A data analysis and application method for space-air-ground-sea integration based on 6G

Abstract

A data analysis and application method for the integration of air, space, land, and sea based on 6G, including: 1) constructing an aerial base station and receiving RDSS data from Beidou satellites through a Beidou signal receiver to divide the air, land, and sea into three commercial service areas; 2) the satellites of the aerial base station synchronize the RDSS data transmitted by the RDSS ground facilities of the Beidou satellites through the IAB base station group, obtain the application scenarios based on the commercial service areas according to the RDSS data, construct a domain association relationship classification model using the Pareto analysis method, and classify the mapping relationships between the air, land, and sea commercial service areas and the application scenarios; 3) according to the mapping relationships between the application scenarios and the commercial service areas, provide commercial services for the high-altitude emergency communication service scenario, the ocean rescue emergency service scenario, and the earthquake high-altitude disaster relief emergency scenario in the application scenarios. The present invention completes the data analysis and application of the integration of air, space, land, and sea by dividing the commercial service areas and applying them to different scenarios, and combining the aerial base station with Beidou.

Description

Technical Field

[0001] The present invention belongs to the field of air-space, land-ground and sea data analysis, and specifically relates to a 6G-based air-space, land-ground and sea integrated data analysis and application method. Background Art

[0002] The space-ground integrated information network consists of a space-based backbone network, a space-based access network, and a ground-based node network, and is interconnected with the ground Internet and mobile communication network to build a space-ground integrated information network system with "global coverage, access at any time, on-demand service, and security and reliability". This space-ground integrated information network system has the capabilities of global space-time continuous communication, highly reliable and secure communication, regional large-capacity communication, and highly mobile full-process information transmission.

[0003] As the commercial service areas of aerial base stations based on air, space, land and sea continue to expand, the acquisition of spatiotemporal information will move towards the integration of space and land and globalization, spatiotemporal information processing and information processing will move towards automation, intelligence and real-time, spatiotemporal information management and distribution will move towards gridding, and spatiotemporal information services will tend to be popular, driving the formation and development of commercial services of aerial base stations. However, this will also generate a large amount of spatiotemporal information and analysis needs.

[0004] However, the existing technology does not have the ability to fully mine spatiotemporal information because the commercial services of 6G aerial base stations have not been deployed on a large scale. Therefore, it is necessary to use artificial intelligence technology to conduct in-depth mining of spatiotemporal big data in the process of large-scale commercial application of aerial base stations in the future. Summary of the invention

[0005] In view of the deficiencies in the prior art, the present invention provides a 6G-based integrated air-space-ground-sea data analysis and application method.

[0006] To achieve the above object, the present invention adopts the following technical solutions:

[0007] A 6G-based air-space-ground-sea integrated data analysis and application method, characterized by comprising the following steps:

[0008] Step 1: Build an aerial base station, install a BeiDou signal receiver on each satellite of the aerial base station, and use the BeiDou signal receiver to receive RDSS data from BeiDou satellites to divide the three commercial service areas of air, land, and sea;

[0009] Step 2: The satellites of the aerial base station synchronize the RDSS data transmitted by the BeiDou satellite RDSS ground facilities through the IAB base station group, and obtain application scenarios based on the commercial service field based on the RDSS data. The Pareto analysis method is used to build a domain association relationship classification model to classify the mapping relationship between the three commercial service fields of air, land and sea and the application scenarios;

[0010] Step 3: Provide commercial services for the high-altitude emergency communication service scenario, marine rescue emergency service scenario, and earthquake high-altitude disaster relief emergency scenario in the application scenario according to the mapping relationship between the application scenario and the commercial service field.

[0011] Specific measures taken to optimize the above technical solutions also include:

[0012] Further, in the said Step 1, the Beidou signal receiver receives the RDSS short message coordinate positioning and IP data of the Beidou satellite, locates and confirms the coordinates of satellites in different orbits in the air base, and uniformly transmits them to the core satellite of the air base.

[0013] Further, in the said Step 2, the mapping relationships between the three commercial service fields of air, land, and sea and the application scenario are divided into five categories: cooperation relationship, shared kernel, customer-supplier relationship, follower relationship, and going their own ways.

[0014] Further, in the said Step 2, after obtaining the application scenario based on the commercial service field, the air base analyzes the RDSS data again to obtain the context mapping relationship of each commercial service field and transmits it to the data middle platform; the data middle platform matches the corresponding application scenario according to the transmitted context mapping relationship, and transmits the matched data and application scenario to the microservice module. The client of the microservice module collects the user's data instructions according to different application scenarios, and then transmits the data instructions back to the air base through the data middle platform and the IAB base station in sequence.

[0015] Further, in the said Step 3, for the high-altitude emergency communication service scenario, the Beidou navigation and positioning provides air obstacle warning for the air base, and the adopted collision prediction model is as follows:

[0016] Taking the air obstacle as the center of the coverage area, the center coordinates are (x0, y0), and the area radius is r; the positioning coordinate set G of the satellite t with coordinates (x t , y t ), and the corresponding time point of the coordinate is t, and the discrete average value s is calculated by combining the optimized wireless positioning algorithm as:

[0017]

[0018] In the formula, n is the number of satellite positioning point sets;

[0019] Performing matrix operation on the positioning coordinate set G t and the time point set T of the corresponding coordinate points to obtain the vector parameter value

[0020]

[0021] From the discrete average value s and the vector parameter value Multiply to obtain the average dispersion coefficient value Z:

[0022]

[0023] The average dispersion coefficient value Z of the satellite thus obtained is compared with the standard dispersion coefficient value β within the range of this area. The standard dispersion coefficient value β is obtained by taking the derivative in the time region Δt. See the following formula:

[0024]

[0025] Within the range of the current area radius and movement time, compare the average dispersion coefficient value Z and the standard dispersion coefficient value β. If Z is greater than β, it means that there is no intersection between the satellite and the coverage area range; conversely, if Z is less than or equal to β, the larger the difference K = β - Z, the more frequent the intersection of the satellite within the coverage area range.

[0026] Furthermore, in step 3, for the ocean rescue and emergency service scenario, coordinate information is collected and transmitted to the Beidou satellite through Beidou signal receivers installed on drilling platforms and fishing boats. The ground MCC of the Beidou satellite obtains the short message log data of the Beidou satellite and synchronizes it to the air base station for data analysis. The air base station warns of submarine pipeline failures according to the submarine pipeline failure warning model. The submarine pipeline failure warning model is as follows:

[0027] Under the action of overpressure outside the seabed, the critical external pressure is calculated as follows:

[0028]

[0029] In the formula, p er represents the critical external pressure, σ F represents the minimum yield strength, D represents the outer diameter of the steel pipe, t represents the minimum wall thickness, and C represents the critical collapse pressure of an absolutely smooth circular pipe;

[0030] Using the critical external pressure of the submarine pipeline detected by the drilling platform and fishing boat as a dynamic index, early warnings are given for situations approaching or exceeding the critical external pressure threshold.

[0031] Furthermore, in step 3, for the earthquake and high altitude disaster relief emergency scenario, load chips on the unmanned aerial vehicles of the air base station, and form an unmanned aerial vehicle wireless network in the air through the SRv6 protocol. Assign independent IPv6 addresses to each unmanned aerial vehicle. Each unmanned aerial vehicle acts as a mobile SR network node. Each hop of the SR network node acts as a temporary IAB node for communication with the ground during the packet forwarding process. The IAB node performs RAN-level fusion on different wireless signals;

[0032] After the ground IAB base station receives the message signal forwarded by the SR network node, it compares and analyzes the obtained air message data with the historical alarm data to determine whether the alarm data of the current SR network node exceeds the alarm threshold of the most recent time. If one exceeds the alarm threshold of the most recent time, it is determined as abnormal, and the operation result is sent back to the SR network node through the IAB base station to inform the SR network node that forwards the message whether it is the optimal passing node, and the confirmation of the optimal node for message forwarding is completed.

[0033] Furthermore, the alarm data of the current SR network node includes UAV failures, memory usage rate, CPU occupancy rate, and network delay anomalies.

[0034] The beneficial effects of the present invention are as follows: The present invention constructs an air base station in which each satellite is equipped with a Beidou signal receiver, divides the commercial service field into air, land, and sea through the analysis of Beidou satellite RDSS data in combination with the DDD technology architecture, and applies it to different scenarios. For the high-altitude emergency communication service scenario, a collision model is constructed for early warning of air obstacles; for the marine rescue emergency service scenario, the critical external pressure of the submarine pipeline is used as a dynamic index for early warning of submarine pipeline failures; for the earthquake high-altitude disaster relief emergency scenario, considering that the base station signal in the disaster relief emergency scenario cannot be transmitted, the low-altitude UAVs are temporarily mobilized through the air base station to form an air emergency rescue network for network transmission in the coverage area of the disaster area scenario. The present invention innovatively combines the air base station and Beidou satellites, gives full play to the commercial service functions in emergency scenarios such as disaster relief and emergency, and finally completes the data analysis and application of the integration of air, land, sea, and space. Description of the Drawings

[0035] Figure 1 It is the overall framework diagram of a method for data analysis and application of air, land, sea, and space integration based on 6G proposed by the present invention. Detailed Embodiment

[0036] Now, the present invention will be further described in detail with reference to the accompanying drawings.

[0037] As Figure 1 shown, a method for data analysis and application of air, land, sea, and space integration based on 6G includes the following steps:

[0038] Step 1: Construct an air base station and divide the commercial service field by using relevant data such as coordinates, data sources, service requests, and user demand parameters obtained through the RDSS data transmission protocol of Beidou satellites.

[0039] First, by integrating satellites in different orbits, non-ground network nodes are assembled into a 6G air base station, and a Beidou signal receiver is installed on each satellite to prepare for data interaction on the air side of the air base station commercial service. The main characteristics, time delays, and coordinates of each layer of satellites in the air base station are shown in Table 1.

[0040] Secondly, the Beidou signal receiver is used to receive data such as Beidou RDSS short message coordinate positioning and IP, so as to locate and confirm the coordinates of satellites in different orbits of the air base, and uniformly transmit them to the core satellites of the 6G air base. In order to reduce the deviation of data such as satellite application scenarios and the expected delay of the air network quality caused by possible reasons (faults, weather, atmosphere, ionosphere, etc.) of the air base, by using the coordinates of Beidou positioning satellites for calibration and reference, it is convenient to provide various air services for subsequent scenarios.

[0041] Table 1 Main characteristics, time delay and coordinates of satellites at each layer of the air base

[0042]

[0043]

[0044] Step 2: Integrate data transmission and analysis between the air base and the ground, and use the Pareto analysis method to classify the relationship between the three fields of air, ground and sea and the commercial scenarios in this field.

[0045] The satellites of the air base synchronize the log data transmitted by the Beidou satellite RDSS ground facilities through the IAB base station group, and obtain the application scenarios based on the (air, ground, sea) commercial service fields according to the RDSS data (see Table 1), providing accurate application scenario positioning for satellite commercial services. After analyzing the RDSS data again to obtain the characteristics of the context mapping relationship in each field, the data is stored in the data middle platform. The context represents the upper and lower boundaries of the field, and information outside this boundary does not belong to this field. The context mapping relationship is expressed as (cooperation relationship, shared kernel, customer-supplier relationship, follower relationship, going their own ways).

[0046] The data middle platform is responsible for storage and scheduling: matching the commercial service scenarios according to the transmitted context mapping relationship. Then, the matched data and application scenarios are transmitted to the microservice module. The client of the microservice module collects data instructions such as user click behavior according to different scenarios, and then transmits the data instructions back to the data middle platform -> back to the IAB base station group -> air base, thus completing the commercial service mode of integrating air, ground, sea and space on the ground side.

[0047] The fields of application scenarios include: air, ground, and sea. The Pareto analysis method is used to construct a sub-field association relationship classification model. Through model analysis, the mapping relationship between the field and the commercial scenarios in this field is divided into 5 categories (cooperation relationship, shared kernel, customer-supplier relationship, follower relationship, going their own ways). For example: the relationship between the ocean and ocean rescue, the relationship between the ocean and tsunami early warning, the relationship between the ocean and submarine cable fault repair.

[0048] According to Domain-Driven Design (DDD), the mapping relationships are as follows:

[0049] 1. Partnership: If the teams of two bounded contexts either succeed together or fail together, a partnership needs to be established at this time. It is necessary to coordinate the development plan and integration management together. The two teams should cooperate in the evolution of the interface to meet the requirements of both systems simultaneously, and a schedule should be made for the interrelated software functions to ensure that these functions are completed in the same release.

[0050] 2. Shared Kernel: The sharing of models and code will produce a strong dependence, which can be either good or bad for the design. It is necessary to specify an explicit boundary for the shared part of the model and keep the shared kernel small. The shared kernel has special states that cannot be changed without consulting another team. A continuous integration process should be introduced to ensure the consistency of the shared kernel with the ubiquitous language.

[0051] 3. Customer-Supplier Development: When two teams are in an upstream-downstream relationship, the upstream team may complete development independently of the downstream team, and at this time, the development of the downstream team may be greatly affected. Therefore, the needs of the downstream team should be taken into account in the plan of the upstream team.

[0052] 4. Conformist: In two teams with an upstream-downstream relationship, if the upstream team has no motivation to provide what the downstream team needs, the downstream team will be helpless. Out of altruism, the upstream team may make various commitments to the downstream team, but it is very likely that these commitments cannot be fulfilled. The downstream team can only blindly use the model of the upstream team.

[0053] 5. SeparateWay: When determining requirements, it should be done resolutely and thoroughly. If two sets of functions have no significant relationship, then they can be completely decoupled. Integration is always expensive and sometimes brings little benefit. Declaring that there is no relationship between two bounded contexts allows developers to find other simple and specialized methods to solve problems.

[0054] Use the Pareto analysis method to construct a classification model for the association relationship of sub-domains, as follows:

[0055] First, access the historical logs of the microservice module.

[0056] Second, use the Pareto analysis method to construct a classification model for the association relationship of sub-domains.

[0057] The formula of Pareto analysis method (Pareto):

[0058] minf(x)=(f1(x),…,f p (x)) T

[0059] The feasible region of variables is S, and the corresponding objective feasible region Z = f(S).

[0060] Given a feasible point x * ∈S, there is If f(x * ) < f(x), then x * is called the absolute optimal solution of the mapping context of the domain. If there does not exist x ∈ S such that f(x) < f(x * ), then x * is called the effective solution of the mapping context relationship of the domain. The effective solution of the multi-objective programming problem is also called the Pareto optimal solution (there can be multiple optimal solutions). The Pareto optimal solution is the classification of the association relationships of all sub-domains in each domain.

[0061] Step 3: Construct a scenario classification model to give examples of scenarios for commercial services in the air, land, and sea domains, so as to assist the aerial base stations to play their roles in disaster relief, emergency and other emergency scenarios.

[0062] 1. High-altitude emergency communication service scenario: Provide early warning of aerial obstacles for aerial base stations through Beidou navigation and positioning, and also provide various commercial services for other aerial objects. For example: aerial early warning service, network-related service, etc.

[0063]

High-altitude emergency communication service scenario

shared kernel

air

[0064] The collision prediction model adopted for early warning is as follows:

[0065] Taking the aerial obstacles given by Beidou navigation satellites as the central coordinates (x0, y0) of the coverage area and the area radius as r, the standard discrete coefficient value, that is, the area coefficient value β, is obtained by derivation in the time region Δt; at the same time, in the coordinate vector set coordinates (x t , y t ) and the corresponding time point t and other parameter factors in the positioning data set of a certain aerial base station satellite are combined with an optimization algorithm to calculate the corresponding average discrete coefficient value Z. In the current range of a certain area radius and movement time, Z and β are compared. If Z is greater than the area coefficient value β, it means that this satellite has no intersection with this area range; conversely, if Z is less than or equal to the area coefficient value β, then the difference value K between the two is compared. The larger K is, the more frequent the intersection of this aerial base station satellite in this area range is.

[0066] Combined with the optimized wireless positioning algorithm function, the positioning coordinate set G of the satellite data set t and the time point set T corresponding to the coordinate points are used for algorithm calculation to obtain the average discrete coefficient value Z of the corresponding IP satellite. The discrete average value formula is as follows:

[0067]

[0068] Take the discrete average value s of the satellite positioning point and the central positioning point (x0, y0) for the calculation of obtaining the current satellite average discrete coefficient value. t is the time difference between each positioning time point of the satellite and the initial time point of the regional central positioning, and n is the number of satellite positioning point sets.

[0069] Positioning coordinate set:

[0070] Coordinate corresponding time point set:

[0071] Expand G t and T for matrix operation to obtain the vector parameter value

[0072]

[0073] Multiply the discrete average value s obtained above directly by the vector parameter value to obtain the average discrete coefficient value Z:

[0074]

[0075] Compare the obtained satellite average discrete coefficient value with the standard discrete coefficient value within the range of this area, that is, the area coefficient value β, to analyze and obtain the result. Standard discrete coefficient value:

[0076]

[0077] Obtain the standard discrete coefficient value within this area, compare it with the average discrete coefficient value of the corresponding IP satellite. If Z is greater than β, then the satellite positioning has no intersection with the range of this area. If Z is less than or equal to β, then in the next step, analyze the difference value K = β - Z. The larger K is, that is, the smaller Z is relative to β, it indicates that the satellite is closer to the central area of the air obstacle and the intersection is more frequent.

[0078] 2. In the marine rescue and emergency service scenario, conduct early warning of submarine pipeline failures.

[0079]

Marine rescue and emergency service scenario

customer - supplier relationship

sea

[0080] First, the Beidou signal receivers installed on offshore drilling platforms, fishing boats, etc. collect and transmit the coordinate information of meteorological and oceanic obstacles, etc. to the Beidou satellites.

[0081] Secondly, the ground MCC of Beidou obtains the short message logs of the satellites in the air from the Beidou satellites, and obtains data (satellite IP, distance between the satellite and the MCC, time when the satellite sends the short message to reach the MCC, satellite coordinates (obtained by combining the Beidou RDSS positioning algorithm with double-star positioning), coordinates, IPs of other satellites related to the service, associated service time) and other data from them, and synchronizes them to the satellite in the air base station or HAPS and UAV relay points for data analysis.

[0082] The early warning model for submarine pipeline failures is as follows:

[0083] Under the action of external overpressure on the seabed, the critical external pressure is calculated as follows:

[0084]

[0085] In the formula, p er represents the critical external pressure, σ F represents the minimum yield strength, D represents the outer diameter of the steel pipe, t represents the minimum wall thickness, and C represents the critical collapse pressure of an absolutely smooth circular pipe;

[0086] Taking the critical external pressure of the submarine pipeline detected by drilling and fishing boats as a dynamic index, monitor the situation of approaching or exceeding the threshold at any time, and give an early warning. After transmitting the data to the sea surface facilities, the RDSS short message is sent to the air base station and the Beidou ground MCC through the Beidou signal receiver to complete the marine rescue emergency service.

[0087] 3. Earthquake high-altitude disaster relief emergency scenario. When the base station signal cannot be transmitted, the low-altitude UAVs are temporarily mobilized through the air base station to form an air emergency rescue network for network transmission in the disaster area scene coverage area.

[0088] The relationship between [Earthquake high-altitude disaster relief emergency scenario] and the domain [Geography] is [Follower relationship].

[0089] First, load the chips for the UAVs of the air base station, and form a wireless network of UAVs in the air through the SRv6 protocol. Assign independent IPv6 addresses to each UAV, and each UAV is equivalent to a mobile SR node.

[0090] Secondly, each hop of the SR network node, also known as the UAV node, acts as a temporary IAB node for communication with the ground during the packet forwarding process. The IAB node performs RAN-level fusion on different wireless signals and adopts a single wireless technology solution (using the same NR air interface between the ground and non-ground networks).

[0091] Then, the UAV node sends a signal to the 5G radio access network (RAN), which can use both NR (gNB) and LTE (eNB) base stations simultaneously.

[0092] Finally, after the ground IAB base station receives the packet signal forwarded by the UAV node, it compares and analyzes the obtained air packet data with the historical log alarm data to determine whether it exceeds the alarm threshold of the most recent time. The alarm data includes UAV failures, memory usage, CPU occupancy, network delay anomalies, etc. If any one exceeds the alarm threshold of the most recent time, it is determined as abnormal, and the operation result is sent back to the UAV node located at low altitude through the IAB base station to inform the node that forwarded the packet whether it is the optimal passing node, thus completing the secondary confirmation of the optimal node for packet forwarding. Thereby, the process of the UAV forming an air emergency rescue network for network transmission in the disaster area scene coverage area is completed.

[0093] Finally, through the definition of the relationship between the domain and the application scenario, it assists the air base station data analysis to provide more accurate data definition and search. At the same time, it also provides more accurate data for artificial intelligence services such as early warning services, disaster assessment services, and emergency plan formulation services.

[0094] The above is only the preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above embodiments. All technical solutions falling within the idea of the present invention belong to the protection scope of the present invention. It should be noted that for those of ordinary skill in the art, several improvements and refinements made without departing from the principle of the present invention should be regarded as within the protection scope of the present invention.

Claims

1. A data analysis and application method for the integration of air, space, land and sea based on 6G, characterized in that, It includes the following steps: Step 1: Construct an aerial base station, install a Beidou signal receiver on each satellite of the aerial base station, and receive the RDSS data of Beidou satellites through the Beidou signal receiver to divide the three commercial service areas of air, land, and sea; In the above Step 1, the Beidou signal receiver receives the RDSS short message coordinate positioning and IP data of Beidou satellites, locates and confirms the coordinates of satellites in different orbits in the aerial base station, and uniformly transmits them to the core satellite of the aerial base station; Step 2: The satellites of the aerial base station synchronize the RDSS data transmitted by the Beidou satellite RDSS ground facilities through the IAB base station group, obtain the application scenarios based on the commercial service areas according to the RDSS data, construct a domain association relationship classification model using the Pareto analysis method, and classify the mapping relationships between the three commercial service areas of air, land, and sea and the application scenarios; In the above Step 2, after obtaining the application scenarios based on the commercial service areas, the aerial base station analyzes the RDSS data again to obtain the context mapping relationship of each commercial service area and transmits it to the data middle platform; The data middle platform matches the corresponding application scenarios according to the transmitted context mapping relationship, and transmits the matched data and application scenarios to the microservice module. The client of the microservice module collects the user's data instructions according to different application scenarios, and then transmits the data instructions back to the aerial base station through the data middle platform and the IAB base station in sequence; Step 3: Provide commercial services for the high-altitude emergency communication service scenario, marine rescue emergency service scenario, and earthquake high-altitude disaster relief emergency scenario in the application scenarios according to the mapping relationship between the application scenarios and the commercial service areas.

2. The data analysis and application method based on 6G integrated space-air-ground-sea, as described in claim 1, is characterized in that: In the above Step 2, the mapping relationships between the three commercial service areas of air, land, and sea and the application scenarios are divided into five categories: cooperation relationship, shared kernel, customer-supplier relationship, follower relationship, and going their own ways.

3. A data analysis and application method for space-air-ground-sea integration based on 6G according to claim 1, characterized in that: In the above Step 3, for the high-altitude emergency communication service scenario, provide aerial obstacle warning for the aerial base station through Beidou navigation and positioning. The collision prediction model used is as follows: Taking the aerial obstacle as the center of the coverage area, with the center coordinates being (x0, y0) and the area radius being r; the set G of satellite positioning coordinates t with coordinates (x t , y t ), and the corresponding time point of the coordinates being t, calculating the discrete average value s by combining the optimized wireless positioning algorithm as follows: In the formula, n is the number of satellite positioning point sets; Perform matrix operations on the set of positioning coordinates G t and the set of time points T corresponding to the coordinate points to obtain vector parameter values The average discrete coefficient value Z is obtained by multiplying the discrete average s by the vector parameter value : The average dispersion coefficient value Z of the obtained satellite is compared with the standard dispersion coefficient value β in this area range, and the standard dispersion coefficient value β is obtained by taking the derivative in the time region Δt. See the following formula: In the current regional radius and movement time range, compare the average dispersion coefficient value Z and the standard dispersion coefficient value β. If Z is greater than β, it means that there is no intersection between the satellite and the coverage area range; on the contrary, if Z is less than or equal to β, the larger the difference value K = β - Z, the more frequent the intersection of the satellite in this coverage area range.

4. A data analysis and application method for the integration of space, air, land and sea based on 6G according to claim 1, characterized in that: In the above Step 3, for the marine rescue emergency service scenario, collect and transmit coordinate information to Beidou satellites through the Beidou signal receivers installed on the drilling platforms and fishing boats. The ground MCC of Beidou satellites obtains the short message log data of Beidou satellites and synchronizes it to the aerial base station for data analysis. The aerial base station warns of submarine pipeline failures according to the submarine pipeline failure warning model. The specific submarine pipeline failure warning model is as follows: Under the action of external overpressure at the bottom of the sea, the critical external pressure is calculated as follows: Where p er represents the critical external pressure, σ F represents the minimum yield strength, D represents the outer diameter of the steel pipe, t represents the minimum wall thickness, and C represents the critical crushing pressure of an absolutely smooth circular pipe; The critical external pressure of the subsea pipeline detected by the drilling platform and fishing boat is used as a dynamic index to give early warnings for situations close to or exceeding the critical external pressure threshold.

5. A data analysis and application method for space-air-ground-sea integration based on 6G according to claim 1, characterized in that: In step 3, for the earthquake high-altitude disaster relief emergency scenario, load chips on the unmanned aerial vehicles of the aerial base station, and form an unmanned aerial vehicle wireless network in the air through the SRv6 protocol. Assign independent IPv6 addresses to each unmanned aerial vehicle. Each unmanned aerial vehicle serves as a mobile SR network node. Each hop of the SR network node acts as a temporary IAB node for communication with the ground during the packet forwarding process. The IAB node performs RAN-level fusion on different wireless signals. After receiving the packet signal forwarded by the SR network node, the ground IAB base station compares and analyzes the obtained aerial packet data with the historical alarm data to determine whether the alarm data of the current SR network node exceeds the alarm threshold of the most recent time. If any one exceeds the alarm threshold of the most recent time, it is determined as abnormal, and the operation result is sent back to the SR network node through the IAB base station to inform the SR network node that forwards the packet whether it is the optimal passing node, thus completing the confirmation of the optimal node for packet forwarding.

6. The data analysis and application method for space-air-ground-sea integration based on 6G according to claim 5, wherein: The alarm data of the current SR network node includes unmanned aerial vehicle failures, memory usage rate, CPU occupancy rate, and abnormal network delay.

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