be controlled or there is a risk of running out of power, the operator can initiate the emergency landing procedure and choose a safer location to land to minimize losses. In high-risk areas, it is recommended to land in a predetermined backup safety zone.

[0135] 7. Subsequent analysis and preventive measures: After the incident, the status of the drone and remote sensing equipment needs to be checked in detail, and the cause of the incident needs to be analyzed (such as equipment failure, weather factors or operational problems). At the same time, the flight plan, equipment maintenance process and emergency plan need to be optimized to prevent similar incidents from happening again.

[0136] Combined with blockchain drone mapping technology, data can be encrypted, stored and transmitted in real time on the blockchain, ensuring data security and non-tamperability even in emergencies, ensuring subsequent work continuity and data integrity.

[0137] Example 2

[0138] See also Figure 2 As shown, based on Example 1, this embodiment constructs different target member block networks through multi-layer associated topological networks through different telemetry object data, and jointly constructs a three-dimensional surface model with different target member block networks, including the following steps:

[0139] S1. parse different telemetry object data to obtain telemetry object data of a first associated topology network included in the current UAV telemetry data, where the first associated topology network is a multi-layer associated topology network used to simulate various telemetry targets in a target area;

[0140] S2, filtering the telemetry object data in the first association topology network by key objects to generate telemetry control point data of the second association topology network, where the second association topology network is a subnetwork of the first association topology network in the multi-layer association topology network;

[0141] S3, determining the target member block network corresponding to the telemetry object data according to the telemetry control point data of the second associated topology network, wherein the member devices in the target member block network correspond one-to-one to the telemetry targets in the sensitive telemetry target queue, and each member device corresponds to a telemetry target in the sensitive telemetry target queue;

[0142] S4. Using the target member block networks corresponding to different telemetry object data obtained in the above steps S1-S3, different target member block networks are combined to construct a three-dimensional surface model of the area.

[0143] Through multi-point monitoring, different telemetry object data are collected and adjusted to ensure the accuracy of different telemetry object data; each telemetry object data is constructed through a multi-layer associated topological network to form a target member block network belonging to the telemetry object data. This technology is recorded in the invention patent "A blockchain-based telemetry data processing method, system and cloud platform" authorized by our company. Based on the invention patent technology, our company has developed a three-dimensional surface model jointly constructed by different target member block networks, and the telemetry control point data is saved through the target member block network, and the target member block network is automatically generated. The target member block networks corresponding to different telemetry object data are further combined to construct a three-dimensional surface model of the area, which can directly understand and feel the underground three-dimensional surface conditions through the three-dimensional surface model, and realize the maturity of the company's telemetry data processing technology to achieve automatic generation of three-dimensional models.

[0144] Based on Example 1, this embodiment automatically obtains other telemetry object data in the target member block network according to different target member block networks in the three-dimensional surface model, including:

[0145] According to the three-dimensional surface model, the telemetry targets in the sensitive telemetry target queue are obtained through the member devices of different target member block networks. The telemetry targets are other telemetry object data in the target member area network and are automatically saved.

[0146] This embodiment carries out precise mapping of the surface environment around the surface engineering. Generally, the main measurement object of surface mapping work refers to the rock cover around the fixed engineering project. With the help of telemetry technology, the surrounding rock cover and rock layer distribution can be accurately mapped. The final mapping results can provide reliable data information for surface workers, help them fully grasp the surrounding surface information, provide comprehensive and reliable data support for the formulation of surface engineering plans, and help surface workers design more scientific and safe engineering plans.

[0147] At the same time, in addition to the need to make accurate measurements of the surrounding rock information, the understanding of the surface structure is also essential in surface surveying and mapping work, especially for construction projects. The surface conditions at the construction site will not only affect the progress of the project, but also have a great impact on the quality of the project. With the help of telemetry technology, multi-spectral, multi-resolution and multi-temporal satellite images are used as surveying and mapping data, the changes in the surface and the surface conditions in different regions can be accurately measured, and even the precise surface deformation of a range, such as landslides, crustal deformation and subsidence, can be detected in real time, providing timely and reliable technical data support for the development of surface engineering projects.

[0148] The above are only embodiments of the present invention, and the common knowledge such as the known specific technical solutions and / or characteristics in the solution are not described in detail here. It should be pointed out that for those skilled in the art, several modifications and improvements can be made without departing from the technical solution of the present invention, which should also be regarded as the protection scope of the present invention, and these will not affect the effect of the implementation of the present invention and the practicality of the patent. The scope of protection required by this application shall be based on the content of its claims, and the specific implementation methods and other records in the specification can be used to interpret the content of the claims.

Claims

1. A blockchain drone telemetry data processing method, comprising: The data of different telemetry objects are collected and conditioned by multi-point monitoring, and different target member block networks are constructed through multi-layer associated topological networks through different telemetry object data, and different target member block networks are jointly constructed into a three-dimensional surface model; Among them, multi-point monitoring and conditioning and collection of different telemetry object data include: Remote sensing mapping using drones to automatically collect data on remotely sensed objects using optical cameras, LiDAR and multispectral cameras; Satellite and UAV integrated remote sensing mapping, using satellite to accurately calibrate the UAV remote sensing object data; Integrated remote sensing mapping with ground cameras and drones, using ground cameras to collect remote sensing object data in the area and supplementing the drone remote sensing object data; When the drone or ground camera transmits the telemetry object data wirelessly, a signal center station is designed in the area, and the signal center station is used as a signal reinforcement point to increase the wireless transmission signal of the drone and ground camera; According to the different target member block networks in the three-dimensional surface model, the other telemetry object data in the target member block network are automatically obtained, and the change status of other telemetry object data between the previous moment and the current moment is analyzed. The mathematical model of other telemetry object data is designed with mathematical functions as the guide, and the surface change status of the telemetry object is output externally.

2. A blockchain drone telemetry data processing method according to claim 1, characterized in that: The UAV remote sensing mapping uses a low flying altitude combined with precise positioning to adjust the mapping scale height, so as to obtain high-definition mapping image data, and the mapping image data is the remote sensing object data.

3. A blockchain drone telemetry data processing method according to claim 1, characterized in that: The integrated remote sensing mapping of satellites and UAVs includes coverage integration, time resolution integration and data fusion integration. The coverage integration is achieved by overlapping the global range of satellite remote sensing with the regional range of UAV remote sensing; the time resolution integration is achieved by making the satellite remote sensing observation frequency less than the UAV remote sensing observation frequency, so that the data of the UAV remote sensing observation frequency supplement the data of the satellite observation frequency; the data fusion integration is achieved by combining the large-scale data of satellite remote sensing with the high-resolution data of UAVs and conducting multi-scale analysis.

4. A blockchain drone telemetry data processing method according to claim 1, characterized in that: The remote sensing mapping integrated with ground cameras and UAVs includes precision supplement integration and data verification integration. Precision supplement integration realizes UAV data precision calibration with ground control points through ground cameras, and data verification integration realizes UAV data verification and supplement through fine measurement of key areas through ground cameras.

5. A blockchain drone telemetry data processing method according to claim 1, characterized in that: The method uses different telemetry object data to construct different target member block networks through multi-layer associated topological networks, and uses different target member block networks to jointly construct a three-dimensional surface model, including the following steps: S1. parse different telemetry object data to obtain telemetry object data of a first associated topology network included in the current UAV telemetry data, where the first associated topology network is a multi-layer associated topology network used to simulate various telemetry targets in a target area; S2, filtering the telemetry object data in the first association topology network by key objects to generate telemetry control point data of the second association topology network, where the second association topology network is a subnetwork of the first association topology network in the multi-layer association topology network; S3, determining the target member block network corresponding to the telemetry object data according to the telemetry control point data of the second associated topology network, wherein the member devices in the target member block network correspond one-to-one to the telemetry targets in the sensitive telemetry target queue, and each member device corresponds to a telemetry target in the sensitive telemetry target queue; S4. Using the target member block networks corresponding to different telemetry object data obtained in the above steps S1-S3, different target member block networks are combined to construct a three-dimensional surface model of the area.

6. A blockchain drone telemetry data processing method according to claim 1, characterized in that: Among them, other telemetry object data in the target member block network are automatically obtained according to different target member block networks in the surface three-dimensional model, including: According to the three-dimensional surface model, the telemetry targets in the sensitive telemetry target queue are obtained through the member devices of different target member block networks. The telemetry targets are other telemetry object data in the target member area network and are automatically saved.

7. A blockchain drone telemetry data processing method according to claim 1, characterized in that: The mathematical function includes a linear function and a curve function. The data model is designed with the linear function as a guide so that the automatically obtained telemetry object data automatically predicts the telemetry data generated in the continuous time; the data model is designed with the curve function as a guide so that the automatically obtained telemetry object data automatically predicts the telemetry data generated in the continuous time.

8. A blockchain drone telemetry data processing method according to claim 7, characterized in that: The mathematical model of other telemetry object data is designed based on linear functions, including the following steps: S1, recording the coordinate position of other remote sensing object data in the three-dimensional model of the earth's surface at the previous moment and the coordinate position at the current moment, performing phase difference calculation between the coordinate position of the remote sensing object data at the previous moment and the current coordinate position to obtain the moving coordinate data of the remote sensing object data and save the data; S2, taking a period of time as a reference, repeatedly obtaining the moving coordinate data with the same time difference in step S1, and taking the moving coordinate data as the surface change value; S3. Based on step S2, assume that the coordinates of the telemetry object data at the current moment are (x, y, z), the coordinates at the previous moment are (x1, y1, z1), and the moving coordinate data is [(x-x1), (y-y1), (z-z1)]; perform linear changes on the telemetry object data at continuous time t, and the calculation formula is: Y=[(x-x1),(y-y1),(z-z1)]t Where: Y is the coordinate data for inferring and predicting the movement of the telemetry object data in continuous time t.

9. A blockchain drone telemetry data processing method according to claim 7, characterized in that: The mathematical model of other telemetry object data is designed based on the curve function, including the following steps: S1, recording the coordinate position of other remote sensing object data in the three-dimensional model of the earth's surface at the previous moment and the coordinate position at the current moment, performing phase difference calculation between the coordinate position of the remote sensing object data at the previous moment and the current coordinate position to obtain the moving coordinate data of the remote sensing object data and save the data; S2, taking a period of time as a reference, repeatedly obtaining the moving coordinate data with the same time difference in step S1, and taking the moving coordinate data as the surface change value; S3. Based on step S2, assume that the coordinates of the telemetry object data at the current moment are (x, y, z), the coordinates at the previous moment are (x1, y1, z1), and the moving coordinate data is [(x-x1), (y-y1), (z-z1)]; perform curve changes on the telemetry object data at continuous time t, and the calculation formula is: Y=sin[(x-x1),(y-y1),(z-z1)] Where: Y is the coordinate data for inferring and predicting the movement of the telemetry object data in continuous time t.

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