Low-altitude economic data processing method, system and platform

Abstract

The invention relates to the technical field of low-altitude economy, and particularly discloses a low-altitude economy data processing method, system and platform, and the method comprises the steps: scheduling and cooperating with data collection nodes, obtaining low-altitude economy original data, synchronously collecting space-time metadata, and carrying out the data preprocessing; acquiring a transmission link distance and transmission complexity of the data acquisition node, calibrating a data acquisition auxiliary node, acquiring initial positioning data, predicting a positioning error value and a positioning confidence coefficient, and performing positioning correction; performing decentration processing on the data acquisition nodes, accessing the data acquisition nodes to a low-altitude economic data processing platform, performing fusion data feature extraction and data packet encapsulation, and obtaining a low-altitude economic data packet; constructing a low-altitude economic data packet evaluation index system, calculating a comprehensive quality score, and generating a quality evaluation result; the low-altitude economic data processing efficiency and quality are effectively optimized through full-process data processing, decentration and quality evaluation.

Description

Technical Field

[0001] This invention relates to the field of low-altitude economic technology, and in particular to a method, system and platform for processing low-altitude economic data. Background Technology

[0002] With the continuous development of the low-altitude economy, drones are widely used in logistics, surveying and mapping, and other fields, thus generating a large amount of low-altitude economic data. The current processing of low-altitude economic data mainly focuses on single data collection, data processing or data evaluation, without forming a systematic processing of the entire process, resulting in low data processing efficiency. In addition, there is a lack of quantitative assessment of data quality before low-altitude economic data is traded or applied, and data processing generally relies on centralized platforms, which have problems such as high failure rate and low security.

[0003] Therefore, the present invention provides a method, system and platform for processing low-altitude economic data. Summary of the Invention

[0004] This invention provides a method, system, and platform for low-altitude economic data processing. It generates a data acquisition task set based on data requirements, schedules and coordinates data acquisition nodes to acquire raw low-altitude economic data, synchronously acquires spatiotemporal metadata, and performs data preprocessing. It obtains the transmission link distance and transmission complexity between the data acquisition nodes and the low-altitude economic data processing platform, calibrates data acquisition auxiliary nodes, acquires initial positioning data, predicts positioning error values ​​and positioning reliability, and performs positioning correction based on the data acquisition auxiliary nodes. The data acquisition nodes are decentralized and connected to the low-altitude economic data processing platform. The preprocessed data results are fused with the spatiotemporal metadata, and fused data features are extracted and data packets are encapsulated to obtain low-altitude economic data packets. An evaluation index system for low-altitude economic data packets is constructed, a comprehensive quality score for the low-altitude economic data packets is calculated, and a quality evaluation result is generated. This invention effectively optimizes the efficiency and quality of low-altitude economic data processing through end-to-end data processing, decentralization, and quality evaluation.

[0005] This invention provides a method for processing low-altitude economic data, comprising: Generate a set of data collection tasks based on data requirements, schedule and coordinate data collection nodes to obtain raw low-altitude economic data, and synchronously collect spatiotemporal metadata and perform data preprocessing. The transmission link distance and transmission complexity between the data acquisition node and the low-altitude economic data processing platform are obtained, the data acquisition auxiliary node is calibrated, the initial positioning data is obtained and the positioning error value and positioning reliability are predicted, and positioning correction is performed based on the data acquisition auxiliary node. The data acquisition nodes are decentralized and connected to the low-altitude economic data processing platform. The data preprocessing results are fused with spatiotemporal metadata, and the fused data features are extracted and data packets are encapsulated to obtain low-altitude economic data packets. A low-altitude economic data packet evaluation index system is constructed, a comprehensive quality score for the low-altitude economic data packet is calculated, and a quality evaluation result is generated.

[0006] According to the present invention, a low-altitude economic data processing method generates a data acquisition task set based on data requirements, schedules and coordinates data acquisition nodes to acquire raw low-altitude economic data, synchronously acquires spatiotemporal metadata, and performs data preprocessing, including: The data requirements for low-altitude economic data collection are obtained, data collection tasks are generated, and a data collection task set is constructed. The data requirements include: data type requirements, data spatial range requirements, data time range requirements, and data accuracy requirements. Obtain the list of data acquisition nodes, schedule and coordinate the data acquisition nodes according to the data acquisition task set and node status, and assign data acquisition nodes to each data acquisition task. Data acquisition tasks are executed according to the data acquisition nodes to obtain raw low-altitude economic data and synchronously collect spatiotemporal metadata. The raw low-altitude economic data and spatiotemporal metadata are processed for spatiotemporal synchronization. The synchronous processing results are preprocessed, including data verification preprocessing, data quality assessment preprocessing, data re-sampling preprocessing, and data compression preprocessing.

[0007] According to the present invention, a low-altitude economic data processing method is provided, which obtains the transmission link distance and transmission complexity between the data acquisition node and the low-altitude economic data processing platform, and calibrates the data acquisition auxiliary node, including: A coordinate system is established based on the low-altitude economic data processing platform. The coordinate data of the data acquisition nodes are obtained from the satellite navigation system. A data transmission link is established, and the transmission link distance between the data acquisition nodes and the low-altitude economic data processing platform is determined. To identify the influencing factors of data transmission, establish a quantitative model for transmission complexity, define a quantitative function for complexity, simulate data transmission based on the coordinate data of the data acquisition nodes, obtain data on the influencing factors of the data acquisition nodes, and quantify the transmission complexity between the data acquisition nodes and the low-altitude economic data processing platform by combining the transmission link distance. Based on the transmission complexity, identify the data acquisition nodes to be located and corrected, and add labels to be located and corrected. A calibration matrix for pre-defined data acquisition auxiliary nodes is used to filter data acquisition nodes based on calibration thresholds, and the data acquisition auxiliary nodes are initially calibrated. The calibration matrix includes: calibration dimension, calibration weight, calibration strategy, and calibration threshold. Obtain the relative coordinate distance data between the initially determined data acquisition auxiliary node and the data acquisition node to be located and corrected; The data acquisition auxiliary nodes initially determined are then calibrated a second time based on the relative coordinate distance data. Establish a data transmission link between the data acquisition node to be located and calibrated and the data acquisition auxiliary node for secondary calibration, and perform spatiotemporal synchronization processing; Simulated data transmission is performed between the data acquisition node to be located and calibrated and the secondary calibration data acquisition auxiliary node. The calibration accuracy is evaluated based on the calibration matrix, and the data acquisition auxiliary node is finally calibrated based on the calibration accuracy threshold. A data acquisition auxiliary node network is established based on the final calibrated data acquisition auxiliary nodes. The network topology is then optimized according to the optimization objective function and network constraints, and the data acquisition auxiliary nodes are recalibrated.

[0008] According to the present invention, a low-altitude economic data processing method is provided, which acquires initial positioning data and predicts positioning error values ​​and positioning reliability, and performs positioning correction based on data acquisition auxiliary nodes, including: The system acquires the initial positioning data of the data acquisition node in the current time period. Based on the transmission link distance and transmission complexity between the data acquisition node and the low-altitude economic data processing platform, combined with historical and current environmental parameters, and according to the spatiotemporal neural network model, it predicts the positioning error value and positioning reliability of the data acquisition node in the future time period. The relative distance between the final determined data acquisition auxiliary node and the data acquisition node is obtained. Combined with the positioning error value, the positioning reliability, and the positioning reliability of the final determined data acquisition auxiliary node itself, a positioning correction model is constructed to correct the initial positioning data of the data acquisition node.

[0009] According to a low-altitude economic data processing method provided by the present invention, data acquisition nodes are decentralized and connected to a low-altitude economic data processing platform, including: Decentralized identity registration and authentication are performed on data collection nodes; A blockchain-based node identity network is constructed based on the coordinate system, and data collection nodes are connected to the low-altitude economic data processing platform through a preset communication protocol.

[0010] According to a low-altitude economic data processing method provided by the present invention, the method fuses data preprocessing results with spatiotemporal metadata, extracts fused data features, and encapsulates data packets to obtain low-altitude economic data packets, including: Based on the spatiotemporal reference and semantic association, the data preprocessing results are fused with spatiotemporal metadata to generate fused data; Based on a preset feature extraction strategy, the fused data features are extracted to obtain fused data features and feature descriptors. Based on a standardized data packet structure, the fused data, fused data features, feature descriptors, and data transmission links are compressed, encrypted, and encapsulated to generate low-altitude economic data packets, which are stored in the storage network of the node identity network and transmitted according to a preset communication protocol.

[0011] According to a low-altitude economic data processing method provided by the present invention, a low-altitude economic data packet evaluation index system is constructed, a comprehensive quality score of the low-altitude economic data packet is calculated, and a quality evaluation result is generated, including: The quality assessment indicators for low-altitude economic data packets are obtained, a low-altitude economic data packet assessment indicator system is established, and the comprehensive quality score of low-altitude economic data packets is calculated. The quality assessment indicators include: timeliness quality assessment indicators, data integrity quality assessment indicators, and data accuracy quality assessment indicators. Quality assessment results are generated based on quality scoring thresholds.

[0012] A low-altitude economic data processing method provided by the present invention further includes: Based on the quality assessment results, a time-stamped quality assessment certificate is generated and uploaded to the low-altitude economic data processing platform for data value allocation. Obtain application feedback on low-altitude economic data and optimize relevant models in the low-altitude economic data processing process.

[0013] This invention provides a low-altitude economic data processing system, comprising: The data acquisition and preprocessing module is used to generate a set of data acquisition tasks according to data requirements, schedule and coordinate data acquisition nodes, acquire raw low-altitude economic data, synchronously acquire spatiotemporal metadata and perform data preprocessing. The data acquisition-assisted positioning correction module is used to obtain the transmission link distance and transmission complexity between the data acquisition node and the low-altitude economic data processing platform, calibrate the data acquisition-assisted node, acquire initial positioning data and predict positioning error value and positioning reliability, and perform positioning correction based on the data acquisition-assisted node. The data packet encapsulation module is used to decentralize the data acquisition nodes, connect them to the low-altitude economic data processing platform, fuse the data preprocessing results with spatiotemporal metadata, extract fused data features, and encapsulate data packets to obtain low-altitude economic data packets. The quality assessment module is used to construct an evaluation index system for low-altitude economic data packets, calculate the comprehensive quality score of low-altitude economic data packets, and generate quality assessment results.

[0014] This invention provides a low-altitude economic data processing platform, comprising: The data packet access module is used to connect to the low-altitude economic data processing system and acquire low-altitude economic data packets; The data visualization module is used to unpack low-altitude economic data packets and visualize the low-altitude economic data.

[0015] The beneficial effects of this invention compared to the prior art are as follows: This invention generates a data acquisition task set based on data requirements, schedules and coordinates data acquisition nodes to acquire raw low-altitude economic data, synchronously collects spatiotemporal metadata, and performs data preprocessing. It obtains the transmission link distance and transmission complexity between the data acquisition nodes and the low-altitude economic data processing platform, calibrates data acquisition auxiliary nodes, acquires initial positioning data, predicts positioning error values ​​and positioning reliability, and performs positioning correction based on the data acquisition auxiliary nodes. The data acquisition nodes are then decentralized and connected to the low-altitude economic data processing platform. The preprocessed data results are fused with the spatiotemporal metadata, and fused data features are extracted and data packets are encapsulated to obtain low-altitude economic data packets. An evaluation index system for low-altitude economic data packets is constructed, and a comprehensive quality score is calculated to generate a quality evaluation result. This invention effectively optimizes the efficiency and quality of low-altitude economic data processing through end-to-end data processing, decentralization, and quality evaluation.

[0016] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in this application.

[0017] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0018] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings: Figure 1 A flowchart illustrating a low-altitude economic data processing method provided in an embodiment of the present invention; Figure 2 A schematic diagram of the structure of a low-altitude economic data processing system provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of the structure of a low-altitude economic data processing platform provided in an embodiment of the present invention. Detailed Implementation

[0019] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0020] Example 1: This invention provides a method for processing low-altitude economic data, referring to... Figure 1 ,include: Generate a set of data collection tasks based on data requirements, schedule and coordinate data collection nodes to obtain raw low-altitude economic data, and synchronously collect spatiotemporal metadata and perform data preprocessing. The transmission link distance and transmission complexity between the data acquisition node and the low-altitude economic data processing platform are obtained, the data acquisition auxiliary node is calibrated, the initial positioning data is obtained and the positioning error value and positioning reliability are predicted, and positioning correction is performed based on the data acquisition auxiliary node. The data acquisition nodes are decentralized and connected to the low-altitude economic data processing platform. The data preprocessing results are fused with spatiotemporal metadata, and the fused data features are extracted and data packets are encapsulated to obtain low-altitude economic data packets. A low-altitude economic data packet evaluation index system is constructed, a comprehensive quality score for the low-altitude economic data packet is calculated, and a quality evaluation result is generated.

[0021] In this embodiment, data requirement refers to the need to collect low-altitude economic data.

[0022] In this embodiment, the data acquisition task set refers to a collection assembled based on data acquisition tasks.

[0023] In this embodiment, the data acquisition node refers to the node formed by the location of the sensor that collects low-altitude economic data.

[0024] In this embodiment, the low-altitude economic raw data refers to the raw data obtained from the data acquisition node without data preprocessing.

[0025] In this embodiment, spatiotemporal metadata refers to metadata used to describe temporal and spatial data.

[0026] In this embodiment, the low-altitude economic data processing platform refers to a platform used to access low-altitude economic data and visualize the processing results.

[0027] In this embodiment, the transmission link distance refers to the channel through which data acquisition nodes transmit data with the low-altitude economic data processing platform.

[0028] In this embodiment, transmission complexity refers to a quantified value of the difficulty of data acquisition nodes transmitting data.

[0029] In this embodiment, the data acquisition auxiliary node refers to the data acquisition node that assists the data acquisition node in performing positioning correction.

[0030] In this embodiment, positioning correction refers to the correction of the coordinates of the data acquisition node to ensure data processing quality and efficiency.

[0031] In this embodiment, decentralization refers to a mode in which data is distributed among various data acquisition nodes for data processing.

[0032] In this embodiment, the low-altitude economic data packet refers to the data set obtained by compressing, encrypting, and encapsulating low-altitude economic data according to standardized data packets.

[0033] In this embodiment, the low-altitude economic data packet evaluation index system refers to the index system used to evaluate the quality of low-altitude economic data packets.

[0034] In this embodiment, the comprehensive quality score refers to the score obtained from the low-altitude economic data packet evaluation index system, which is used to quantitatively represent the data quality.

[0035] The beneficial effects of the above technical solution are as follows: A data acquisition task set is generated based on data requirements; data acquisition nodes are scheduled and coordinated to acquire raw low-altitude economic data; spatiotemporal metadata is collected synchronously and preprocessed; the transmission link distance and transmission complexity between the data acquisition nodes and the low-altitude economic data processing platform are obtained; auxiliary data acquisition nodes are calibrated; initial positioning data is acquired and positioning error values ​​and positioning reliability are predicted; positioning correction is performed based on the auxiliary data acquisition nodes; the data acquisition nodes are decentralized and connected to the low-altitude economic data processing platform; the preprocessed data results are fused with spatiotemporal metadata, and fused data features are extracted and data packets are encapsulated to obtain low-altitude economic data packets; a low-altitude economic data packet evaluation index system is constructed, the comprehensive quality score of the low-altitude economic data packets is calculated, and a quality evaluation result is generated; this invention effectively optimizes the efficiency and quality of low-altitude economic data processing through full-process data processing, decentralization, and quality evaluation.

[0036] Example 2: Based on Example 1, this invention provides a low-altitude economic data processing method, which generates a data acquisition task set according to data requirements, schedules and coordinates data acquisition nodes, acquires raw low-altitude economic data, synchronously acquires spatiotemporal metadata, and performs data preprocessing, including: The data requirements for low-altitude economic data collection are obtained, data collection tasks are generated, and a data collection task set is constructed. The data requirements include: data type requirements, data spatial range requirements, data time range requirements, and data accuracy requirements. Obtain the list of data acquisition nodes, schedule and coordinate the data acquisition nodes according to the data acquisition task set and node status, and assign data acquisition nodes to each data acquisition task. Data acquisition tasks are executed according to the data acquisition nodes to obtain raw low-altitude economic data and synchronously collect spatiotemporal metadata. The raw low-altitude economic data and spatiotemporal metadata are processed for spatiotemporal synchronization. The synchronous processing results are preprocessed, including data verification preprocessing, data quality assessment preprocessing, data re-sampling preprocessing, and data compression preprocessing.

[0037] In this embodiment, data requirement refers to the need to collect low-altitude economic data.

[0038] In this embodiment, the data acquisition task refers to the task generated based on data requirements, which is used to ensure the execution of data requirements.

[0039] In this embodiment, the data acquisition task set refers to the set assembled based on the data acquisition tasks.

[0040] In this embodiment, the data acquisition node list refers to a list constructed based on the data acquisition nodes.

[0041] In this embodiment, the data acquisition node refers to the node formed by the location of the sensor that collects low-altitude economic data.

[0042] In this embodiment, node status refers to the state of data acquisition nodes, including data acquisition efficiency status and data acquisition accuracy status.

[0043] In this embodiment, data acquisition nodes are assigned to each data acquisition task. For example, data acquisition nodes b1, b2, and b3 are assigned to data acquisition task a1 to ensure the completion of data acquisition task a1.

[0044] In this embodiment, the low-altitude economic raw data refers to the raw data obtained from the data acquisition node without data preprocessing.

[0045] In this embodiment, spatiotemporal metadata refers to metadata used to describe temporal and spatial data.

[0046] In this embodiment, synchronous acquisition of spatiotemporal metadata refers to the simultaneous acquisition of time data and spatial data when acquiring raw low-altitude economic data.

[0047] In this embodiment, spatiotemporal synchronization processing refers to adding time data and spatial data to the corresponding low-altitude economic raw data to clarify the collection time and collection space of the low-altitude economic raw data.

[0048] In this embodiment, the synchronization processing results are preprocessed to obtain low-altitude economic data.

[0049] In this embodiment, data preprocessing also includes data cleaning to ensure data quality.

[0050] The beneficial effects of the above technical solutions are as follows: by generating a data collection task set based on data requirements, scheduling and coordinating data collection nodes, acquiring raw low-altitude economic data, synchronously collecting spatiotemporal metadata and performing data preprocessing, laying a data foundation for subsequent operations.

[0051] Example 3: Based on Example 1, this invention provides a low-altitude economic data processing method, which obtains the transmission link distance and transmission complexity between the data acquisition node and the low-altitude economic data processing platform, and calibrates the data acquisition auxiliary node, including: A coordinate system is established based on the low-altitude economic data processing platform. The coordinate data of the data acquisition nodes are obtained from the satellite navigation system. A data transmission link is established, and the transmission link distance between the data acquisition nodes and the low-altitude economic data processing platform is determined. To identify the influencing factors of data transmission, establish a quantitative model for transmission complexity, define a quantitative function for complexity, simulate data transmission based on the coordinate data of the data acquisition nodes, obtain data on the influencing factors of the data acquisition nodes, and quantify the transmission complexity between the data acquisition nodes and the low-altitude economic data processing platform by combining the transmission link distance. Based on the transmission complexity, identify the data acquisition nodes to be located and corrected, and add labels to be located and corrected. A calibration matrix for pre-defined data acquisition auxiliary nodes is used to filter data acquisition nodes based on calibration thresholds, and the data acquisition auxiliary nodes are initially calibrated. The calibration matrix includes: calibration dimension, calibration weight, calibration strategy, and calibration threshold. Obtain the relative coordinate distance data between the initially determined data acquisition auxiliary node and the data acquisition node to be located and corrected; The data acquisition auxiliary nodes initially determined are then calibrated a second time based on the relative coordinate distance data. Establish a data transmission link between the data acquisition node to be located and calibrated and the data acquisition auxiliary node for secondary calibration, and perform spatiotemporal synchronization processing; Simulated data transmission is performed between the data acquisition node to be located and calibrated and the secondary calibration data acquisition auxiliary node. The calibration accuracy is evaluated based on the calibration matrix, and the data acquisition auxiliary node is finally calibrated based on the calibration accuracy threshold. A data acquisition auxiliary node network is established based on the final calibrated data acquisition auxiliary nodes. The network topology is then optimized according to the optimization objective function and network constraints, and the data acquisition auxiliary nodes are recalibrated.

[0052] In this embodiment, the low-altitude economic data processing platform refers to a platform used to access low-altitude economic data and visualize the processing results.

[0053] In this embodiment, a coordinate system is established based on the low-altitude economic data processing platform, with the low-altitude economic data processing platform as the origin of the coordinate system.

[0054] In this embodiment, the satellite navigation system refers to a system used for positioning and navigating data acquisition nodes to ensure positioning accuracy.

[0055] In this embodiment, the node coordinate data refers to the coordinate data of the data acquisition node obtained from the satellite navigation system in the coordinate system.

[0056] In this embodiment, the data transmission link refers to the channel through which data acquisition nodes transmit data with the low-altitude economic data processing platform.

[0057] In this embodiment, the transmission link distance refers to the straight-line distance of the data transmission link, which is determined based on the coordinates of the data acquisition node and the coordinates of the low-altitude economic data processing platform.

[0058] In this embodiment, influencing factors refer to factors that affect data transmission, including: interference intensity influencing factors, multi-link influencing factors, obstruction influencing factors, and dynamic influencing factors.

[0059] In this embodiment, the transmission complexity quantification model refers to a model used to quantify the complexity of transmitting low-altitude economic data from the data acquisition node to the low-altitude economic data processing platform.

[0060] In this embodiment, the complexity quantification function refers to the function used to quantify the transmission complexity.

[0061] In this embodiment, simulated data transmission refers to data transmission based on simulated data prior to low-altitude economic data transmission. The simulated data is known and is used to specifically determine the influencing factors of the data acquisition nodes.

[0062] In this embodiment, the influencing factor data refers to the actual impact of influencing factors on data transmission during the data transmission process. For example, multiple link influencing factors cause data transmission delay, with a delay time t1, where t1 is the multi-link influencing factor data.

[0063] In this embodiment, data on influencing factors of the data acquisition node are obtained, and the transmission link distance is combined to quantify and determine the transmission complexity between the data acquisition node and the low-altitude economic data processing platform. ; in, This represents the transmission complexity between the i1th data acquisition node and the low-altitude economic data processing platform during the ti1 time period; This represents the data on factors influencing multiple links; a1 represents the weighting coefficient of the data on factors influencing multiple links. This represents the coordinate data of the i1th data acquisition node during the time period ti1; Indicates when When it is not less than 1, Output Otherwise, output 0; a2 represents the data on factors affecting occlusion; a2 represents the weighting coefficient of the data on factors affecting occlusion. This represents the transmitted signal strength of the i1th data acquisition node during the time period ti1; This indicates the received signal strength of the i1th data acquisition node received by the low-altitude economic data processing platform during the ti1 time period; a3 represents the data on factors affecting interference intensity; a3 represents the weighting coefficient of the data on factors affecting interference intensity. This represents the interference signal power of the i1th data acquisition node during the time period ti1; This represents the expected signal power of the i1th data acquisition node during the time period ti1; This represents data on dynamic influencing factors; a4 represents the weighting coefficients of the dynamic influencing factor data. This represents the transmission rate of the i1th data acquisition node during the time period ti1; This represents the signal wavelength of the i1th data acquisition node during the time period ti1; the weighting coefficients satisfy... n1 represents the number of data acquisition nodes; ti1 represents the time period during which the t1th data acquisition node transmits data.

[0064] In this embodiment, the data acquisition node to be located and corrected is calibrated according to the transmission complexity. For example, when the transmission complexity of the data acquisition node is not lower than F1, the data acquisition node is calibrated as the data acquisition node to be located and corrected.

[0065] In this embodiment, the data acquisition node to be located and corrected refers to the node whose data transmission needs to be protected from being affected by positioning errors.

[0066] In this embodiment, the data acquisition auxiliary node refers to the data acquisition node that assists the data acquisition node in performing positioning correction.

[0067] In this embodiment, the calibration matrix is ​​a matrix that specifies the calibration data acquisition auxiliary nodes.

[0068] In this embodiment, the calibration threshold is a threshold for calibrating the data acquisition auxiliary node. For example, the distance between the data acquisition auxiliary node and the data acquisition node to be located and calibrated should not be higher than d1.

[0069] In this embodiment, preliminary calibration refers to the calibration of data acquisition nodes based on the calibration matrix.

[0070] In this embodiment, the relative coordinate distance data refers to the distance data between the initially determined data acquisition auxiliary node and the data acquisition node to be located and corrected, which is used to further adapt the data acquisition node.

[0071] In this embodiment, the calibration threshold is used to adapt to all data acquisition nodes, and the relative coordinate distance data is used to further adapt to each data acquisition node.

[0072] In this embodiment, secondary calibration refers to calibration performed based on the preliminary calibration and incorporating relative coordinate distance data.

[0073] In this embodiment, calibration accuracy refers to the accuracy at which the data acquisition auxiliary node can be used to position and correct the data acquisition node, which is determined by evaluating the results of simulated data transmission in conjunction with the calibration matrix.

[0074] In this embodiment, the calibration accuracy threshold refers to the accuracy threshold used to determine whether a data acquisition auxiliary node can be used to perform positioning correction on the data acquisition node to be positioned.

[0075] In this embodiment, the data acquisition auxiliary node network refers to the node network established based on the finally calibrated data acquisition auxiliary nodes for positioning correction.

[0076] In this embodiment, the optimization objective function refers to the objective function of optimizing positioning correction by adding or removing data acquisition auxiliary nodes based on the data acquisition auxiliary node network.

[0077] In this embodiment, the constraints are, for example, that the number of data acquisition auxiliary nodes for one data acquisition node is not less than b1 and not more than b2.

[0078] The beneficial effects of the above technical solution are as follows: by obtaining the transmission link distance and transmission complexity between the data acquisition node and the low-altitude economic data processing platform, the data acquisition auxiliary node can be calibrated, which is beneficial for subsequent positioning and correction of the data acquisition node, thereby ensuring the quality of low-altitude economic data.

[0079] Example 4: Based on Example 3, this invention provides a low-altitude economic data processing method, which acquires initial positioning data and predicts positioning error values ​​and positioning confidence, and performs positioning correction based on data acquisition auxiliary nodes, including: The system acquires the initial positioning data of the data acquisition node in the current time period. Based on the transmission link distance and transmission complexity between the data acquisition node and the low-altitude economic data processing platform, combined with historical and current environmental parameters, and according to the spatiotemporal neural network model, it predicts the positioning error value and positioning reliability of the data acquisition node in the future time period. The relative distance between the final determined data acquisition auxiliary node and the data acquisition node is obtained. Combined with the positioning error value, the positioning reliability, and the positioning reliability of the final determined data acquisition auxiliary node itself, a positioning correction model is constructed to correct the initial positioning data of the data acquisition node.

[0080] In this embodiment, the initial positioning data refers to the positioning data of the data acquisition node when it has not performed positioning correction in the current time period.

[0081] In this embodiment, the spatiotemporal neural network model refers to a neural network model established based on time data and spatial data, which is used to predict the positioning error value and positioning reliability of the data acquisition node in the future time period.

[0082] In this embodiment, location confidence refers to the reliability of the predicted location of the data acquisition node in the future time period.

[0083] In this embodiment, the positioning correction model refers to a model used to correct the initial positioning data of the data acquisition node.

[0084] The beneficial effects of the above technical solutions are as follows: by acquiring initial positioning data and predicting positioning error values ​​and positioning reliability, positioning correction is performed based on data acquisition auxiliary nodes, which is conducive to optimizing the processing quality of subsequent low-altitude economic data processing.

[0085] Example 5: Based on Example 1, this invention provides a low-altitude economic data processing method that decentralizes data acquisition nodes and connects them to a low-altitude economic data processing platform, including: Decentralized identity registration and authentication are performed on data collection nodes; A blockchain-based node identity network is constructed based on the coordinate system, and data collection nodes are connected to the low-altitude economic data processing platform through a preset communication protocol.

[0086] In this embodiment, decentralization refers to a mode of distributing data to various data collection nodes for data processing.

[0087] In this embodiment, blockchain refers to dividing data collection nodes into regions and constructing data transmission links based on data collection nodes in the same region.

[0088] In this embodiment, a preset communication protocol is used to ensure data transmission security, such as a point-to-point communication protocol.

[0089] In this embodiment, the node identity network refers to a network built based on the decentralized identities of each data collection node in the blockchain.

[0090] The beneficial effects of the above technical solution are as follows: by decentralizing the data acquisition nodes and connecting them to the low-altitude economic data processing platform, the reliance on a single center is avoided, and the processing quality of low-altitude economic data processing is further optimized.

[0091] Example 6: Based on Example 5, this invention provides a low-altitude economic data processing method, which fuses data preprocessing results with spatiotemporal metadata, extracts fused data features, and encapsulates data packets to obtain low-altitude economic data packets, including: Based on the spatiotemporal reference and semantic association, the data preprocessing results are fused with spatiotemporal metadata to generate fused data; Based on a preset feature extraction strategy, the fused data features are extracted to obtain fused data features and feature descriptors. Based on a standardized data packet structure, the fused data, fused data features, feature descriptors, and data transmission links are compressed, encrypted, and encapsulated to generate low-altitude economic data packets, which are stored in the storage network of the node identity network and transmitted according to a preset communication protocol.

[0092] In this embodiment, fused data refers to the data obtained by fusing the data preprocessing results with spatiotemporal metadata based on spatiotemporal references and semantic associations.

[0093] In this embodiment, the spatiotemporal reference refers to the basic calibration based on time and space data, used to construct a unified coordinate system for data fusion; semantic association refers to the association based on the spatiotemporal reference and the understanding of the relationships between data.

[0094] In this embodiment, the preset feature extraction strategy refers to a preset strategy used for fusion feature extraction.

[0095] In this embodiment, the fused data features refer to the features used to represent the fused data, obtained according to a preset feature extraction strategy.

[0096] In this embodiment, a feature descriptor refers to a symbol used to intuitively and simply describe the features of fused data.

[0097] In this embodiment, the standardized data packet structure refers to a pre-defined structure used for packaging data.

[0098] In this embodiment, low-altitude economic data packets refer to the data set obtained by compressing, encrypting, and encapsulating standardized data packets.

[0099] In this embodiment, the fused data, fused data features, feature descriptors, and data transmission links are compressed, encrypted, and encapsulated according to a standardized data packet structure, effectively ensuring the processing efficiency of low-altitude economic data.

[0100] In this embodiment, the storage network refers to the network in the node identity network used to store low-altitude economic data packets.

[0101] The beneficial effects of the above technical solution are: by integrating the data preprocessing results with spatiotemporal metadata, extracting the integrated data features, and encapsulating data packets, low-altitude economic data packets can be obtained, which is conducive to optimizing the quality and efficiency of low-altitude economic data processing.

[0102] Example 7: Based on Example 1, this invention provides a low-altitude economic data processing method, which constructs a low-altitude economic data packet evaluation index system, calculates the comprehensive quality score of the low-altitude economic data packet, and generates quality evaluation results, including: The quality assessment indicators for low-altitude economic data packets are obtained, a low-altitude economic data packet assessment indicator system is established, and the comprehensive quality score of low-altitude economic data packets is calculated. The quality assessment indicators include: timeliness quality assessment indicators, data integrity quality assessment indicators, and data accuracy quality assessment indicators. Quality assessment results are generated based on quality scoring thresholds.

[0103] In this embodiment, the quality assessment indicators refer to the indicators used to evaluate the data quality of low-altitude economic data packets, including: timeliness quality assessment indicators, data integrity quality assessment indicators, and data accuracy quality assessment indicators.

[0104] In this embodiment, the quality score threshold refers to a preset threshold used to divide the quality assessment score, which is used to specifically determine the data quality. For example, when the quality assessment score is not lower than the quality score threshold, the data quality is determined to be high; when the quality assessment score is lower than the quality score threshold, the data quality is determined to be low.

[0105] In this embodiment, the low-altitude economic data packet evaluation index system refers to the index system used to evaluate the quality of low-altitude economic data packets.

[0106] In this embodiment, the comprehensive quality score refers to the score obtained from the low-altitude economic data packet evaluation index system, which is used to quantitatively represent the data quality.

[0107] The beneficial effects of the above technical solution are as follows: by constructing an evaluation index system for low-altitude economic data packets, calculating the comprehensive quality score of low-altitude economic data packets, and generating quality evaluation results, it is convenient to apply low-altitude economic data packets based on the quality evaluation results and ensure the quality of low-altitude economic data packets.

[0108] Example 8: Based on Example 1, the present invention provides a low-altitude economic data processing method, which further includes: Based on the quality assessment results, a time-stamped quality assessment certificate is generated and uploaded to the low-altitude economic data processing platform for data value allocation. Obtain application feedback on low-altitude economic data and optimize relevant models in the low-altitude economic data processing process.

[0109] In this embodiment, the quality assessment certificate refers to a certificate generated based on the quality assessment results to visually represent the quality assessment results; it is accompanied by a timestamp to ensure the uniqueness and credibility of the quality assessment certificate.

[0110] In this embodiment, data value refers to the value of data in application; the higher the data value, the easier it is to apply the data.

[0111] In this embodiment, data value allocation, for example, assigns a high data value to low-altitude economic data that is shown as high quality in the quality assessment certificate, so that the data can be directly applied to other applications.

[0112] In this embodiment, application feedback refers to feedback data from the application of low-altitude economic data, such as insufficient accuracy of the feedback data.

[0113] In this embodiment, relevant models in the low-altitude economic data processing process include, for example, transmission complexity quantification models and spatiotemporal neural network models.

[0114] The beneficial effects of the above technical solutions are as follows: by generating quality assessment certificates with timestamps and allocating data value, the utilization efficiency of low-altitude economic data processing results is ensured; and by providing feedback optimization to the model, the processing quality and efficiency of low-altitude economic data processing are further ensured.

[0115] Example 9: This invention provides a low-altitude economic data processing system, referencing... Figure 2 ,include: The data acquisition and preprocessing module is used to generate a set of data acquisition tasks according to data requirements, schedule and coordinate data acquisition nodes, acquire raw low-altitude economic data, synchronously acquire spatiotemporal metadata and perform data preprocessing. The data acquisition-assisted positioning correction module is used to obtain the transmission link distance and transmission complexity between the data acquisition node and the low-altitude economic data processing platform, calibrate the data acquisition-assisted node, acquire initial positioning data and predict positioning error value and positioning reliability, and perform positioning correction based on the data acquisition-assisted node. The data packet encapsulation module is used to decentralize the data acquisition nodes, connect them to the low-altitude economic data processing platform, fuse the data preprocessing results with spatiotemporal metadata, extract fused data features, and encapsulate data packets to obtain low-altitude economic data packets. The quality assessment module is used to construct an evaluation index system for low-altitude economic data packets, calculate the comprehensive quality score of low-altitude economic data packets, and generate quality assessment results.

[0116] The beneficial effects of the above technical solution are as follows: A data acquisition task set is generated based on data requirements; data acquisition nodes are scheduled and coordinated to acquire raw low-altitude economic data; spatiotemporal metadata is collected synchronously and preprocessed; the transmission link distance and transmission complexity between the data acquisition nodes and the low-altitude economic data processing platform are obtained; auxiliary data acquisition nodes are calibrated; initial positioning data is acquired and positioning error values ​​and positioning reliability are predicted; positioning correction is performed based on the auxiliary data acquisition nodes; the data acquisition nodes are decentralized and connected to the low-altitude economic data processing platform; the preprocessed data results are fused with spatiotemporal metadata, and fused data features are extracted and data packets are encapsulated to obtain low-altitude economic data packets; a low-altitude economic data packet evaluation index system is constructed, the comprehensive quality score of the low-altitude economic data packets is calculated, and a quality evaluation result is generated; this invention effectively optimizes the efficiency and quality of low-altitude economic data processing through full-process data processing, decentralization, and quality evaluation.

[0117] Example 10: This invention provides a low-altitude economic data processing platform, such as... Figure 3 As shown, it includes: The data packet access module is used to connect to the low-altitude economic data processing system and acquire low-altitude economic data packets; The data visualization module is used to unpack low-altitude economic data packets and visualize the low-altitude economic data.

[0118] In this embodiment, unpacking refers to the process of restoring low-altitude economic data packets.

[0119] The beneficial effects of the above technical solution are as follows: by connecting to the low-altitude economic data processing system and visually displaying the low-altitude economic data, the intuitive observation of the low-altitude economic data processing results is effectively realized.

[0120] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for processing low-altitude economic data, characterized in that, include: Generate a set of data collection tasks based on data requirements, schedule and coordinate data collection nodes to obtain raw low-altitude economic data, and synchronously collect spatiotemporal metadata and perform data preprocessing. The transmission link distance and transmission complexity between the data acquisition node and the low-altitude economic data processing platform are obtained, the data acquisition auxiliary node is calibrated, the initial positioning data is obtained and the positioning error value and positioning reliability are predicted, and positioning correction is performed based on the data acquisition auxiliary node. The data acquisition nodes are decentralized and connected to the low-altitude economic data processing platform. The data preprocessing results are fused with spatiotemporal metadata, and the fused data features are extracted and data packets are encapsulated to obtain low-altitude economic data packets. A low-altitude economic data packet evaluation index system is constructed, a comprehensive quality score for the low-altitude economic data packet is calculated, and a quality evaluation result is generated.

2. The low-altitude economic data processing method according to claim 1, characterized in that, Generate a data acquisition task set based on data requirements, schedule and coordinate data acquisition nodes to obtain raw low-altitude economic data, synchronously collect spatiotemporal metadata, and perform data preprocessing, including: The data requirements for low-altitude economic data collection are obtained, data collection tasks are generated, and a data collection task set is constructed. The data requirements include: data type requirements, data spatial range requirements, data time range requirements, and data accuracy requirements. Obtain the list of data acquisition nodes, schedule and coordinate the data acquisition nodes according to the data acquisition task set and node status, and assign data acquisition nodes to each data acquisition task. Data acquisition tasks are executed according to the data acquisition nodes to obtain raw low-altitude economic data and synchronously collect spatiotemporal metadata. The raw low-altitude economic data and spatiotemporal metadata are processed for spatiotemporal synchronization. The synchronous processing results are preprocessed, including data verification preprocessing, data quality assessment preprocessing, data re-sampling preprocessing, and data compression preprocessing.

3. The low-altitude economic data processing method according to claim 1, characterized in that, Obtain the transmission link distance and transmission complexity between the data acquisition node and the low-altitude economic data processing platform, and calibrate the data acquisition auxiliary nodes, including: A coordinate system is established based on the low-altitude economic data processing platform. The coordinate data of the data acquisition nodes are obtained from the satellite navigation system. A data transmission link is established, and the transmission link distance between the data acquisition nodes and the low-altitude economic data processing platform is determined. To identify the influencing factors of data transmission, establish a quantitative model for transmission complexity, define a quantitative function for complexity, simulate data transmission based on the coordinate data of the data acquisition nodes, obtain data on the influencing factors of the data acquisition nodes, and quantify the transmission complexity between the data acquisition nodes and the low-altitude economic data processing platform by combining the transmission link distance. Based on the transmission complexity, identify the data acquisition nodes to be located and corrected, and add labels to be located and corrected. A calibration matrix for pre-defined data acquisition auxiliary nodes is used to filter data acquisition nodes based on calibration thresholds, and the data acquisition auxiliary nodes are initially calibrated. The calibration matrix includes: calibration dimension, calibration weight, calibration strategy, and calibration threshold. Obtain the relative coordinate distance data between the initially determined data acquisition auxiliary node and the data acquisition node to be located and corrected; The data acquisition auxiliary nodes initially determined are then calibrated a second time based on the relative coordinate distance data. Establish a data transmission link between the data acquisition node to be located and calibrated and the data acquisition auxiliary node for secondary calibration, and perform spatiotemporal synchronization processing; Simulated data transmission is performed between the data acquisition node to be located and calibrated and the secondary calibration data acquisition auxiliary node. The calibration accuracy is evaluated based on the calibration matrix, and the data acquisition auxiliary node is finally calibrated based on the calibration accuracy threshold. A data acquisition auxiliary node network is established based on the final calibrated data acquisition auxiliary nodes. The network topology is then optimized according to the optimization objective function and network constraints, and the data acquisition auxiliary nodes are recalibrated.

4. The low-altitude economic data processing method according to claim 3, characterized in that, Acquire initial positioning data and predict positioning error and positioning reliability. Perform positioning correction based on data acquisition auxiliary nodes, including: The system acquires the initial positioning data of the data acquisition node in the current time period. Based on the transmission link distance and transmission complexity between the data acquisition node and the low-altitude economic data processing platform, combined with historical and current environmental parameters, and according to the spatiotemporal neural network model, it predicts the positioning error value and positioning reliability of the data acquisition node in the future time period. The relative distance between the final determined data acquisition auxiliary node and the data acquisition node is obtained. Combined with the positioning error value, the positioning reliability, and the positioning reliability of the final determined data acquisition auxiliary node itself, a positioning correction model is constructed to correct the initial positioning data of the data acquisition node.

5. The low-altitude economic data processing method according to claim 1, characterized in that, The data collection nodes are decentralized and connected to the low-altitude economic data processing platform, including: Decentralized identity registration and authentication are performed on data collection nodes; A blockchain-based node identity network is constructed based on the coordinate system, and data collection nodes are connected to the low-altitude economic data processing platform through a preset communication protocol.

6. The low-altitude economic data processing method according to claim 5, characterized in that, The data preprocessing results are fused with spatiotemporal metadata, and fused data features are extracted and data packets are encapsulated to obtain low-altitude economic data packets, including: Based on the spatiotemporal reference and semantic association, the data preprocessing results are fused with spatiotemporal metadata to generate fused data; Based on a preset feature extraction strategy, the fused data features are extracted to obtain fused data features and feature descriptors. Based on a standardized data packet structure, the fused data, fused data features, feature descriptors, and data transmission links are compressed, encrypted, and encapsulated to generate low-altitude economic data packets, which are stored in the storage network of the node identity network and transmitted according to a preset communication protocol.

7. The low-altitude economic data processing method according to claim 1, characterized in that, A low-altitude economic data packet evaluation index system is constructed, a comprehensive quality score for the low-altitude economic data packet is calculated, and quality evaluation results are generated, including: The quality assessment indicators for low-altitude economic data packets are obtained, a low-altitude economic data packet assessment indicator system is established, and the comprehensive quality score of low-altitude economic data packets is calculated. The quality assessment indicators include: timeliness quality assessment indicators, data integrity quality assessment indicators, and data accuracy quality assessment indicators. Quality assessment results are generated based on quality scoring thresholds.

8. The low-altitude economic data processing method according to claim 1, characterized in that, Also includes: Based on the quality assessment results, a time-stamped quality assessment certificate is generated and uploaded to the low-altitude economic data processing platform for data value allocation. Obtain application feedback on low-altitude economic data and optimize relevant models in the low-altitude economic data processing process.

9. A low-altitude economic data processing system, characterized in that, include: The data acquisition and preprocessing module is used to generate a set of data acquisition tasks according to data requirements, schedule and coordinate data acquisition nodes, acquire raw low-altitude economic data, synchronously acquire spatiotemporal metadata and perform data preprocessing. The data acquisition-assisted positioning correction module is used to obtain the transmission link distance and transmission complexity between the data acquisition node and the low-altitude economic data processing platform, calibrate the data acquisition-assisted node, acquire initial positioning data and predict positioning error value and positioning reliability, and perform positioning correction based on the data acquisition-assisted node. The data packet encapsulation module is used to decentralize the data acquisition nodes, connect them to the low-altitude economic data processing platform, fuse the data preprocessing results with spatiotemporal metadata, extract fused data features, and encapsulate data packets to obtain low-altitude economic data packets. The quality assessment module is used to construct an evaluation index system for low-altitude economic data packets, calculate the comprehensive quality score of low-altitude economic data packets, and generate quality assessment results.

10. A low-altitude economic data processing platform, characterized in that, include: The data packet access module is used to connect to the low-altitude economic data processing system and acquire low-altitude economic data packets; The data visualization module is used to unpack low-altitude economic data packets and visualize the low-altitude economic data.

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