Airborne cloud particle data processing method, device and system based on collaborative feature mapping

Abstract

The invention provides an airborne cloud particle data processing method, device, device and system based on collaborative feature mapping. Carrying out time sequence semantic anchoring binding on particle original data collected by airborne cloud particle detection equipment, flight state data collected by an airborne flight state sensor and airflow monitoring data collected by an airborne airflow monitoring sensor to generate a multi-dimensional time sequence association data set, and carrying out association path traversal mining on the multi-dimensional time sequence association data set; generating a coupling association path data set, executing dynamic node mapping of airborne flight working conditions and particle original data, generating a working condition adaptive mapping content set, performing one-to-one link binding on the working condition adaptive mapping content set and the particle original data subjected to false target removal based on sample time sequence attributes, and generating a link-bound particle data set; in combination with the working condition adaptation mapping content set, cloud micro physical parameter calculation and working condition adaptation correction are carried out, and standardized liquid water content, number concentration and spectral distribution data are generated. According to the invention, the accuracy and reliability of airborne cloud particle data processing can be improved.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to an airborne cloud particle data processing method, apparatus and system based on collaborative feature mapping. Background Technology

[0002] Airborne cloud particle data processing is a core component of aviation meteorological detection. It processes cloud particle-related data acquired by airborne detection equipment to generate standardized results that serve meteorological research and flight safety. The current industry standard processing flow involves timestamping and integrating cloud particle detection data, flight status data, and airflow monitoring data. It only analyzes the direct correlations between data, matches operational conditions with particle data based on preset rules, and finally standardizes the output by unifying the data format. However, this approach fails to consider the inherent logical connections between the data, the correlation analysis cannot cover potential transitive logic, the operational condition matching is difficult to adapt to complex and ever-changing real-world scenarios, and standardization remains merely at the format level. The final output data fails to accurately reflect the true state of cloud particles and cannot meet the demands of high-precision applications. Summary of the Invention

[0003] In view of this, the present invention provides an airborne cloud particle data processing method, apparatus, and system based on cooperative feature mapping. The technical solution of the embodiments of the present invention is implemented as follows:

[0004] On one hand, this invention provides an airborne cloud particle data processing method based on collaborative feature mapping, comprising: performing temporal semantic anchoring and binding on raw particle data collected by an airborne cloud particle detection device, flight status data collected by an airborne flight status sensor, and airflow monitoring data collected by an airborne airflow monitoring sensor, retaining the original acquisition attributes and timestamps of each data, and generating a multi-dimensional temporal correlation dataset; performing correlation path traversal mining on the multi-dimensional temporal correlation dataset, covering the direct correlation paths between raw particle data and flight status data and airflow monitoring data, as well as the indirect correlation paths transmitted through intermediate data, and generating coupling relationships. The coupled path dataset is used as input to perform dynamic node mapping between airborne flight conditions and raw particle data. Adjustments to the nodes and corresponding path content of the raw particle data under different flight conditions are identified, generating a set of flight condition adaptation mapping content. This set of flight condition adaptation mapping content is then linked to the raw particle data after pseudo-target removal, using a one-to-one link binding based on sample temporal attributes, to generate a link-bound particle dataset. Based on the link-bound particle dataset and the flight condition adaptation mapping content set, cloud microphysics parameters are calculated and flight condition adaptation corrections are performed to generate standardized liquid water content, number concentration, and spectral distribution data.

[0005] On the other hand, embodiments of the present invention provide a data processing apparatus, including: The data binding module is used to perform temporal semantic anchoring and binding on the raw particle data collected by the airborne cloud particle detection equipment, the flight status data collected by the airborne flight status sensor, and the airflow monitoring data collected by the airborne airflow monitoring sensor. It retains the original acquisition attributes and timestamps of each data point, generating a multi-dimensional time-series associated dataset. The data mining module is used to traverse and mine the association paths in the multi-dimensional time-series associated dataset, covering direct association paths between the raw particle data and the flight status data and airflow monitoring data, as well as indirect association paths transmitted through intermediate data, generating a coupled association path dataset. The node mapping module is used to map coupled association paths... The system takes the path dataset as input and performs dynamic node mapping between airborne flight conditions and raw particle data. It sorts out the adjustment nodes and corresponding path contents of the raw particle data under different flight conditions and generates a set of flight condition adaptation mapping contents. The link binding module is used to bind the set of flight condition adaptation mapping contents to the raw particle data after pseudo-target removal one-to-one link based on the sample time sequence attributes, and generate a link-bound particle dataset. The data generation module is used to calculate cloud microphysical parameters and perform flight condition adaptation correction based on the link-bound particle dataset and the set of flight condition adaptation mapping contents, and generate standardized liquid water content, number concentration and spectral distribution data.

[0006] In another aspect, embodiments of the present invention provide a computer system including a memory and a processor. The memory stores a computer program that can run on the processor, and the processor executes the program to implement the steps in the above method.

[0007] This invention provides an airborne cloud particle data processing method based on collaborative feature mapping. By binding and associating three types of heterogeneous data through temporal semantic anchoring, it lays a reliable foundation for subsequent data processing, ensuring both temporal consistency and semantic relevance. Relying on full-range association path mining to cover all potential association logics, it provides realistic support for mapping operating conditions and particle data. Dynamic node mapping breaks through the limitations of preset rules, ensuring that the mapping relationship between operating conditions and particle data perfectly matches the actual association state. Based on the temporal attributes of samples, it completes the precise link binding between the operating condition adaptation mapping content and the original particle data, avoiding matching misalignment problems in conventional operations. Multi-dimensional semantic alignment transformation ultimately generates semantically unified standardized data, comprehensively improving the accuracy and reliability of airborne cloud particle data processing. Attached Figure Description

[0008] Figure 1 This is a schematic diagram illustrating the implementation process of an airborne cloud particle data processing method based on collaborative feature mapping provided by the present invention.

[0009] Figure 2 This is a schematic diagram of the composition structure of a data processing device provided by the present invention.

[0010] Figure 3This is a schematic diagram of the hardware entity of a computer system provided by the present invention. Detailed Implementation

[0011] This invention provides an airborne cloud particle data processing method based on cooperative feature mapping, which can be executed by a processor of a computer system. The computer system can refer to devices with data processing capabilities, such as servers, laptops, and desktop computers.

[0012] Figure 1 This is a schematic diagram illustrating the implementation flow of the airborne cloud particle data processing method based on collaborative feature mapping provided in an embodiment of the present invention, as shown below. Figure 1 As shown, the method includes: Step S100: Perform time-series semantic anchoring and binding on the raw particle data collected by the airborne cloud particle detection device, the flight status data collected by the airborne flight status sensor, and the airflow monitoring data collected by the airborne airflow monitoring sensor, retain the original collection attributes and timestamps of each data, and generate a multi-dimensional time-series associated dataset.

[0013] Airborne cloud particle detection equipment may include, for example, the ZBT-LC laser cloud particle detection system, which is an airborne atmospheric physics detection system that can be used for cloud physics research and aircraft-based weather modification operations. This detection system includes the ZBT-LC-01 cloud particle spectrometer, the ZBT-LC-02 cloud particle meter, and the ZBT-LC-03 precipitation particle meter.

[0014] Raw particle data consists of unprocessed information about cloud particles directly collected by airborne cloud particle detection equipment, such as particle size, quantity, and distribution. Flight status data, collected by airborne flight status sensors, reflects the aircraft's flight status and includes parameters such as speed, altitude, attitude (pitch angle, roll angle, etc.), and heading. These parameters affect cloud particle detection. Airflow monitoring data, acquired by airborne airflow monitoring sensors, provides information about the airflow around the aircraft, including airflow speed, direction, and turbulence intensity. Airflow conditions influence the distribution and movement of cloud particles.

[0015] In one implementation, step S100 may specifically include the following steps S110 to S160: Step S110: Perform linear format conversion on the timeline of the raw particle data collected by the airborne cloud particle detection equipment, the flight status data collected by the airborne flight status sensor, and the airflow monitoring data collected by the airborne airflow monitoring sensor. Convert the start and end times of each data point into a unified continuous timeline scale, record the start and end positions of each data point on the continuous timeline, and obtain the timeline scale of the raw particle data, the timeline scale of the flight status data, and the timeline scale of the airflow monitoring data.

[0016] Because different sensors may have different sampling frequencies and time recording methods, the acquisition times of raw particle data, flight status data, and airflow monitoring data may differ. To facilitate subsequent data correlation and analysis, these different time formats need to be converted into a unified continuous timeline scale. Specifically, a unified timeline reference must first be determined. A standard time system, such as the GPS time system, can be chosen. Then, for each data point, its start and end times are converted into scale values ​​on this unified timeline. For example, a time conversion algorithm can be used to convert the times recorded by different sensors into corresponding values ​​on the unified timeline according to certain conversion rules. For instance, if the start time of a piece of raw particle data is a certain moment in local time, the offset between local time and GPS time can be looked up to convert it into GPS time, and then into scale values ​​on the unified timeline.

[0017] Step S120: Match the time axis nodes of the particle raw data time axis, the flight status data time axis, and the airflow monitoring data time axis. Select the position where the scales on the three time axes completely overlap as the overlapping nodes. Mark the time axis scale value and corresponding data source of each overlapping node to obtain the time axis overlapping node set.

[0018] This step can be performed using a traversal comparison method. First, sort the timeline scales of the raw particle data, flight status data, and airflow monitoring data, ensuring they are arranged in ascending order. Then, iterate through the scale values ​​on the three timelines, comparing whether they are identical. When a scale value is found to be identical on all three timelines, that position is identified as a coincident node. Labeling the timeline scale value and corresponding data source of each coincident node is to clarify the time point represented by each coincident node and its corresponding data source. This information can be stored in a data structure, such as a list, where each element is a tuple containing the timeline scale value and the data source identifier. For example, for a coincident node with a timeline scale value of t, a corresponding raw particle data source identifier of "P", a flight status data source identifier of "F", and an airflow monitoring data source identifier of "A", then (t, "P", "F", "A") can be added as an element to the set of coincident timeline nodes.

[0019] Step S130: Using the set of overlapping nodes on the time axis as input, traverse the original particle data, flight status data, and airflow monitoring data respectively, extract the content of the corresponding overlapping nodes in each data, arrange the extracted content according to the time axis scale order of the overlapping nodes, and obtain the data subset corresponding to the node.

[0020] In one implementation, step S130 may specifically include the following steps S131 to S136: Step S131: Parse the set of overlapping nodes on the timeline, extract the timeline scale value and corresponding data source identifier of each overlapping node, organize them into a list of corresponding node scales and sources, mark the order of each list item on the timeline, and obtain the list of corresponding sources of overlapping nodes.

[0021] When extracting timeline scale values ​​and corresponding data source identifiers, data parsing algorithms can be used. For example, if the set of overlapping timeline nodes is stored as a list, where each element is a tuple containing a timeline scale value and a data source identifier, then the information in each tuple can be extracted sequentially by traversing the list. The extracted information is then organized into a list of node scale and source correspondences. This list can be a two-dimensional array, where each row represents overlapping node information, the first column stores the timeline scale value, and the subsequent columns store the source identifiers for the original particle data, flight status data, and airflow monitoring data, respectively. Marking the order of each list item on the timeline clarifies the sequential relationship of each overlapping node. The list of node scale and source correspondences can be sorted according to the timeline scale value, and then a sequence number can be added to each item.

[0022] Step S132: Based on the list of sources corresponding to overlapping nodes, traverse the original particle data, query the entries in each data entry whose collection start and end range includes the time axis scale value of overlapping nodes, extract the content of the corresponding scale value in the entry, record the position and source of the extracted content, and obtain the content corresponding to the original particle nodes.

[0023] Using the list of sources corresponding to overlapping nodes as a basis means processing the raw particle data according to the time axis scale values ​​and data source information in the list. Traversing the raw particle data involves checking each record in the raw particle data in turn to determine whether its collection start and end range includes the time axis scale values ​​of overlapping nodes.

[0024] When querying entries containing timeline scale values ​​of overlapping nodes, a range-based algorithm can be used. Each record in the raw particle data has a start and end time for data collection. The timeline scale value of the overlapping node is compared to this time range. If the scale value falls within the collection start and end range, the entry is considered to contain the overlapping node. When extracting the content corresponding to the scale value from this entry, the corresponding raw particle data content, such as particle size and quantity, is located based on the data storage structure and format.

[0025] Recording the location and source of the extracted content facilitates subsequent data traceability and management. Location information can be the index of the content within the original particle data, while source information specifies the particular acquisition device or sensor from which the data originated. The extracted content, location information, and source information are stored in a new data structure, such as a list, where each element is a tuple containing the content, location, and source. This yields the content corresponding to the original particle node.

[0026] Step S133: Based on the list of sources corresponding to overlapping nodes, traverse the flight status data, query the entries in each data entry whose collection start and end range includes the time axis scale value of overlapping nodes, extract the content of the corresponding scale value in the entry, record the position and source of the extracted content, and obtain the content corresponding to the flight status nodes.

[0027] Once an entry containing the timeline scale value of the overlapping node is found, the content corresponding to that scale value is extracted, which includes parameters such as the aircraft's speed, altitude, pitch angle, and roll angle at that time point. The location and source of the extracted content are recorded. The location information can be the storage location of the content in the flight status data, and the source information indicates which flight status sensor collected the data. The extracted content, location information, and source information are stored in a data structure, such as a list, where each element is a tuple containing the content, location, and source, thus obtaining the content corresponding to the flight status node.

[0028] Step S134: Based on the list of sources corresponding to overlapping nodes, traverse the airflow monitoring data, query the entries in each data entry whose collection start and end range includes the time axis scale value of overlapping nodes, extract the content of the corresponding scale value in the entry, record the location and source of the extracted content, and obtain the content corresponding to the airflow monitoring nodes.

[0029] If a matching entry is found, extract the corresponding scale value from that entry, i.e., the airflow velocity, direction, and other data at that time point. Record the location and source of the extracted content. The location information indicates the storage location of the content in the airflow monitoring data, and the source information indicates which airflow monitoring sensor collected the data. Store this information in a list, where each element is a tuple containing the content, location, and source, thus obtaining the content corresponding to the airflow monitoring node.

[0030] Step S135: Arrange the contents corresponding to the original particle node, the contents corresponding to the flight status node, and the contents corresponding to the airflow monitoring node one by one according to the order of the overlapping node source corresponding list, ensuring that the contents of the same overlapping node are arranged in the same position, and obtain an ordered set of node corresponding contents.

[0031] For example, a synchronous traversal method can be used. Starting from the first entry in the list of overlapping node sources, find the content corresponding to the original particle node, the flight status node, and the airflow monitoring node in sequence, and arrange them in the same row or in the same position. For example, for the first overlapping node, arrange its corresponding content for the original particle node, the flight status node, and the airflow monitoring node in sequence in one row.

[0032] Step S136: Perform a content integrity check on the content set corresponding to the ordered nodes, supplement the missing content corresponding to the nodes, correct the entries with content extraction errors, and obtain the data subset corresponding to the nodes.

[0033] During integrity checks, preset rules can be used to determine content completeness. For example, for each overlapping node, check if its corresponding raw particle data, flight status data, and airflow monitoring data are all present; if any are missing, they are marked as missing content. The missing content for a node can be supplemented by querying the original data source or by using interpolation algorithms to estimate based on the content of adjacent nodes. Entries with incorrect content extraction need to be corrected. The erroneous content can be re-extracted or adjusted based on the original data and their logical relationships.

[0034] During the correction process, data verification and error correction algorithms can be employed. For example, if a clearly unreasonable speed value appears in the flight status data, it can be compared with the speed values ​​at adjacent time points, and the judgment and correction can be made in conjunction with the aircraft's flight performance. After content integrity checks, supplementing missing content, and correcting erroneous entries, the resulting set is the data subset corresponding to each node, containing accurate and complete data content corresponding to each overlapping node.

[0035] Step S140: Associate and bind the data subsets corresponding to the nodes one by one, combine the original particle data content, flight status data content and airflow monitoring data content corresponding to the same overlapping node, record the node correspondence relationship of the combination, and obtain the dataset after node combination.

[0036] Association binding combines the original particle data, flight status data, and airflow monitoring data according to the correspondence of overlapping nodes, forming a complete data unit. During this step, the data subsets corresponding to each node are traversed. For each overlapping node, its corresponding original particle data, flight status data, and airflow monitoring data are combined into a new data record. Data merging methods can be used, such as storing the three types of data in a new list or dictionary, with each element or key-value pair corresponding to data from a different data source. Recording the node correspondence of the combinations clarifies which overlapping node's content constitutes each combined data record; this can be achieved by adding node identifiers to the new data records.

[0037] For example, for a coincident node, the original particle data is the size and number of particles, the flight status data is the speed and altitude of the aircraft, and the airflow monitoring data is the speed and direction of the airflow. These contents are combined into a new data record, and the identifier of the coincident node is added to indicate that this combined data corresponds to that node.

[0038] Step S150: For the dataset after node combination, the original collection attributes are attached one by one. The original attributes such as the collection device identifier and collection environment description of each data are attached to the specified fields of the corresponding combination content to ensure that the attributes and content correspond one by one, and the combined dataset with the original attributes attached is obtained.

[0039] During this step, for each data record in the combined dataset, its corresponding original attributes, such as the acquisition device identifier and acquisition environment description, are attached to designated fields. This attachment can be achieved through data mapping, for example, by pre-establishing a data dictionary and associating the acquisition device identifier and acquisition environment description with specific fields in the data records. For each data record, based on its source information, the corresponding original attributes are found in the data dictionary and added to the designated fields. For example, for a combined data record, where the original particle data was acquired by a ZBT-LC-01 cloud particle spectrometer, the flight status data by a specific flight status sensor, and the airflow monitoring data by an airflow monitoring sensor, these acquisition device identifiers are attached to the corresponding designated fields in the combined data record. Simultaneously, descriptions of the acquisition environment, such as atmospheric temperature and humidity, are also attached to the corresponding fields.

[0040] Step S160: Add timestamp markers to the combined dataset with the original attributes, convert the time axis scale value of each overlapping node into timestamp format, add it to the header field of the corresponding combined content, arrange all combined content in the order of time axis scale, and obtain a multi-dimensional time-series related dataset.

[0041] In this step, the timeline tick values ​​of each overlapping node are first converted to timestamp format. A time conversion algorithm can be used to convert the timeline tick values ​​to timestamps based on their correspondence with a standard time system. For example, if the timeline tick values ​​are based on a local time system, the offset between the local time and the standard time (such as UTC) can be used to convert them to standard timestamps. Then, the converted timestamps are added to the header field of the corresponding combined content. This can be achieved by modifying the data record structure, adding a dedicated field for storing timestamps to the header, and assigning the timestamp values ​​to that field. Arranging all combined content in timeline order is to organize the data chronologically, facilitating subsequent time-series analysis. A sorting algorithm can be used to sort the combined dataset, sorting it from smallest to largest timestamp field value.

[0042] Step S200: Perform path traversal mining on the multi-dimensional time-series correlation dataset, covering the direct correlation paths between the original particle data and flight status data and airflow monitoring data, as well as the indirect correlation paths transmitted through intermediate data, to generate a coupled correlation path dataset.

[0043] In one implementation, step S200 may specifically include the following steps S210 to S260: Step S210: Analyze the multi-dimensional time-series correlation dataset, extract all data entries and their corresponding node combinations, organize them into a list of corresponding entries and node relationships, label the data type and associated nodes of each entry, and obtain the core list of the correlation dataset.

[0044] When extracting all data entries and their corresponding node combinations, it is necessary to traverse the dataset and extract each data entry and its corresponding overlapping node information. Data parsing algorithms can be used to separate data entries and node combinations based on the dataset's storage structure and format. A list of corresponding entries and node relationships is then compiled. This list can be a two-dimensional array, where each row represents a data entry, and the columns store the data entry content, the corresponding overlapping node identifier, and other information. Labeling each entry with its data type and associated nodes clarifies which data source each entry belongs to (raw particle data, flight status data, or airflow monitoring data) and which other nodes it is associated with. This labeling can be done using predefined data type identifiers and association rules. For example, for a data entry that is about particle size, its data type is labeled as "raw particle data," and its associated flight status data nodes and airflow monitoring data nodes are labeled according to the node combination relationships.

[0045] Step S220: Based on the core list of the associated dataset, traverse the original particle data entries, flight status data entries, and airflow monitoring data entries, query the direct node combination relationships between the two, record the starting entry, ending entry, and corresponding node content of each direct relationship, and obtain the set of direct associated paths.

[0046] Using the core list of associated datasets as a basis means operating according to information such as data entries, node combinations, data types, and associated nodes in this list. Traversing the particle raw data entries and flight status data entries, and airflow monitoring data entries, involves sequentially checking the particle raw data entries and flight status data entries, and the particle raw data entries and airflow monitoring data entries, to find direct relationships between them. When querying directly corresponding node combinations, the associated node information for each entry in the core list of associated datasets is used to determine whether the particle raw data entries are directly related to the flight status data entries and airflow monitoring data entries. Recording the starting entry, ending entry, and corresponding node content for each direct relationship clarifies the specific information of the direct association path. The starting entry is the particle raw data entry, the ending entry is the corresponding flight status data entry or airflow monitoring data entry, and the corresponding node content is the specific data content at the overlapping node. This information can be stored in a list, where each element is a tuple containing the starting entry, ending entry, and corresponding node content.

[0047] Step S230: Starting with the set of directly related paths, traverse the entries in the core list of the related dataset that are not included in the directly related paths, query the node combination relationships passed through other entries, record the intermediate entries, the passing order and the corresponding content of the nodes for each passing relationship, and obtain the set of indirect related paths.

[0048] In one implementation, step S230 may specifically include the following steps S231 to S236: Step S231: Parse the set of directly related paths, extract the starting and ending entries and corresponding node combinations of each path, organize them to obtain a list of core information of direct paths, mark the node correspondence of each entry, and obtain the core list of direct paths.

[0049] Parsing the set of directly related paths involves detailed analysis and processing to extract key information. This set contains direct connections between raw particle data, flight status data, and airflow monitoring data. Each path consists of a start entry, an end entry, and corresponding node content. Extracting the start entry, end entry, and corresponding node combinations for each path requires traversing the set and extracting this information for each path. Data parsing algorithms can be used to separate the start entry, end entry, and node combinations based on the set's storage structure and format. A core information list of the direct paths is then generated. This list can be a two-dimensional array, where each row represents a directly related path, and the columns store the start entry, end entry, and node combinations, respectively. Labeling the node correspondences for each entry clarifies the specific relationships between the start and end entries and other nodes in each directly related path. This labeling can be done by analyzing node combinations and predefined association rules. For example, for a directly related path, the overlapping nodes corresponding to its start and end entries, as well as their connections to other relevant nodes, can be labeled.

[0050] Step S232: Based on the core list of direct paths, traverse all entries in the core list of the associated dataset, filter out the unincluded entries that have a node combination relationship with the starting and ending entries in the core list of direct paths, label the data type of each filtered entry, and obtain the intermediate candidate entry set.

[0051] When filtering out unincluded entries that have node combination relationships with the start and end entries in the core list of direct paths, it is necessary to make a judgment based on the node combination relationship and association rules. For example, if an entry not included in the direct association path has the same overlapping node as a start or end entry in the core list of direct paths, or if there are other preset association conditions, then it is considered that the entry has a node combination relationship with the start or end entry, and it is filtered out.

[0052] Labeling each filter item with its data type clarifies which data source each intermediate candidate item belongs to (raw particle data, flight status data, or airflow monitoring data). This labeling can be done using predefined data type identifiers. For example, for a filtered item that relates to airflow velocity, its data type could be labeled as "airflow monitoring data."

[0053] Step S233: Traverse each entry in the intermediate candidate entry set, query the node combination relationship between the entry and the starting and ending entries in the direct path core list, record the direction of relationship transmission and the corresponding content of the nodes, and obtain the intermediate entry association relationship.

[0054] When querying node combination relationships, the system determines whether there is a relationship between intermediate candidate entries and starting or ending entries based on the node combination information in the core list of the associated dataset and the core list of direct paths. For example, if the overlapping nodes corresponding to intermediate candidate entries overlap with the overlapping nodes corresponding to the starting or ending entries, or if other preset association rules are satisfied, then a node combination relationship is considered to exist between them.

[0055] Recording the direction of data transfer and the corresponding node content is crucial for accurately describing the association between intermediate entries and the starting and ending entries. The transfer direction clarifies the flow of data within the association, such as whether it's from the starting entry through intermediate entries to the ending entry, or vice versa. The node content refers to the specific data content corresponding to each node in the association. This information can be stored in a list, where each element is a tuple containing the intermediate entry, starting entry, ending entry, transfer direction, and node content. After traversing and querying all intermediate candidate entries, the resulting set represents the intermediate entry associations, containing the association information between the intermediate candidate entries and the starting and ending entries in the direct path core list.

[0056] Step S234: Based on the relationship between intermediate entries, construct the transmission path from the starting entry through intermediate candidate entries to the ending entry, mark the starting, intermediate and ending entries of the path, record the corresponding content of the nodes of each path, and obtain the preliminary indirect association path.

[0057] In one implementation, step S234 may specifically include the following steps S2341 to S2346: Step S2341: Analyze the relationships between intermediate entries, extract the node combination direction and corresponding content between intermediate candidate entries and starting and ending entries, organize them into a list of corresponding association directions and content, mark the transmission logic of each list item, and obtain the core list of intermediate associations.

[0058] Parsing the relationships between intermediate entries involves a detailed analysis and processing of these relationships to extract key information. These relationships include the connections between intermediate candidate entries and the starting and ending entries in the core list of direct paths, such as the direction of transmission and the corresponding node content.

[0059] When extracting the node combination direction and corresponding content between intermediate candidate entries and the starting and ending entries, it is necessary to traverse the relationships between intermediate entries and extract the transmission direction and corresponding node content in each relationship. A data parsing algorithm can be used to separate the transmission direction and corresponding node content based on the storage structure and format of the relationship. This results in a list of corresponding association directions and content, which can be a two-dimensional array where each row represents an association and the columns store the transmission direction and corresponding node content.

[0060] Labeling the transmission logic of each list item clarifies the data transmission method and rules within each relationship. This can be done by analyzing the transmission direction and the content corresponding to the nodes, combined with predefined transmission logic rules. For example, if the transmission direction is from the starting item to the intermediate candidate item and then to the ending item, and the content corresponding to the nodes conforms to a specific logical relationship, then its transmission logic is labeled as "forward transmission."

[0061] Step S2342: Based on the intermediate association core list, determine the transmission order from the starting item to the intermediate candidate item and then to the ending item, mark the node correspondence and transmission direction of the order, and obtain the path transmission order rules.

[0062] Using the core list of intermediate relationships as a basis means determining the transmission order based on information such as the association direction, node correspondence, and transmission logic within this list. When determining the transmission order, the starting item, intermediate candidate items, and ending item are arranged in a logical order according to the transmission direction and logic in the core list of intermediate relationships. For example, if the transmission logic is "forward transmission," the starting item is placed first, the intermediate candidate item second, and the ending item third. Annotating the node correspondences and transmission directions in the order is to accurately describe the association between each node and the direction of data flow in the transmission order. This annotation can be done by analyzing the node correspondences and transmission directions. For example, annotating the overlapping nodes corresponding to the starting item and intermediate candidate items, as well as the data transmission direction between them. After these operations, the resulting rule is the path transmission order rule, which includes information such as the transmission order from the starting item to the intermediate candidate item and then to the ending item, the node correspondences, and the transmission direction.

[0063] Step S2343: According to the path transmission order rules, concatenate the starting item, intermediate candidate items, ending item and corresponding node combination content, record the order of the concatenated items and the corresponding content of the nodes, mark the transmission logic of the path, and obtain the content of a single indirect path.

[0064] During the concatenation process, starting with the initial entry, intermediate candidate entries and the final entry are added sequentially, while simultaneously connecting their corresponding node combinations. Recording the concatenated entry order and corresponding node content is crucial for accurately describing the structure and data information of this indirect path. The concatenated entry order and corresponding node content can be stored in a list, where the elements are, in order, the initial entry, intermediate candidate entries, the final entry, and their corresponding node combinations.

[0065] The purpose of annotating the transmission logic of a path is to clarify the transmission method and rules of data within that indirect path. Annotations can be made based on the transmission logic within the path transmission order rules. For example, if the transmission logic is "forward transmission," then the transmission logic for that single indirect path is labeled as "forward transmission."

[0066] Step S2344: Traverse all entries in the intermediate candidate entry set, repeat the above path concatenation operation, generate multiple single indirect path contents, and mark the start, middle and end entries of each path to obtain multiple indirect path contents.

[0067] When generating the content of each individual indirect path, the starting entry, intermediate candidate entries, ending entry, and their corresponding node combinations are concatenated according to the path transmission order rules. The order of the concatenated entries and the corresponding node content are recorded, and the transmission logic of the path is marked. Marking the starting, intermediate, and ending entries of each path is to clarify the structure and components of each indirect path. The content of each individual indirect path can be stored in a list, where each element is a tuple containing the starting entry, intermediate entries, ending entry, and the corresponding node content. After traversing all intermediate candidate entries and performing path concatenation operations, the resulting set is the content of multiple indirect paths, containing information about multiple different indirect paths from starting entries through intermediate candidate entries to ending entries.

[0068] Step S2345: Perform path deduplication on multiple indirect path contents, remove path entries with completely identical content, and merge paths with the same starting entry, intermediate entry, and ending entry but different node descriptions to obtain deduplicated indirect path contents.

[0069] Deduplication of multiple indirect path contents aims to eliminate duplicate path entries in the path set and avoid redundant information. During the generation of multiple indirect path contents, path entries with identical content may appear, or paths may have the same starting, intermediate, and ending entries but different node descriptions.

[0070] When removing path entries with identical content, a comparison algorithm can be used to compare each path in multiple indirect paths one by one. If two paths have identical starting, intermediate, ending, and node contents, they are considered duplicate paths, and one of them is removed. Merging paths with identical starting, intermediate, and ending entries but different node descriptions is to integrate these similar paths. By analyzing the node contents, identical parts can be merged, and different parts retained, forming a more comprehensive path description.

[0071] Step S2346: Structure the deduplicated indirect path content to obtain path entries in a unified format, mark the node correspondence and transmission logic of each path, and obtain the preliminary indirect association path.

[0072] When performing structured organization, data formatting algorithms can be used to transform each path in the deduplicated indirect path content according to a preset path entry format. For example, the starting, intermediate, and ending entries of a path, along with the corresponding node content, can be arranged in a specific order to form a unified path entry structure. Labeling the node correspondences and transmission logic of each path clarifies the specific relationships between nodes and the data transmission method within each path. This labeling can be performed by analyzing the node correspondences and preset transmission logic rules of the path.

[0073] Step S235: Traverse all direct path core list entries, repeat the above path construction operation, generate multiple preliminary indirect association paths, and mark the start, middle and end entries of each path to obtain a preliminary indirect path set.

[0074] Traversing all entries in the core list of direct paths is to construct indirect paths for each directly related path. The core list of direct paths contains information such as the starting and ending entries, node combinations, and node correspondences for the direct paths between the original particle data, flight status data, and airflow monitoring data.

[0075] Repeating the path construction operation described above follows the method outlined in steps S232-S234. Based on the starting and ending entries in the core list of direct paths, intermediate candidate entries are identified, and indirect association paths are constructed from the starting entry through intermediate candidate entries to the ending entry. During the construction process, multiple preliminary indirect association paths are generated.

[0076] Labeling the start, middle, and end entries of each path clarifies the structure and components of each indirect path. Each initial indirect path can be stored in a list, where each element is a tuple containing the start, middle, and end entries, along with the corresponding node information.

[0077] Step S236: Perform a path validity check on the preliminary indirect path set, remove path entries that cannot form a complete transmission, correct errors in the path descriptions, and obtain the indirect association path set.

[0078] During validity checks, each path is judged to be valid according to preset path validity rules. For example, it checks whether there is a reasonable relationship between the starting, intermediate, and ending entries of the path, and whether the content corresponding to the nodes is complete and logical. If a path cannot form a complete transmission, such as due to missing intermediate entries or unreasonable transmission direction, it is marked as an invalid path and removed from the set.

[0079] Errors in the path descriptions need to be corrected. The incorrect path descriptions can be reconstructed or adjusted based on the original data and their logical relationships. For example, if there are errors in the content corresponding to nodes in the path description, they can be corrected by querying the original dataset and association rules. After path validity checks, invalid path removal, and error correction, the resulting set is the indirect association path set, containing accurate and valid indirect association path information from the starting entry through intermediate candidate entries to the ending entry.

[0080] Step S240: Merge the directly associated path set and the indirectly associated path set, remove path entries with identical content, and merge paths with the same starting and ending entries but different intermediate descriptions to obtain the merged associated path set.

[0081] During the merging process, the path entries in the two sets are first summarized. Then, path entries with identical content are removed to avoid redundancy. A comparison algorithm can be used to compare the summarized path entries one by one. If the start entries, end entries, and corresponding node contents of the two paths are completely identical, one of them is discarded.

[0082] Merging paths with identical starting and ending entries but different intermediate descriptions aims to integrate these similar paths. By analyzing the intermediate descriptions, identical parts can be merged while different parts are retained, forming a more comprehensive path description. For example, for two paths with identical starting and ending entries but different intermediate descriptions, their intermediate descriptions can be merged, removing duplicate descriptions and retaining their unique information. After path merging, deduplication, and integration, the resulting set is the merged associated path set, containing integrated information on direct and indirect association paths between raw particle data, flight status data, and airflow monitoring data.

[0083] Step S250: Perform path hierarchy sorting on the merged associated path set, classify according to the number of items involved in the path, and mark the hierarchical relationship and transmission logic of each path to obtain a hierarchical associated path set.

[0084] For example, a direct path containing only a start and an end entry can be classified as a first-level path, while an indirect path containing a start entry, an intermediate entry, and an end entry can be classified as a second-level path. Labeling the hierarchical relationship and transmission logic of each path clarifies the position of each path within the hierarchical structure and the method of data transmission. Labeling can be performed by analyzing the number of entries and transmission direction of each path, combined with predefined hierarchical relationships and transmission logic rules.

[0085] For example, for a second-level path, its hierarchical relationship is labeled as "second-level" and its transmission logic is "forward transmission". After the path hierarchy is sorted out and labeled, the resulting set is a hierarchical set of related paths, which includes the hierarchical relationship, transmission logic and classification information of the paths in the merged set of related paths.

[0086] Step S260: Standardize the hierarchical association path set, arrange the path content according to the starting entry type, and supplement the node descriptions of each path to obtain the coupled association path dataset.

[0087] Arranging path content by starting entry type involves classifying and sorting path entries according to their data source (raw particle data, flight status data, or airflow monitoring data). A sorting algorithm can be used to arrange the path entries in ascending or descending order based on their starting entry type. Adding a description of each path's corresponding nodes completes the information for each path entry, making it more comprehensive and accurate. This can be done by querying the original dataset and association rules to obtain a detailed description of the node content for each path.

[0088] Step S300: Using the coupled associated path dataset as input, perform dynamic node mapping generation between airborne flight conditions and particle raw data, sort out the adjustment nodes and corresponding path contents of particle raw data under different flight conditions, and generate a set of conditions-adaptive mapping contents.

[0089] In one implementation, step S300 may specifically include the following steps S310 to S360: Step S310: Parse the coupled path dataset, extract all path entries and their corresponding entry types and node combination relationships, organize them into a corresponding list of path and node relationships, mark the working condition association probability of each entry, and obtain the core list of coupled paths.

[0090] Parsing the coupled path dataset involves detailed analysis and processing of the dataset to extract key information. The coupled path dataset contains information about the relationships between raw particle data, flight status data, and airflow monitoring data, such as path entries, entry types, and node combination relationships.

[0091] When extracting all path entries and their corresponding entry types and node combinations, it is necessary to traverse the coupled path dataset and extract each path entry and its corresponding entry type and node combination. Data parsing algorithms can be used to separate the path entries, entry types, and node combinations based on the dataset's storage structure and format. This results in a list of corresponding path and node relationships, which can be a two-dimensional array where each row represents a path entry, and the columns store the path entry content, entry type, and node combination relationship, respectively.

[0092] Labeling the operational condition association probability of each entry is to assess the degree of association between each path entry and different flight operational conditions. This can be done by analyzing the content, entry type, and node combination relationships of the path entries, combined with predefined operational condition association rules. For example, for a path entry involving flight speed and particle quantity, if changes in flight speed are closely related to a certain flight operational condition (such as cruise), and the particle quantity changes with changes in flight speed, then the operational condition association probability of this entry is labeled as "high." After these operations, the resulting list is the core list of coupled paths, containing key information such as path entries, entry types, node combination relationships, and operational condition association probabilities.

[0093] Step S320: Based on the core list of coupling paths, sort out the contents of the items corresponding to the airborne flight conditions, mark the flight status data item type and node combination relationship corresponding to each condition, and obtain the set of items corresponding to the conditions.

[0094] During this step, for each flight condition, the core list of coupling paths is traversed to filter out path entries with a high probability of condition association. For the selected path entries, the types of flight state data entries involved are further analyzed, such as parameters like aircraft speed, altitude, and attitude, as well as the combination relationships between these entries and other data nodes. Labeling the types of flight state data entries and node combination relationships corresponding to each flight condition clearly presents the connections between data under different flight conditions. A classification and labeling method can be used to create a corresponding entry list for each flight condition, where each element records the relevant flight state data entry type and node combination relationship. For example, for the cruise condition, path entries related to flight speed stability are filtered out, and the data types of flight speed involved in these entries and their combination relationships with certain nodes in the original particle data are labeled, such as the correlation between flight speed and the number of cloud particles.

[0095] Step S330: Based on the core list of coupling paths and the set of corresponding items for the working conditions, query the associated paths between the flight working condition items and the original particle data items, mark the node combination relationship and transmission logic of the paths, and obtain the associated paths between the working conditions and the particles.

[0096] When querying associated paths, the flight status data entries corresponding to each flight condition are retrieved from the set of entries corresponding to the flight conditions. Then, the paths between these entries and the original particle data entries are searched in the core list of coupled paths. For the found associated paths, their node combination relationships and transmission logic need to be labeled. The node combination relationship describes the connection method and correspondence between the various nodes in the path (flight condition entries, flight status data entries, airflow monitoring data entries, and original particle data entries), while the transmission logic describes the flow direction and influence mechanism of data between these nodes.

[0097] For example, during an aircraft climb, the increase in altitude in the flight status data leads to changes in airflow temperature and pressure. Airflow monitoring data reflects these changes, which in turn affect the distribution of cloud particles, thus altering the original particle data. In this correlation path, the relationship between the labeled nodes is: Flight Condition (Climb) - Flight Status Data (Altitude) - Airflow Monitoring Data (Temperature, Pressure) - Original Particle Data (Cloud Particle Distribution). The transmission logic is forward, meaning that changes in the flight condition affect the original particle data through the flight status data and airflow monitoring data.

[0098] Step S340: Based on the working conditions and particle association paths, sort out the node adjustment content corresponding to the original particle data entries under different flight working conditions, mark the adjusted node positions and corresponding path content, and obtain the working condition adaptation adjustment content.

[0099] In one implementation, step S340 may specifically include the following steps S341 to S346: Step S341: Analyze the working conditions and particle association paths, extract the flight working condition entries, particle raw data entries and corresponding node combination relationships for each path, organize them to obtain the core list of working conditions and particle paths, mark the node position of each entry, and obtain the core list of working condition and particle paths.

[0100] When extracting the flight condition entries, particle raw data entries, and corresponding node combination relationships for each path, it is necessary to traverse the paths associated with the conditions and particles, extracting this information from each path. A data parsing algorithm can be used to separate the flight condition entries, particle raw data entries, and node combination relationships based on the storage structure and format of the associated paths. This results in a core list of flight condition and particle paths, which can be a two-dimensional array where each row represents an associated path, and the columns store the flight condition entries, particle raw data entries, and node combination relationships, respectively.

[0101] Labeling the node positions of each entry is to clarify the specific node positions in the original particle data entry that are associated with the flight condition. The relevant node positions of the original particle data entries in each associated path can be determined by analyzing the node combination relationships and the structure of the original particle data. For example, for an associated path, after analyzing its node combination relationships, it can be determined that the 3rd to 7th data nodes in the original particle data entry are related to that path, and these node positions are labeled.

[0102] Step S342: Based on the core list of working condition particle paths, traverse different flight working condition entries, query the original particle data entries and node combination relationships corresponding to each working condition entry, mark the specific position of the node in the original particle data entry, and obtain the particle node corresponding to the working condition.

[0103] Based on the core list of particle paths for each flight condition, the system traverses different flight condition entries to query the corresponding original particle data entries and node combinations for each flight condition in detail. The core list of particle paths for each flight condition already provides the core information and node locations of the associated paths. By traversing the flight condition entries, the specific relationships between the original particle data under each flight condition can be further clarified.

[0104] During the query process, for each flight condition entry, the corresponding original particle data entry and node combination relationship are identified from the core list of particle paths for that condition. Then, based on the marked node positions, the specific location of the node within the original particle data entry is further determined. For example, for the cruise condition, after finding the corresponding original particle data entry, the 10th to 15th data elements in that entry are determined to be the specific node associated with the cruise condition based on the node position information. Marking the specific location of the node within the original particle data entry accurately pinpoints the impact of each flight condition on the original particle data. A record list can be used to create a corresponding particle node list for each flight condition, where each element records the associated original particle data entry and the node's specific location within that entry.

[0105] Step S343: Based on the particle nodes corresponding to the working conditions, analyze the changes in the content of the original particle data entries under different working conditions, query the content differences of the same particle node under different working conditions, mark the specific manifestations and locations of the differences, and obtain the content differences of the particle nodes.

[0106] In one implementation, step S343 may specifically include the following steps S3431 to S3436: Step S3431: Analyze the particle nodes corresponding to the working conditions, extract the original particle data entry identifier and node position corresponding to each working condition, organize them into a list of corresponding working conditions and particle nodes, label the working condition type of each entry, and obtain a list of corresponding working condition particle nodes.

[0107] When extracting the particle raw data entry identifier and node position corresponding to each flight condition, it is necessary to traverse the particle nodes corresponding to each flight condition and extract this information for each condition. A data parsing algorithm can be used to separate the particle raw data entry identifier and node position based on the storage structure and format of the particle nodes corresponding to each flight condition. A list of correspondences between flight conditions and particle nodes is then generated. This list can be a two-dimensional array, where each row represents the particle node information for a flight condition, and the columns store the particle raw data entry identifier and node position, respectively. Labeling each entry with its flight condition type is to clarify the corresponding flight condition for each entry. This can be done by querying the flight condition information in the particle nodes corresponding to each flight condition and adding a corresponding flight condition type label to each entry. For example, for an entry, its flight condition type can be labeled as "cruising".

[0108] Step S3432: Based on the list of particle nodes corresponding to working conditions, extract the original data entries of particles under different working conditions corresponding to the same particle node, organize them to obtain a set of multiple working condition contents for the same node, label the working condition type and node position of each content, and obtain a set of multiple working condition node contents.

[0109] During the extraction process, for each particle node, all corresponding working condition entries are identified from the list of working condition particle nodes, and then the original particle data content corresponding to these entries is extracted. For example, for a particle node representing the size of cloud particles, the content related to the cloud particle size is extracted from the original particle data corresponding to different working conditions such as cruise, climb, and descent. This results in a set of multi-working-condition content for the same node. This set can be a list, where each element is a tuple containing the content of the particle node under different working conditions.

[0110] Labeling each content with its working condition type and node position is crucial for accurately recording the working condition corresponding to each content and its location within the original particle data entry. This can be done by querying the list of particle nodes corresponding to each working condition, allowing you to add working condition type and node position labels to each content. For example, for a content, label its working condition type as "climbing," and its node position as the 5th position of that particle node in the original particle data entry.

[0111] Step S3433: Compare the content in the multi-condition node content set segment by segment, query the specific description differences corresponding to the node positions in the content, mark the start and end positions of the differences, and obtain the node content difference positions.

[0112] The purpose of comparing the content of a multi-condition node content set segment by segment is to identify the specific differences in the content of the same particle node under different conditions. The multi-condition node content set contains the content information of the same particle node under different conditions, and the differences in content can be discovered by comparing segment by segment.

[0113] During the comparison process, for each particle node, its content under different operating conditions is compared segment by segment. For example, for a particle node representing the velocity of cloud particles, its content under cruising and turning conditions is compared, and the differences in data values, data characteristics, etc., are checked segment by segment. The specific differences in the descriptions corresponding to the node positions in the query content are identified to determine the specific manifestations of the differences, such as increases or decreases in values, changes in data format, etc.

[0114] Step S3434: Based on the location of the node content difference, extract the content at the difference location, organize it into a corresponding list of difference content, mark the working condition type and node location of each difference content, and obtain the node difference content list.

[0115] During the extraction process, for each particle node, the content at the difference position is extracted from the multi-condition node content set based on the start and end positions of the difference position in the node content. For example, for a particle node, based on the difference position information, the difference content from the 5th to the 8th character in its content under cruise and climb conditions is extracted. A corresponding list of difference content is then obtained. This list can be a list where each element is a tuple containing the difference content.

[0116] Labeling the working condition type and node location for each difference is to accurately record the working condition corresponding to the difference and its position in the original particle data entry. The working condition type and node location can be added to each difference by querying the list of corresponding particle nodes for that working condition.

[0117] Step S3435: Organize the list of node difference content types, classify the differences according to the different content descriptions, label the difference characteristics of each type, and obtain a set of node difference types.

[0118] During the analysis, differences are categorized based on their expression, data characteristics, and other factors. For example, differences can be classified into numerical differences, data format differences, and data feature differences. For each type of difference, its characteristics are analyzed, such as the range of numerical differences and the specific manifestations of data format differences. Labeling the characteristics of each type of difference is crucial for accurately recording its features. These characteristics can be labeled using textual descriptions, numerical statistics, or other methods. For example, for numerical differences, features such as the average difference value and the maximum difference value can be labeled.

[0119] Step S3436: Associate the set of node difference types with the corresponding difference locations and working conditions, and label the specific manifestations and locations of each difference to obtain the particle node content differences.

[0120] Associating the node difference type set with the corresponding difference location and operating condition type aims to combine the difference type information with the specific difference location and operating condition information to form a complete difference description. The node difference type set has already categorized and labeled the differences, while the difference location and operating condition type information clarifies the specific location where the difference occurs and the corresponding operating condition.

[0121] During the association process, for each difference type, locate the corresponding difference location and operating condition information. For example, for numerical difference types, identify the locations of all differences belonging to that type and their corresponding operating conditions. Mark the specific manifestations and locations of each difference, recording in detail the specific manifestations (such as increases or decreases in value, changes in data format, etc.) and locations (such as their specific locations within the original particle data entries). A comprehensive record table can be used to list the corresponding difference location, operating condition type, specific manifestations, and location for each difference type.

[0122] Step S344: Based on the differences in particle node content, sort out the related path content corresponding to the differences, mark the node combination relationship and transmission logic that cause the differences in the path, and obtain the path content corresponding to the differences.

[0123] During the analysis, for each particle node's content differences, the corresponding associated paths were identified from the operational conditions and particle association paths. The combination relationships between nodes in the paths and the data transmission logic were analyzed to determine which node changes caused the differences in particle node content. For example, for a particle node representing the number of cloud particles, the number differs under cruise and turning conditions. By tracing the associated paths, it was found that changes in flight attitude (flight status data node) affected airflow (airflow monitoring data node), thus leading to changes in the number of cloud particles (particle raw data node). Annotating the node combination relationships and transmission logic that cause differences in the paths is crucial for accurately recording the causes and mechanisms of these differences. Node combination relationships and transmission logic can be annotated using text descriptions, charts, etc.

[0124] Step S345: Associate the differences in particle node content with the corresponding differences in the path content, mark the adjustment direction of the differences and the correspondence of the paths, and obtain the working condition adaptation adjustment items.

[0125] During the correlation process, for each particle node's content difference, the corresponding path content is identified. The adjustment direction and path correspondence are marked to determine how the particle node content should be adjusted under different operating conditions, and the relationship between this adjustment and the correlation path. For example, for a particle node representing cloud particle size, if it becomes larger under climb conditions than under cruise conditions, the correlation path analysis indicates this is due to changes in airflow temperature and pressure caused by increased flight altitude. The adjustment direction is to appropriately increase the recorded cloud particle size value under climb conditions, with the path correspondence being: Flight condition (climb) - Flight status data (altitude) - Airflow monitoring data (temperature, pressure) - Raw particle data (cloud particle size). An adjustment record table can be used to list the corresponding path content, adjustment direction, and path correspondence for each particle node's content difference.

[0126] Step S346: Perform a completeness check on the working condition adaptation adjustment items, supplement missing difference annotations, correct items with incorrect path descriptions, and obtain the working condition adaptation adjustment content.

[0127] During the inspection, for each working condition adaptation and adjustment item, the completeness of its difference annotations was checked, including the specific manifestation of the difference, its location, and the direction of adjustment. Simultaneously, the accuracy of the path description and the rationality of the node combinations and transmission logic within the path were checked. If missing difference annotations were found, they were supplemented by querying the original data and associated path information. For example, if an adjustment item lacked specific numerical changes in the difference, it was supplemented by comparing the particle node content under different working conditions. For items with incorrect path descriptions, corrections were made based on the correct associated path information. For example, if the node order in the path was incorrect, adjustments were made to conform to the actual transmission logic.

[0128] Step S350: Associate and map the working condition adaptation and adjustment content with the corresponding flight working condition entries and particle original data entries, mark the mapping path nodes and transmission logic, and obtain the working condition particle mapping entries.

[0129] The purpose of mapping the adaptation settings to the corresponding flight condition entries and particle raw data entries is to establish an accurate correspondence between different flight conditions and particle raw data, so as to adjust the particle raw data according to the flight conditions. The adaptation settings clearly define the adjustment information for particle raw data under different flight conditions. Through mapping, this adjustment information can be linked to specific flight conditions and particle raw data entries.

[0130] During the association mapping process, for each operating condition adaptation adjustment item, the corresponding flight operating condition item and particle raw data item are identified. The association path between the adjustment content and the flight operating condition and particle raw data is analyzed, and the mapping path nodes and transmission logic are marked. For example, for an adjustment item that adjusts the number of cloud particles under the climb condition, the corresponding climb operating condition item and particle raw data item containing cloud particle number information are found. The association path is analyzed as flight operating condition (climb) - flight status data (altitude) - airflow monitoring data (air pressure change) - particle raw data (cloud particle number), and the path nodes are marked as flight operating condition, flight status data, airflow monitoring data, and particle raw data. The transmission logic is forward transmission. A mapping record table can be used to list the corresponding flight operating condition item, particle raw data item, mapping path nodes, and transmission logic for each operating condition adaptation adjustment item.

[0131] Step S360: Standardize and organize the particle mapping entries for each working condition, arrange the mapping content according to the flight working condition type, and supplement the path node description for each mapping to obtain a set of working condition-adaptive mapping content.

[0132] During the standardization process, the mapping content is first arranged according to flight condition type. Flight conditions can be divided into types such as cruise, climb, descent, and turn, and the particle mapping entries under each type are grouped together. Then, the path node descriptions for each mapping are supplemented, providing detailed explanations of the specific meaning and function of each path node. For example, for a mapping path Flight Condition (Cruise) - Flight Status Data (Speed) - Airflow Monitoring Data (Airflow Stability) - Raw Particle Data (Cloud Particle Distribution), the supplemented path node description is: Flight Condition (Cruise: The aircraft maintains a stable flight state) - Flight Status Data (Speed: The aircraft's flight speed affects airflow) - Airflow Monitoring Data (Airflow Stability: Affects the distribution of cloud particles) - Raw Particle Data (Cloud Particle Distribution: Records of cloud particle distribution adjusted according to airflow conditions).

[0133] Step S400: Bind the working condition adaptation mapping content set to the original particle data after pseudo-target removal by link binding based on sample time series attributes to generate a link-bound particle dataset.

[0134] In one implementation, step S400 may specifically include the following steps S410 to S460: Step S410: Analyze the set of working condition adaptation mapping content, extract the flight working condition type, particle original data node position and associated path content corresponding to each mapping entry, organize them to obtain the core list of mapping entries, and mark the time sequence corresponding mark of each entry to obtain the core list of mapping.

[0135] The analysis of the flight condition adaptation mapping content set involves detailed analysis and processing of this set to extract key information. The flight condition adaptation mapping content set contains the mapping relationships and adjustment information between different flight conditions and the original particle data. Each mapping entry includes information such as the flight condition type, the location of the original particle data node, and the associated path content.

[0136] When extracting the flight condition type, particle original data node location, and associated path content corresponding to each mapping entry, it is necessary to traverse the set of condition adaptation mapping content and extract this information for each mapping entry. A data parsing algorithm can be used to separate the flight condition type, particle original data node location, and associated path content according to the storage structure and format of the set. This results in a core list of mapping entries, which can be a two-dimensional array where each row represents a mapping entry, and the columns store the flight condition type, particle original data node location, and associated path content, respectively.

[0137] The purpose of marking each entry with a time-series marker is to clarify the time information corresponding to each mapping entry. This time-series marker can be added to each entry by querying the timestamp information in the set of working condition adaptation mapping content. For example, for a mapping entry, its time-series marker could be "2024-01-01 10:00:00".

[0138] Step S420: Analyze the original particle data after the pseudo-targets are removed, extract the time sequence marker, node position and content corresponding to each data entry, organize them to obtain the core list of particle data, and mark the data source identifier of each entry to obtain the core list of particle data.

[0139] Analyzing the original particle data after false target removal involves detailed analysis and processing of the data to extract key information. The original particle data after false target removal is the real and valid particle data after removing false target information. Each data entry contains information such as time sequence markers, node positions, and content.

[0140] When extracting the time stamp, node position, and content corresponding to each data entry, it is necessary to traverse the original particle data after pseudo-target removal and extract this information for each data entry. A data parsing algorithm can be used to separate the time stamp, node position, and content based on the data's storage structure and format. This results in a core list of particle data, which can be a two-dimensional array where each row represents a data entry, and the columns store the time stamp, node position, and content, respectively.

[0141] Labeling each entry with a data source identifier clarifies which specific acquisition device or sensor acquired each data entry. This data source identifier can be added to each entry by querying the data's metadata. For example, a data entry could be labeled with the data source identifier "ZBT-LC-01 Cloud Particle Spectrometer".

[0142] Step S430: Based on the time-series correspondence markers of the mapping core list and the particle data core list, query the entries whose time-series markers match perfectly, mark the correspondence between the mapping content of the matching entries and the particle data content, and obtain the time-series matching entry set.

[0143] Based on the time-series correspondence markers in the mapping core list and the particle data core list, we can query entries whose time-series markers match exactly. Both the mapping core list and the particle data core list contain time-series correspondence markers; by comparing these markers, we can find entries that are completely identical in time.

[0144] During the query process, a double traversal method is used to compare each entry in the mapping core list and each entry in the particle data core list sequentially to find entry pairs with the same time series marker. For each matching entry pair, the correspondence between the mapping content and the particle data content is marked. For example, for a mapping entry and a particle data entry, both with the time series marker "2024-01-01 10:00:00", the correspondence between the adjustment information on the number of cloud particles in the mapping content and the number of cloud particles in the particle data content is marked. A matching record list can be used to record the correspondence between the mapping content and the particle data content for each matching entry pair.

[0145] Step S440: Link-bind the mapping entries in the time-series matching entry set with the particle data entries one by one, mark the time-series correspondence, node position and associated path content of the binding, record the binding link identifier, and obtain the initial link binding entries.

[0146] In one implementation, step S440 may specifically include the following steps S441 to S446: Step S441: Parse the time-series matching entry set, extract the mapping entry content, particle data entry content and time-series corresponding mark of each matching entry, organize them to obtain the core list of matching entries, mark the node position of each entry, and obtain the core list of matching entries.

[0147] Parsing the time-series matching entry set involves detailed analysis and processing of this set to extract key information. The time-series matching entry set contains time-matched mapping entries and particle data entries, along with their correspondences. Each matching entry includes the content of the mapping entry, the content of the particle data entry, and time-series correspondence markers, among other information.

[0148] When extracting the mapping entry content, particle data entry content, and time-series correspondence markers for each matching entry, it is necessary to traverse the time-series matching entry set and extract this information for each matching entry. A data parsing algorithm can be used to separate the mapping entry content, particle data entry content, and time-series correspondence markers based on the storage structure and format of the set. This results in a core list of matching entries, which can be a two-dimensional array where each row represents a matching entry, and the columns store the mapping entry content, particle data entry content, and time-series correspondence markers, respectively. Marking the node position of each entry clarifies which specific node in the original particle data each matching entry relates to. Node position markers can be added to each entry by querying the node information in the mapping entry content and particle data entry content.

[0149] Step S442: Based on the core list of matching entries, generate a unique link identifier for each matching entry, mark the correspondence between the identifier and the matching entry, record the generation rules and corresponding content of the identifier, and obtain a set of link identifiers.

[0150] Based on the core list of matching entries, a unique link identifier is generated for each matching entry. The core list contains temporally matched mapping entries and particle data entries, along with related node location information. To uniquely identify these matching entries, link identifiers need to be generated. The UUID (Universally Unique Identifier) ​​algorithm can be used to generate globally unique identifiers. For each matching entry in the core list, a unique link identifier is generated using the UUID algorithm. The correspondence between the identifier and the matching entry is then marked, associating the generated link identifier with the corresponding matching entry. For example, for a matching entry, the generated link identifier might be "123e4567-e89b-12d3-a456-426614174000," and this identifier is marked as corresponding to that matching entry. Recording the identifier generation rules and their corresponding content facilitates subsequent management and retrieval of the link identifiers. A record table can be used to record the link identifier generation algorithm (such as the UUID algorithm) and the content of the matching entry corresponding to each identifier.

[0151] Step S443: Add the link identifier to the corresponding matching entry, mark the specified position of the identifier in the entry, record the position of the identifier and the corresponding relationship, and obtain the matching entry with the identifier.

[0152] During the addition process, for each matching entry, the corresponding link identifier is retrieved from the link identifier set and added to the specified position within the matching entry. The link identifier can be added to a specified field by modifying the data structure of the matching entry. For example, a dedicated field for storing link identifiers can be added to the header of the matching entry, and the generated link identifier can be assigned to that field. Marking the specified position of the identifier within the entry clarifies its storage location within the matching entry. This can be done by recording the field name and index position. For example, marking the link identifier as added to the first field of the matching entry. Recording the position of the added identifier and its corresponding relationship facilitates subsequent querying and verification of the link identifiers. A record table can be used to record the position of each added link identifier and its corresponding relationship with the matching entry. After adding link identifiers, marking their positions, and recording their corresponding relationships, the resulting set is the identifiable set of matching entries, containing matching entries with unique link identifiers and related identifier addition information.

[0153] Step S444: Mark the bound temporal correspondence, node position and associated path content in the identified matching entries, add this information to the specified fields of the entries, mark the source and logic of the information, and obtain the matching entries with associated information.

[0154] In one implementation, step S444 may specifically include the following steps S4441 to S4446: Step S4441: Parse the tagged matching entries, extract the link identifier, mapping entry content, particle data entry content and time sequence correspondence mark of each entry, organize them to obtain the core list of tagged entries, mark the node position of each entry, and obtain the core list of tagged entries.

[0155] Parsing the tagged matching entries involves detailed analysis and processing of the set to extract key information. Tagged matching entries include those with unique link identifiers and related identifier addition information. Each entry contains the link identifier, mapping entry content, particle data entry content, and time-series correspondence markers, among other information.

[0156] When extracting the link identifier, mapping entry content, particle data entry content, and timing correspondence marker for each entry, it is necessary to traverse the identifiable matching entries and extract this information for each entry. A data parsing algorithm can be used to separate the link identifier, mapping entry content, particle data entry content, and timing correspondence marker based on the storage structure and format of the set. This results in a core list of identifiable entries, which can be a two-dimensional array where each row represents an identifiable matching entry, and the columns store the link identifier, mapping entry content, particle data entry content, and timing correspondence marker, respectively.

[0157] Labeling the node position of each entry is to clarify which specific node in the original particle data each identifiable match entry relates to. Node position labels can be added for each entry by querying the node information in the mapping entry content and the particle data entry content. For example, for an identifiable match entry, its node position is labeled as the 7th data node in the original particle data.

[0158] Step S4442: Based on the core list of tagged entries, extract the time-series corresponding markers for each matching entry, mark them in the time-series field specified by the entry, record the position and correspondence of the markers, and obtain the entries with time-series markings.

[0159] During the annotation process, for each matching entry, the extracted time-series correspondence marker is added to the entry's designated time-series field. This can be achieved by modifying the entry's data structure, creating a dedicated field to store the time-series correspondence marker, and assigning the extracted marker to that field. For example, a field named "Time-Series Correspondence Marker" can be added to the entry header, and the time-series correspondence marker "2024-01-01 10:00:00" can be added to this field. Recording the marker's location and correspondence facilitates subsequent querying and verification of the time-series correspondence marker. This can be done using a record table, recording the field name and index position of each time-series correspondence marker, as well as its correspondence with the matching entry. After the time-series correspondence marker extraction, annotation, and location recording, the resulting set is the entries with time-series annotations, containing the matching entries with the time-series correspondence markers, along with the related annotation location and correspondence information.

[0160] Step S4443: Based on the core list of labeled entries, extract the node position information of each matching entry, mark it in the node field specified by the entry, record the marked position and corresponding relationship, and obtain the entries with node labels.

[0161] During the annotation process, for each matching entry, the extracted node location information is added to a designated node field of the entry. This can be achieved by modifying the entry's data structure to create a dedicated field for storing node location information and assigning the extracted node location information to that field. For example, a field named "Node Location" can be added to the middle of the entry, and the node location "5th data node" can be added to this field.

[0162] Recording the location and corresponding relationships of the annotations facilitates subsequent querying and verification of node location information. A record table can be used to record the field names and index positions of each node location information annotation, as well as its correspondence with the matching entries. After extracting, annotating, and recording the node location information, the resulting set is the list of entries with node annotations, containing matching entries with node location information annotations, along with related annotation location and correspondence information.

[0163] Step S4444: Based on the core list of tagged entries, extract the associated path content of each matching entry, mark it in the path field specified by the entry, record the position of the mark and the corresponding relationship, and obtain the entry with path mark.

[0164] During the annotation process, for each matching entry, the extracted associated path content is added to the entry's designated path field. This can be achieved by modifying the entry's data structure to create a dedicated field for storing associated path content and assigning the extracted associated path content to that field. For example, a field named "Associated Path Content" can be added to the end of the entry, with the associated path content "Flight Conditions (Cruise) - Flight Status Data (Speed) - Airflow Monitoring Data (Airflow Stability) - Raw Particle Data (Cloud Particle Count)" added to this field.

[0165] Recording the location and correspondence of annotations facilitates subsequent querying and verification of related path content. A record table can be used to record the field name and index position of each related path content annotation, as well as its correspondence with matching entries. After extracting, annotating, and recording the location of related path content, the resulting set is the list of path-annotated entries, containing matching entries with related path content annotations, along with related annotation location and correspondence information.

[0166] Step S4445: Integrate the entries with time sequence annotations, the entries with node annotations, and the entries with path annotations, and annotate the information source and logical relationship of each field to obtain the integrated matching entries.

[0167] The purpose of integrating entries with time sequence labels, entries with node labels, and entries with path labels is to merge matching entries that are respectively labeled with time sequence correspondence markers, node location information, and associated path content into a complete entry that contains all binding information.

[0168] During the integration process, for each matching entry, the information from entries with time-series annotations, entries with node annotations, and entries with path annotations is merged. Field information from different entries can be integrated into a new entry by copying and pasting. For example, the "Time-Series Correspondence Marker" field from the entry with time-series annotations, the "Node Position" field from the entry with node annotations, and the "Associated Path Content" field from the entry with path annotations can be integrated into a new entry.

[0169] Labeling the information source and logical relationships of each field clarifies the origin and associated logic of each field. This can be done through textual descriptions, explaining that the information in the "Time-Sequence Correspondence Marker" field originates from the time-sequence correspondence marks in the core list of identifiable entries; the information in the "Node Location" field is determined based on the mapping entries and particle data entries; and the information in the "Associated Path Content" field is obtained from the associated path information in the set of working condition adaptation mapping content. Simultaneously, the logical relationships between these fields should be explained, such as the time-sequence correspondence mark determining the binding time, the node location clarifying the original particle data nodes involved in the binding, and the associated path content explaining the basis and transmission logic of the binding. After information integration and labeling of field information sources and logical relationships, the resulting set is the integrated matching entries, containing matching entries with complete binding information along with related information source and logical relationship explanations.

[0170] Step S4446: Perform a labeling consistency check on the integrated matching entries, correct entries with incorrect labeling positions or content description deviations, supplement missing labeling information, and obtain matching entries with associated information.

[0171] During the inspection process, for each integrated matching entry, the accuracy of the annotation information in each field is checked. For example, the format of the "Time Sequence Correspondence Marker" field is checked to ensure it is correct, the description of the "Node Position" field matches the actual situation, and the logic of the "Association Path Content" field is reasonable. If an annotation position is found to be incorrect, such as a field being annotated in the wrong index position, it is adjusted to be annotated in the correct position. For discrepancies in content description, such as an incorrect node order in the "Association Path Content" field, it is corrected to conform to the actual association logic. If annotation information is found to be missing, such as an entry lacking an annotation in the "Node Position" field, it is supplemented by querying the original data and association information.

[0172] Step S445: Perform an information integrity check on the matching entries with associated information, supplement missing time sequence or node annotations, correct entries with incorrect associated path descriptions, and obtain complete binding entries.

[0173] During the inspection, for each matching entry with associated information, the completeness of information such as time-series correspondence markers, node positions, and associated path content is checked. If a time-series correspondence marker is found to be missing, it is supplemented by querying the original information in the core list of matching entries. For example, if an entry is missing a time-series correspondence marker, the corresponding time-series correspondence marker is found from the core list of matching entries and added to that entry. For cases where node position information is missing, the node positions are re-determined and labeled based on the mapping entry and particle data entry.

[0174] For entries with incorrect association path descriptions, analyze the logic and node combination relationships of the association path and make corrections to make them conform to the actual situation. For example, if the node order in the association path content is incorrect, adjust the node order to correctly reflect the association relationship between flight conditions, flight status data, airflow monitoring data, and raw particle data.

[0175] Step S446: Standardize the format of the complete binding entries, arrange the contents of the entries according to the time sequence mark, and supplement the link identifier and association logic of each entry to obtain the initial link binding entries.

[0176] During the format standardization process, a unified entry format is first determined, including field names, order, and data types. For each complete binding entry, its content is adjusted according to the unified format. For example, information such as time sequence markers, node positions, and associated path content are arranged in a prescribed order within the entry. Arranging the entry content according to the time sequence marker order is to organize the binding entries in chronological order, facilitating subsequent time series analysis. A sorting algorithm can be used to sort all entries in ascending order based on the time sequence markers in the entries. The link identifier and association logic for each entry are then added to ensure that each entry contains a unique link identifier and a clear description of the association logic. For example, a link identifier field is added to the entry header, and an association logic description field is added to the entry footer, detailing the basis and transmission logic of the binding.

[0177] Step S450: Perform a binding consistency check on the initial link binding entries, correct entries with timing mismatches or incorrect node positions, supplement missing associated path content, and obtain corrected link binding entries.

[0178] During the inspection process, for each initial link binding entry, the timing correspondence marker is checked to ensure it matches the actual binding time. For example, by comparing the timing correspondence marker in the entry with the acquisition time of the relevant flight status data and particle raw data, it is determined whether there are timing mismatches. If a timing mismatch is found, it is corrected according to the actual situation, such as adjusting the timing correspondence marker to match the actual binding time. The node position correspondence is checked to ensure it is correct, analyzing whether the node positions marked in the entry correspond to the actual nodes in the mapping entry and particle raw data. If an incorrect node position is found, the node position is re-determined and corrected. For example, if an entry is marked as the 3rd data node in the particle raw data, but actually corresponds to the 5th data node, the node position is corrected to the 5th data node. For cases where associated path content is missing, the missing associated path content is supplemented by querying the working condition adaptation mapping content set and associated path information. For example, if an entry lacks information about a certain node in the associated path content, the corresponding associated path information is found from the working condition adaptation mapping content set and supplemented.

[0179] Step S460: Standardize and organize the corrected link binding entries, arrange the binding content according to the time sequence mark, and supplement the link identifier and association description of each binding entry to obtain the particle dataset after link binding.

[0180] During the standardization process, a standardized entry format is first determined, including the field names, order, data types, and value ranges. For each corrected link binding entry, its content is adjusted according to the standardized format. For example, information such as time sequence correspondence markers, node positions, and associated path content are arranged in the entry according to the prescribed order and format.

[0181] Arranging the bound content according to the time sequence markers is to ensure that the bound entries are arranged in chronological order, facilitating time series analysis and data retrieval. A sorting algorithm can be used to sort all entries in ascending order based on their corresponding time sequence markers. Supplementing each bound entry with a link identifier and association description ensures that each entry contains a unique link identifier and a detailed association description. The link identifier uniquely identifies each bound entry, while the association description further explains the basis, purpose, and logical relationship of the binding. For example, a link identifier field can be added to the beginning of the entry, and an association description field can be added to the end of the entry, detailing the flight conditions under which the binding is based and the association path used to adjust the original particle data.

[0182] When supplementing the association description, the interaction between flight conditions, flight status data, airflow monitoring data, and raw particle data is accurately described by combining the set of working condition adaptation mapping content and association path information. For example, for an entry bound to an aircraft turning condition, the association description can state: "During an aircraft turning condition, the change in heading in the flight status data causes changes in airflow direction and speed. After the airflow monitoring data reflects this change, the cloud particle distribution in the raw particle data is adjusted according to the association path to accurately reflect the actual cloud particle situation."

[0183] Step S500: Based on the link-bound particle dataset and combined with the working condition adaptation mapping content set, perform cloud microphysical parameter calculation and working condition adaptation correction to generate standardized liquid water content, number concentration and spectral distribution data.

[0184] In one implementation, step S500 may specifically include the following steps S510-S560: Step S510: Parse the particle dataset after link binding, extract the original particle data content, working condition adaptation mapping association content and time sequence corresponding tags for each binding entry. The working condition adaptation mapping association content includes flight working condition type, associated flight status and airflow monitoring data nodes, and adjustment nodes and path content of the original particle data, and organize them to obtain the core list of binding data.

[0185] When extracting the original particle data for each bound entry, the original data information such as the size and quantity of cloud particles is extracted from the bound entry according to the storage structure and format of the dataset. The operational condition adaptation mapping includes the flight operational condition type, such as cruise, climb, and descent; associated flight status and airflow monitoring data nodes, such as flight speed, altitude, airflow speed, and direction; and the adjustment nodes and paths for the original particle data, i.e., which original particle data nodes need adjustment and through what associated paths. The time sequence correspondence marker records the time information corresponding to the bound entry. The resulting core list of bound data can be a two-dimensional array, where each row represents a bound entry, and the columns store the original particle data content, operational condition adaptation mapping content, and time sequence correspondence marker, respectively.

[0186] Step S520: Based on the original particle data content in the core list of bound data, perform initial calculation of cloud microphysical parameters, calculate the initial particle spectrum distribution according to the original count and calibration size of the particle size channel, calculate the initial liquid water content based on the initial particle spectrum distribution and the particle assumed density integral, and statistically calculate the initial number concentration based on the initial particle spectrum distribution.

[0187] When calculating the initial particle size distribution, analysis is performed based on the original counts and calibration dimensions of the particle size channels. The particle size channels divide cloud particles into different size ranges, with each channel recording the number of cloud particles within that range. By statistically analyzing the cloud particle counts in different size channels and combining this with the calibration dimensions, the distribution of cloud particles at different sizes can be obtained, i.e., the initial particle size distribution. For example, by dividing cloud particles into multiple channels according to their size from smallest to largest, counting the number of cloud particles in each channel, and then plotting the curve of cloud particle count as a function of particle size, we obtain the initial particle size distribution curve.

[0188] The initial liquid water content is calculated based on the initial particle distribution and the integral of the assumed particle density. The assumed particle density is the density assuming the cloud particles are liquid, and it is usually set based on experience or experimental data. By integrating the number of cloud particles corresponding to each particle size in the initial particle distribution with the assumed particle density, the mass of liquid water per unit volume of air can be obtained, i.e., the initial liquid water content. Specifically, cloud particles within each particle size range can be considered as small liquid water droplets. Based on their number and volume, the mass of liquid water within that particle size range is calculated, and then the masses of liquid water across all particle size ranges are added together to obtain the total initial liquid water content. The initial number concentration is also calculated statistically based on the initial particle distribution. The initial number concentration can be obtained by directly counting the total number of cloud particles per unit volume in the initial particle distribution. For example, adding the number of cloud particles in all particle size channels gives the total number of cloud particles per unit volume, which is the initial number concentration.

[0189] Step S530: Guided by the working condition adaptation mapping association content in the core data list, perform airflow disturbance correction on the initial particle spectrum distribution. Based on the airflow disturbance intensity and direction data indicated in the association path, compensate and correct the particle count of the size segment corresponding to the original particle data node recorded in the working condition adaptation mapping association content as the airflow disturbance adjustment target in the initial particle spectrum distribution, and generate the airflow disturbance corrected spectrum distribution.

[0190] Airflow disturbances affect the movement and distribution of cloud particles, leading to discrepancies between the initial particle spectrum distribution and the actual situation. For example, strong airflow may cause cloud particles to aggregate or disperse, altering the distribution of cloud particles across different particle sizes. Based on the airflow disturbance intensity and direction data indicated in the correlation path, the impact of airflow on cloud particles is analyzed. If the airflow disturbance is strong and directional, it may cause cloud particles of certain particle sizes to be blown away or aggregated in other areas. For the initial particle spectrum distribution, the particle counts corresponding to the original particle data nodes in the configuration mapping, which serve as the targets for airflow disturbance adjustment, are compensated and corrected. For example, if the correlation path indicates that under a certain flight condition, airflow disturbances will reduce the number of cloud particles with a diameter of 10-20 μm, the particle counts for that particle size range are increased in the initial particle spectrum distribution to reflect the actual cloud particle distribution.

[0191] During compensation correction, a mathematical model can be established based on airflow disturbance intensity and direction data to calculate the compensation amount for particle counts in each size segment. For example, the relationship curve between airflow disturbance intensity and particle count variation can be obtained through experiments or theoretical analysis. Based on the current airflow disturbance intensity, the corresponding particle count compensation value can be found from the curve. After compensation correction, the generated spectral distribution is the airflow disturbance corrected spectral distribution, which more accurately reflects the cloud particle size distribution after considering airflow disturbance factors.

[0192] Step S540: Guided by the working condition adaptation mapping associated content in the core data list, perform flight attitude geometric correction on the spectral distribution after airflow disturbance correction. Based on the flight pitch angle and roll angle data indicated in the associated path, calculate the change in the effective sampling cross-sectional area of ​​the particle detector, perform geometric correction on the overall number concentration of the spectral distribution after airflow disturbance correction, and generate the attitude-corrected spectral distribution.

[0193] The associated paths in the working condition adaptation mapping content include flight pitch and roll angle data. Changes in flight attitude (changes in pitch and roll angles) will affect the effective sampling cross-section of the particle detector.

[0194] The effective sampling cross-section of a particle detector is the area of ​​cloud particles that the detector can actually collect. When the aircraft's flight attitude changes, the angle of the particle detector relative to the airflow changes, thus causing a change in the effective sampling cross-section. For example, when the aircraft's pitch angle increases, the particle detector may be more exposed to the airflow, increasing the effective sampling cross-section; while when the roll angle increases, the effective sampling cross-section may decrease.

[0195] Based on the flight pitch and roll angle data indicated in the associated path, the change in the effective sampling cross-section of the particle detector is calculated. This can be calculated using a geometric model. Based on the values ​​of the flight pitch and roll angles, combined with the particle detector's geometry and installation position, the effective sampling cross-section under different attitudes can be calculated. For example, for a rectangular particle detector, its projected area in the airflow is calculated based on the changes in pitch and roll angles; this is the effective sampling cross-section.

[0196] Geometric correction is performed on the overall number concentration of the spectral distribution after airflow disturbance correction. Since changes in the effective sampling cross-section affect the number of cloud particles collected by the detector, thus influencing the number concentration calculation, a larger effective sampling cross-section results in a relatively larger number of cloud particles collected, leading to a higher number concentration; conversely, a smaller effective sampling cross-section results in a lower number concentration. The overall number concentration of the spectral distribution after airflow disturbance correction is adjusted according to the proportion of change in the effective sampling cross-section.

[0197] Step S550: Classify and integrate the attitude-corrected spectral distribution data according to the flight condition type in the working condition adaptation mapping association content to ensure that the spectral data under the same steady-state flight condition is continuous and consistent in the time domain, and generate standardized spectral distribution data after working condition integration.

[0198] The attitude-corrected spectral distribution data is categorized and integrated according to the flight condition type in the condition adaptation mapping association content. Different flight conditions (such as cruise, climb, descent, and turn) have different effects on the distribution of cloud particles. Classifying the attitude-corrected spectral distribution data according to flight condition type allows for better analysis of the characteristics of cloud particles under different conditions. During the classification process, the flight condition type corresponding to each piece of attitude-corrected spectral distribution data is determined based on the condition adaptation mapping association content in the core data binding list. For example, a spectral distribution data piece collected and corrected under cruise condition is classified into the cruise condition category.

[0199] When integrating spectral distribution data under the same flight condition, it is essential to ensure that the spectral data under the same steady-state flight condition is continuous and consistent in the time domain. During actual flight, spectral distribution data may be collected at multiple time points under the same flight condition; these data should be continuous and consistent in time. Data smoothing and interpolation methods can be used to process the spectral distribution data under the same flight condition. For example, if there are differences in the spectral distribution data between two adjacent time points during cruise, linear interpolation can be used to generate a smooth transition of spectral distribution data between these two time points, ensuring the spectral data under the same cruise condition is continuous in the time domain.

[0200] Step S560: Based on the standardized spectral distribution data after the integration of operating conditions, recalculate the final standardized liquid water content and standardized number concentration. Arrange and encapsulate the standardized liquid water content, standardized number concentration, and standardized spectral distribution data after the integration of operating conditions according to the corresponding time sequence markers to generate standardized liquid water content, number concentration, and spectral distribution data.

[0201] Since the standardized spectral distribution data after the integration of operating conditions is the result of airflow disturbance correction, flight attitude geometry correction and operating condition classification and integration, it more accurately reflects the actual distribution of cloud particles. Therefore, the liquid water content and number concentration calculated based on this data are also more accurate.

[0202] For the calculation of standardized liquid water content, the final standardized liquid water content is obtained through integration based on the number and size of cloud particles of different sizes in the standardized spectral distribution data after integration under operating conditions, combined with the density of liquid water. For standardized number concentration, the number of cloud particles per unit volume is directly counted. The standardized liquid water content, standardized number concentration, and standardized spectral distribution data after integration under operating conditions are arranged and encapsulated according to time-series corresponding markers. The time-series corresponding markers record the time information corresponding to each data point. Arranging these data in chronological order can clearly show the changes of cloud microphysical parameters over time. During the encapsulation process, the standardized liquid water content, standardized number concentration, and standardized spectral distribution data after integration under operating conditions can be stored in a unified data structure, such as a three-dimensional array, where the first dimension represents time, the second dimension represents different cloud microphysical parameters (liquid water content, number concentration, spectral distribution), and the third dimension represents different particle size channels in the spectral distribution data.

[0203] Figure 2 This is a schematic diagram of the composition structure of a data processing device provided in an embodiment of the present invention, as shown below. Figure 2 As shown, the data processing device 200 includes: The data binding module 210 is used to perform temporal semantic anchoring and binding on the raw particle data collected by the airborne cloud particle detection equipment, the flight status data collected by the airborne flight status sensor, and the airflow monitoring data collected by the airborne airflow monitoring sensor, retaining the original collection attributes and timestamps of each data, and generating a multi-dimensional temporal association dataset. The data mining module 220 is used to perform correlation path traversal mining on multi-dimensional time-series correlation datasets, covering direct correlation paths between particle raw data and flight status data and airflow monitoring data, as well as indirect correlation paths transmitted through intermediate data, to generate coupled correlation path datasets. The node mapping module 230 is used to take the coupled and related path dataset as input, perform dynamic node mapping generation between airborne flight conditions and particle raw data, sort out the adjustment nodes and corresponding path contents of particle raw data under different flight conditions, and generate a set of flight condition adaptation mapping contents. The link binding module 240 is used to perform one-to-one link binding between the working condition adaptation mapping content set and the original particle data after pseudo-target removal based on sample time series attributes, and generate a link-bound particle dataset. The data generation module 250 is used to calculate cloud microphysical parameters and perform working condition adaptation correction based on the particle dataset after link binding and the set of working condition adaptation mapping content, and generate standardized liquid water content, number concentration and spectral distribution data.

[0204] The description of the above device embodiments is similar to that of the above method embodiments, and has similar beneficial effects. For technical details not disclosed in the device embodiments of the present invention, please refer to the description of the method embodiments of the present invention for understanding.

[0205] Figure 3 A hardware entity diagram of a computer system provided as an embodiment of the present invention, such as... Figure 3 As shown, the hardware entity of the computer system 1000 includes a processor 1001 and a memory 1002, wherein the memory 1002 stores a computer program that can run on the processor 1001, and the processor 1001 executes the program to implement the steps in the method of any of the above embodiments.

Claims

1. A method for processing airborne cloud particle data based on collaborative feature mapping, characterized in that, The method includes: Temporal semantic anchoring and binding are performed on the raw particle data collected by the airborne cloud particle detection equipment, the flight status data collected by the airborne flight status sensor, and the airflow monitoring data collected by the airborne airflow monitoring sensor. The original collection attributes and timestamps of each data are preserved to generate a multi-dimensional temporal association dataset. The multi-dimensional time-series correlation dataset is subjected to correlation path traversal mining, covering the direct correlation paths between the original particle data and flight status data and airflow monitoring data, as well as the indirect correlation paths transmitted through intermediate data, to generate a coupled correlation path dataset. Using the coupled path dataset as input, the dynamic node mapping between airborne flight conditions and particle raw data is generated, the adjustment nodes and corresponding path contents of particle raw data under different flight conditions are sorted out, and a set of flight condition adaptation mapping contents is generated. The working condition adaptation mapping content set is linked with the original particle data after the pseudo-targets are removed, and a link-bound one-to-one link binding is performed based on the sample time sequence attributes to generate a link-bound particle dataset. Based on the particle dataset after link binding, and combined with the set of working condition adaptation mapping contents, cloud microphysical parameters are calculated and working condition adaptation is corrected to generate standardized liquid water content, number concentration and spectral distribution data.

2. The airborne cloud particle data processing method based on cooperative feature mapping according to claim 1, characterized in that, The process involves temporal semantic anchoring and binding of raw particle data collected by airborne cloud particle detection equipment, flight status data collected by airborne flight status sensors, and airflow monitoring data collected by airborne airflow monitoring sensors. This preserves the original acquisition attributes and timestamps of each data point, generating a multi-dimensional temporal-series associated dataset, including: The raw particle data collected by the airborne cloud particle detection equipment, the flight status data collected by the airborne flight status sensor, and the airflow monitoring data collected by the airborne airflow monitoring sensor are converted into linear format of the acquisition timeline. The start and end times of the acquisition of each data point are converted into a unified continuous time axis scale. The start and end positions of each data point on the continuous time axis are recorded to obtain the time axis scale of the raw particle data, the time axis scale of the flight status data, and the time axis scale of the airflow monitoring data. The time axis nodes of the particle raw data time axis, the flight status data time axis, and the airflow monitoring data time axis are matched for overlap. The positions where the scales on the three time axes completely overlap are selected as overlap nodes. The time axis scale value and corresponding data source of each overlap node are marked to obtain the time axis overlap node set. Using the set of overlapping nodes on the time axis as input, the original particle data, flight status data and airflow monitoring data are traversed respectively. The content of the corresponding overlapping node in each data is extracted, and the extracted content is arranged in the order of the time axis scale of the overlapping nodes to obtain the data subset corresponding to the node. The data subsets corresponding to the nodes are associated and bound one by one. The original particle data content, flight status data content and airflow monitoring data content corresponding to the same overlapping node are combined, and the node correspondence is recorded to obtain the dataset after node combination. The original collection attributes are attached one by one to the dataset after the nodes are combined. The original attributes such as the collection device identifier and collection environment description of each data are attached to the specified fields of the corresponding combined content to ensure that the attributes and content correspond one by one, and the combined dataset with the original attributes attached is obtained. Add timestamp markers to the combined dataset with the original attributes, convert the time axis scale value of each overlapping node into timestamp format, add it to the header field of the corresponding combined content, and arrange all combined content in time axis scale order to obtain the multi-dimensional time-series associated dataset.

3. The airborne cloud particle data processing method based on collaborative feature mapping according to claim 2, characterized in that, The process involves taking the set of overlapping nodes on the time axis as input, traversing the original particle data, flight status data, and airflow monitoring data respectively, extracting the content of the corresponding overlapping nodes from each data point, and arranging the extracted content according to the time axis scale order of the overlapping nodes to obtain the data subset corresponding to the node, including: The set of overlapping nodes on the timeline is parsed, the timeline scale value of each overlapping node and the corresponding data source identifier are extracted, a list of node scales and sources is compiled, and the order of each list item on the timeline is marked to obtain the list of sources corresponding to overlapping nodes. Based on the list of sources corresponding to the overlapping nodes, traverse the original particle data, query the entries in each data entry whose collection start and end range includes the time axis scale value of the overlapping node, extract the content of the corresponding scale value in the entry, record the position and source of the extracted content, and obtain the content corresponding to the original particle node. Based on the list of sources corresponding to overlapping nodes, traverse the flight status data, query the entries in each data entry whose collection start and end range includes the time axis scale value of overlapping nodes, extract the content of the corresponding scale value in the entry, record the position and source of the extracted content, and obtain the content corresponding to the flight status nodes. Based on the list of sources corresponding to overlapping nodes, traverse the airflow monitoring data, query the entries in each data entry whose collection start and end range includes the time axis scale value of overlapping nodes, extract the content of the corresponding scale value in the entry, record the position and source of the extracted content, and obtain the content corresponding to the airflow monitoring nodes. The contents corresponding to the original particle nodes, the contents corresponding to the flight status nodes, and the contents corresponding to the airflow monitoring nodes are arranged one by one in the order of the overlapping node source correspondence list to ensure that the contents of the same overlapping node are arranged in the same position, thus obtaining an ordered set of node corresponding contents. Perform a content integrity check on the content set corresponding to the ordered nodes, supplement the missing content corresponding to the nodes, correct entries with erroneous content extraction, and obtain the data subset corresponding to the nodes.

4. The airborne cloud particle data processing method based on cooperative feature mapping according to claim 1, characterized in that, The process of performing path traversal mining on the multi-dimensional time-series correlation dataset covers direct correlation paths between original particle data and flight state data, as well as indirect correlation paths transmitted through intermediate data, generating a coupled correlation path dataset, including: The multi-dimensional time-series correlation dataset is analyzed, all data entries and their corresponding node combinations are extracted, a list of corresponding entries and node relationships is compiled, the data type and associated nodes of each entry are labeled, and the core list of the correlation dataset is obtained. Based on the core list of the associated dataset, the original particle data entries, flight status data entries, and airflow monitoring data entries are traversed to query the direct node combination relationship between the two. The starting entry, ending entry, and corresponding node content of each direct relationship are recorded to obtain the set of direct associated paths. Starting with the set of directly related paths, traverse the entries in the core list of the related dataset that are not included in the directly related paths, query the node combination relationship passed through other entries, record the intermediate entries, the passing order and the corresponding content of the nodes for each passing relationship, and obtain the set of indirect related paths. The directly associated path set and the indirectly associated path set are merged. Path entries with identical content are removed, and paths with the same starting and ending entries but different intermediate descriptions are merged to obtain the merged associated path set. The merged associated path set is hierarchically sorted out, classified according to the number of items involved in the path, and the hierarchical relationship and transmission logic of each path are marked to obtain a hierarchical associated path set; The hierarchical association path set is standardized and formatted, the path content is arranged according to the starting entry type, and the node corresponding description of each path is supplemented to obtain the coupled association path dataset.

5. The airborne cloud particle data processing method based on cooperative feature mapping according to claim 4, characterized in that, Starting with the set of directly related paths, the process iterates through entries in the core list of the related dataset that are not included in the directly related paths, queries the node combination relationships passed through other entries, records the intermediate entries, the transmission order, and the corresponding content of each node in each transmission relationship, and obtains the set of indirectly related paths, including: The set of directly associated paths is parsed, and the starting and ending entries and corresponding node combinations of each path are extracted. A list of core information of the direct paths is compiled, and the node correspondence of each entry is marked to obtain the core list of direct paths. Based on the core list of direct paths, all entries in the core list of the associated dataset are traversed, and unincluded entries that have a node combination relationship with the start and end entries in the core list of direct paths are selected. The data type of each selected entry is labeled to obtain a set of intermediate candidate entries. Traverse each entry in the intermediate candidate entry set, query the node combination relationship between the entry and the starting and ending entries in the direct path core list, record the transmission direction of the relationship and the corresponding content of the nodes, and obtain the intermediate entry association relationship; Based on the aforementioned intermediate item association relationship, a transmission path from the starting item through intermediate candidate items to the ending item is constructed. The starting, intermediate, and ending items of the path are marked, and the corresponding content of each node in the path is recorded to obtain the preliminary indirect association path. Iterate through all direct path core list entries, repeat the above path construction operation, generate multiple preliminary indirect association paths, and mark the start, middle and end entries of each path to obtain a preliminary indirect path set; The initial indirect path set is subjected to path validity checks, path entries that cannot form a complete transmission are removed, and errors in the path descriptions are corrected to obtain the indirect associated path set.

6. The airborne cloud particle data processing method based on cooperative feature mapping according to claim 5, characterized in that, Based on the aforementioned intermediate entry relationships, a transmission path is constructed from the starting entry through intermediate candidate entries to the ending entry. The starting, intermediate, and ending entries of the path are labeled, and the corresponding content of each node in the path is recorded, resulting in a preliminary indirect association path, including: The relationship between intermediate entries is analyzed, the node combination direction and corresponding content between intermediate candidate entries and starting and ending entries are extracted, a list of corresponding association directions and contents is compiled, the transmission logic of each list item is marked, and the core list of intermediate associations is obtained. Based on the aforementioned intermediate association core list, the transmission order from the starting item to the intermediate candidate item and then to the ending item is determined, the node correspondence and transmission direction of the order are marked, and the path transmission order rules are obtained. According to the path transmission order rules, the starting item, intermediate candidate items, ending item and corresponding node combination content are concatenated, the order of the concatenated items and the corresponding content of the nodes are recorded, the transmission logic of the path is marked, and the content of a single indirect path is obtained. Traverse all entries in the intermediate candidate entry set, repeat the above path concatenation operation to generate multiple single indirect path contents, and mark the start, middle and end entries of each path to obtain multiple indirect path contents; The multiple indirect path contents are deduplicated by removing path entries with identical content and merging paths with the same starting, intermediate, and ending entries but different node descriptions to obtain the deduplicated indirect path contents. The deduplicated indirect path content is structured and organized to obtain path entries in a unified format. The node correspondence and transmission logic of each path are marked to obtain the preliminary indirect association path.

7. The airborne cloud particle data processing method based on cooperative feature mapping according to claim 1, characterized in that, The process involves taking the coupled path dataset as input, performing dynamic node mapping generation between airborne flight conditions and raw particle data, sorting out the adjustment nodes and corresponding path content of raw particle data under different flight conditions, and generating a set of flight condition adaptation mapping content, including: The coupled path dataset is parsed, all path entries and their corresponding entry types and node combination relationships are extracted, a corresponding list of path and node relationships is compiled, the working condition association probability of each entry is marked, and the core list of coupled paths is obtained. Based on the core list of the coupling path, sort out the contents of the items corresponding to the airborne flight conditions, mark the flight status data item type and node combination relationship corresponding to each condition, and obtain the set of items corresponding to the conditions. Based on the core list of coupling paths and the set of corresponding entries for each working condition, the associated paths between flight working condition entries and particle raw data entries are queried, the node combination relationship and transmission logic of the paths are marked, and the associated paths between working conditions and particles are obtained. Based on the working conditions and particle association paths, the node adjustment content corresponding to the original particle data entries under different flight working conditions is sorted out, the adjusted node positions and corresponding path contents are marked, and the working condition adaptation adjustment content is obtained. The working condition adaptation and adjustment content is associated and mapped with the corresponding flight working condition entries and particle raw data entries, and the mapping path nodes and transmission logic are marked to obtain the working condition particle mapping entries. The particle mapping entries for the working conditions are standardized and organized, and the mapping content is arranged according to the flight working condition type. The path node description of each mapping is supplemented to obtain the set of working condition adapted mapping content.

8. The airborne cloud particle data processing method based on cooperative feature mapping according to claim 7, characterized in that, Based on the aforementioned working conditions and particle association paths, the node adjustment content corresponding to the original particle data entries under different flight working conditions is sorted out, and the adjusted node positions and corresponding path content are marked to obtain the working condition adaptation adjustment content, including: The working conditions and particle-related paths are analyzed, and the flight working condition entries, particle raw data entries and corresponding node combination relationships of each path are extracted. The core list of working conditions and particle paths is compiled and the node position of each entry is marked to obtain the core list of working condition particle paths. Based on the core list of particle paths for the working conditions, traverse different flight working condition entries, query the original particle data entries and node combination relationships corresponding to each working condition entry, mark the specific position of the node in the original particle data entry, and obtain the particle node corresponding to the working condition. Based on the particle nodes corresponding to the working conditions, analyze the changes in the content of the original particle data entries under different working conditions, query the content differences of the same particle node under different working conditions, mark the specific manifestations and locations of the differences, and obtain the content differences of the particle nodes. Based on the differences in the content of the particle nodes, the related path content corresponding to the differences is sorted out, and the node combination relationship and transmission logic that cause the differences in the path are marked to obtain the path content corresponding to the differences. The differences in particle node content are associated with the corresponding path content, and the adjustment direction of the differences and the path correspondence are marked to obtain the working condition adaptation adjustment items. Perform a completeness check on the working condition adaptation and adjustment items, supplement missing difference annotations, and correct items with incorrect path descriptions to obtain the working condition adaptation and adjustment content; Specifically, based on the particle nodes corresponding to the operating conditions, the analysis of the content changes of the original particle data entries under different operating conditions, the query of the content differences of the same particle node under different operating conditions, the annotation of the specific manifestations and locations of the differences, and the obtaining of particle node content differences include: The particle nodes corresponding to the working conditions are analyzed, the original data entry identifiers and node positions of the particles corresponding to each working condition are extracted, and a list of the correspondence between working conditions and particle nodes is compiled. The working condition type of each entry is marked, and a list of the corresponding particle nodes for each working condition is obtained. Based on the list of particle nodes corresponding to the working conditions, the original data entries of particles under different working conditions corresponding to the same particle node are extracted, and the contents of multiple working conditions of the same node are organized to obtain a set of contents of the same node. The working condition type and node position of each content are marked to obtain a set of contents of multiple working conditions nodes. The content in the multi-condition node content set is compared segment by segment. The specific differences in the descriptions corresponding to the node positions are queried, and the start and end positions of the differences are marked to obtain the node content difference positions. Based on the differences in the node content, the content at the differences is extracted, and a corresponding list of differences is compiled. The working condition type and node position of each difference are marked to obtain a list of node differences. The list of node differences is sorted by difference type, and the differences are classified according to the different content descriptions. The difference characteristics of each type are marked to obtain a set of node difference types. Associating the set of node difference types with the corresponding difference locations and operating conditions, and labeling the specific manifestations and locations of each difference, we obtain the particle node content differences.

9. A data processing apparatus, characterized in that, include: The data binding module is used to perform temporal semantic anchoring and binding on the raw particle data collected by the airborne cloud particle detection equipment, the flight status data collected by the airborne flight status sensor, and the airflow monitoring data collected by the airborne airflow monitoring sensor. It retains the original collection attributes and timestamps of each data and generates a multi-dimensional temporal association dataset. The data mining module is used to perform correlation path traversal mining on the multi-dimensional time-series correlation dataset, covering the direct correlation paths between the original particle data and flight status data and airflow monitoring data, as well as the indirect correlation paths transmitted through intermediate data, to generate a coupled correlation path dataset. The node mapping module is used to take the coupled and associated path dataset as input, perform dynamic node mapping generation between airborne flight conditions and particle raw data, sort out the adjustment nodes and corresponding path contents of particle raw data under different flight conditions, and generate a set of flight condition adaptation mapping contents. The link binding module is used to perform one-to-one link binding between the working condition adaptation mapping content set and the original particle data after pseudo-target removal based on sample time sequence attributes, and generate a link-bound particle dataset. The data generation module is used to calculate cloud microphysical parameters and perform working condition adaptation correction based on the particle dataset after link binding and the set of working condition adaptation mapping content, and generate standardized liquid water content, number concentration and spectral distribution data.

10. A computer system comprising a memory and a processor, the memory storing a computer program executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the method according to any one of claims 1 to 8.

Images