Data storage method of unmanned aerial vehicle for atmosphere and carbon emission monitoring

Abstract

The invention discloses a data storage method of an unmanned aerial vehicle for monitoring atmosphere and carbon emission, which comprises the following steps: setting secondary branches under head based on the relevance of subtasks, connecting each secondary branch between the head and a data pile, and ensuring that each secondary branch meets the principle of continuous storage; wherein each subtask is used for describing storage or clearing of metadata or a combination of the storage and the clearing of the metadata; each head is in one-to-one correspondence with the metadata with the specific size. The core of the invention aims to solve the problem of storage crash of the storage module caused by metadata, and the problem of disordered storage is solved by creatively setting a secondary branch under the original head. On the basis, the problems of storage management cost and storage efficiency are considered, and the cattleya number is creatively utilized to find the optimal solution of the secondary branch.

Description

Technical Field

[0001] The present invention belongs to the field of atmosphere monitoring, and more specifically, relates to a data storage method for a drone used for atmosphere and carbon emission monitoring. Background Art

[0002] As the global climate change problem becomes increasingly serious, carbon emission control and air pollution control have become important issues that need to be urgently addressed by countries around the world. In order to achieve accurate carbon emission monitoring and air quality assessment, scientists are increasingly relying on efficient monitoring technology, among which drones have become an indispensable tool. The application of drones in environmental monitoring has the advantages of high flexibility, convenient deployment, and strong real-time performance, especially in complex terrain and high-altitude environments, making it an ideal platform for collecting air pollution data and carbon emission information.

[0003] In actual carbon emission and atmosphere monitoring missions, drones need to be equipped with a variety of sensors, such as gas sensors, meteorological sensors, particulate matter monitoring equipment, temperature and humidity sensors, etc. These sensors can collect data such as various gas compositions, temperature, humidity, pressure, and particulate matter concentration in the atmosphere in real time, and transmit them back to the ground control center via wireless communication. Based on these data, researchers can analyze carbon emission sources, air quality changes, and environmental pollution trends, thereby providing data support for policy making and environmental governance.

[0004] However, during this monitoring process, the amount of data is huge and the real-time requirements are extremely high. Especially for long-term flight missions, the ability to collect, store and transmit data has become a key bottleneck of the UAV system.

[0005] In the UAV atmosphere and carbon emission monitoring mission, the amount of data collected by the sensors is usually very large, especially when multiple sensors are working at the same time, a large amount of high-dimensional data (usually composed of a large amount of metadata) may be generated every second. The UAV needs to process this data in real time during the flight, and also store it and send it back for analysis after the flight. Therefore, the selection and configuration of the storage module is particularly important in the design of the UAV.

[0006] Storage modules usually include built-in storage and expandable storage. Built-in storage is used to store raw data generated during flight, while expandable storage is used to expand storage space to meet the data storage needs of long-duration missions. However, the overall weight of the drone limits the volume and mass of its storage module, especially in the case of high load and long-duration flight. An overweight storage module will significantly affect flight performance and range. Therefore, how to design a storage module that can efficiently store a large amount of data without excessively increasing weight under limited weight and volume conditions is a key technical issue in drone atmospheric and carbon emission monitoring. Summary of the invention

[0007] In order to solve the deficiencies in the prior art, the purpose of the present invention is to solve the above-mentioned defects and further propose a data storage method for a drone used for atmosphere and carbon emission monitoring.

[0008] The present invention adopts the following technical solution.

[0009] The first aspect of the present invention discloses a data storage method for a drone used for atmosphere and carbon emission monitoring, comprising: based on the relevance of subtasks, setting secondary branches under a head, each secondary branch connecting between the head and a data pile, and ensuring that each secondary branch meets the principle of continuous storage;

[0010] Each subtask is used to describe the storage or clearing of metadata or a combination of the two; each head corresponds to metadata of a specific size.

[0011] Furthermore, the secondary branch is constructed in the form of a vector.

[0012] Furthermore, the relevance of the subtasks is predetermined based on the flight route of the UAV and the weather monitoring mission.

[0013] Furthermore, based on the relevance of the subtasks, a secondary branch is set under the head, including:

[0014] Set the number of secondary branches under each head to be equal to the number of tail tasks associated with the head; and the secondary branches correspond to the tail tasks one by one;

[0015] The tail task refers to the subtask describing the clearing of metadata or a combination of the two.

[0016] Furthermore, based on the relevance of the subtasks, a secondary branch is set under the head, including:

[0017] Step 1, determine the valid Cattleya sequence based on the relevance of the subtasks;

[0018] Step 2, split the valid Cattleya sequence into the least sequential sequences;

[0019] Step 3, setting the number of secondary branches to be equal to the number of sequential sequences; and the secondary branches correspond to the sequential sequences one by one.

[0020] Furthermore, it is characterized in that the effective Cattleya sequence calculated in step 1 is calculated based on the constructed Cattleya number.

[0021] Furthermore, the method of splitting the valid Cattleya sequence in step 2 is calculated based on dynamic programming or greedy algorithm.

[0022] The second aspect of the present invention discloses a data storage system for a drone used for atmosphere and carbon emission monitoring, which is used in the method described in the first aspect, and the system comprises: a sensor module, a storage module, a logic analysis module and a transmission module;

[0023] The sensor module is used to obtain metadata and save it to the storage module;

[0024] The logic analysis module is used to determine the logical order of tasks and package the sorted metadata and send it to the transmission module. In addition, it is also used to set up secondary branches under the head based on the relevance of subtasks.

[0025] In the storage module, each secondary branch replaces the original head to point to the data stack, and ensures that each secondary branch meets the principle of continuous storage;

[0026] The transmission module is used to send the packaged metadata to the corresponding server;

[0027] Each subtask is used to describe the storage or clearing of metadata or a combination of the two; each head corresponds to metadata of a specific size.

[0028] The third aspect of the present invention discloses a terminal, including a processor and a storage medium; the characteristics are:

[0029] The storage medium is used to store instructions;

[0030] The processor is used to operate according to the instructions to execute the steps of the method described in the first aspect.

[0031] The fourth aspect of the present invention discloses a computer-readable storage medium having a computer program stored thereon, characterized in that when the program is executed by a processor, the steps of the method described in the first aspect are implemented.

[0032] The beneficial effects of the present invention are that, compared with the prior art, the present invention has the following advantages:

[0033] (1) The core purpose of the present invention is to solve the storage module storage crash caused by metadata, and creatively set a secondary branch under the original head to solve the problem of disordered storage.

[0034] (2) On this basis, the present invention takes into account the issues of storage management cost and storage efficiency, and creatively uses the Cattelan number to find the optimal solution for the secondary branch. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 It is a schematic diagram of the difference between centralized storage and decentralized storage.

[0036] Figure 2AIt is a schematic diagram of centralized storage in the prior art.

[0037] Figure 2B In the prior art, Figure 2A Based on the above, a schematic diagram of centralized storage is shown after deleting some data.

[0038] Figure 2C In the prior art, Figure 2B Based on the above, a schematic diagram of centralized storage is shown after deleting some data.

[0039] Figure 3 It is a schematic diagram of centralized storage satisfying the continuous storage principle according to an embodiment of the present invention. DETAILED DESCRIPTION

[0040] The present application is further described below in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and cannot be used to limit the protection scope of the present application.

[0041] It is not difficult to understand that the characteristics of massive metadata storage determine that it must be stored in a centralized manner to reduce space (such as cookies) overhead. In a 64-bit system, the upper and lower cookies themselves occupy 4 bytes, while the metadata itself may only occupy one byte, which results in 80% of the space being occupied by invalid characters.

[0042] Figure 1 The difference between decentralized storage and centralized storage is shown in Figure 1. Among them, cookies are used to index metadata (i.e. Figure 1 in the .

[0043] Each centralized storage can be understood as a data pile, and the number of metadata stored in it represents the length of the data pile. Figure 1 In the example, the length of the data heap is 4.

[0044] In order to facilitate management, in the prior art, this data pile is usually in the form of a linked list (ie: Figure 2A The arrow in the figure is expanded as follows: Figure 2A shown.

[0045] It should be understood that the length of a data pile is a fixed value, which can usually be set to 20 to 50. For convenience in the present invention, the length of the data pile is uniformly set to 4. For example, Figure 2A A total of 2 data piles are shown in FIG.

[0046] exist Figure 2AIn the example, head points to the first metadata in the data heap, which means that once new metadata is stored in the storage module, its storage location will be stored in the order pointed by head. It can be understood that the subsequent metadata is concatenated with the previous metadata in the form of pointers.

[0047] Figure 2B The schematic diagram shows the sequential deletion of metadata5 and metadata3 from the storage module. It is understandable that when the subsequent storage module needs to insert new metadata (eg metadata7) again, it will be inserted into the original location of metadata3 based on the direction pointed by the head.

[0048] It should be understood that a storage module may include multiple heads, and based on the principle of object-oriented encapsulation, each head corresponds to metadata of a specific size. In other words, in the storage module, the centralized storage of metadata is not based on the data type, but on the size of the metadata itself.

[0049] In the prior art, in the application scenario of the present invention, the data storage system of the drone used for atmosphere and carbon emission monitoring includes at least: a sensor module, a storage module, a logic analysis module and a transmission module.

[0050] The sensor module is used to obtain metadata and save it to the storage module;

[0051] In some embodiments, the sensor module may include: a gas sensor for obtaining metadata such as carbon dioxide (CO2) concentration, nitrogen oxide (NO2), sulfur dioxide (SO2), and methane (CH4) concentration; a temperature and humidity sensor for obtaining metadata such as air temperature and relative humidity; a pressure sensor for obtaining metadata such as atmospheric pressure; a PM2.5 / PM10 sensor for obtaining metadata such as PM2.5 and PM10 particle concentrations; an infrared CO2 sensor for obtaining infrared detected CO2 concentrations for long-distance monitoring and other metadata; an oxygen sensor for obtaining metadata such as oxygen concentration in the air; a laser radar (LiDAR) for obtaining metadata such as ground elevation and urban building distribution for analyzing the spatial distribution of carbon emission sources; an ultraviolet radiation sensor for obtaining metadata such as the amount of solar ultraviolet radiation; a spectral imaging sensor for obtaining spectral data of gas components including CO2 and NO2 for analyzing metadata such as the content of various gases in the atmosphere.

[0052] The logic analysis module is used to determine the logical order of tasks and package the sorted metadata and send it to the transmission module;

[0053] The transmission module is used to send the packaged metadata to the corresponding server.

[0054] It is understandable that the tasks carried by drones often come from the needs of different business departments. The metadata types involved in each business need are often different, and these metadata are not directly related in some cases. For example:

[0055] Assuming that the environmental monitoring department needs to perform air quality monitoring tasks, the drone needs to collect gas composition data such as carbon dioxide (CO2) concentration and nitrogen oxide (NO2) concentration based on gas sensors and save these data to the storage module;

[0056] Assuming that the carbon emission management department needs to perform carbon source tracking tasks, the drone needs to collect ground elevation data based on LiDAR and save this data to the storage module;

[0057] Assuming that the weather forecast department needs to perform meteorological monitoring tasks, the drone needs to collect meteorological data such as wind speed, wind direction, and air pressure based on meteorological sensors and save these data to the storage module.

[0058] The metadata collected by each task is different and independent. Therefore, the metadata is not sent to the transmission module immediately after the collection is completed. Instead, based on the integrity of the task, all relevant metadata are sorted and packaged to ensure the integrity and timing of the data before sending it to the transmission module for transmission.

[0059] Therefore, the above scenario can be summarized as follows mathematically:

[0060] (1) Each task includes a head task and a tail task;

[0061] (2) The head task indicates the time to start acquiring metadata and saving it to the storage module, i.e., data storage;

[0062] (3) The tail task indicates the time when all metadata are sorted and packaged and sent to the transmission module, that is, data clearing.

[0063] It should be understood that the mission to be performed by the drone and the flight route of the drone are usually pre-set. In other words, the relevance of the subtasks is predetermined based on the flight route of the drone and the weather monitoring mission.

[0064] In addition, if the head task and the tail task of the task are consistent, preferably, the task can be represented as a subtask. That is, each subtask is used to describe the storage or clearing of metadata or a combination of the two.

[0065] In order to better describe the defects of the prior art, firstly, both the head task and the tail task are classified as subtasks. It should be particularly noted that the subtasks are different from the above-mentioned meteorological monitoring tasks. Assume that a certain meteorological monitoring task is responsible for obtaining the six types of data metadata1~metadata6, among which metadata1~metadata3 can also be used for another subsequent meteorological monitoring task (that is, in the other meteorological monitoring task, merged with the metadata therein to complete data sorting), and metadata4~metadata5 can also be used for another subsequent meteorological monitoring task (that is, in the other meteorological monitoring task, merged with the metadata therein to complete data sorting), and metadata6 is not used for subsequent tasks and is directly sent to the transmission module. It is not difficult to infer that the meteorological monitoring task will form 2+2+1=5 subtasks.

[0066] The head task and the tail task of the above task are consistent, which should be understood as: there is no other task whose start time or end time is between the start time and the end time of the task.

[0067] It is understandable that subtasks can be independent or causally related. For example, two subtasks of the same task must be causally related; in addition, two subtasks of different tasks can also be causally related. For example, since the flight route is pre-set, the execution order of the subtasks in the early stage of the flight must be after the execution order of the subtasks in the later stage of the flight.

[0068] The above-mentioned independence means that there is no order between the two subtasks, and the causal relationship means that there is a order between the two subtasks.

[0069] Assume that metadata1 to metadata6 are generated based on subtask A, and the execution order of subtasks is A->D->C->B. At the same time, assume that metadata2 and metadata4 are also used for subtask C, and metadata1 and metadata6 are also used for subtask B, that is, they are packaged and sent to the transmission module in subsequent subtasks.

[0070] Understandably, Figure 2B The state corresponds to: subtask A has been completed and is preparing to execute subtask B. At this time, the useless metadata3 and metadata5 have been cleared by subtask A.

[0071] Assume that subtask D generates metadata7 to metadata12 in sequence, and assume that metadata12 is also used for subtask B. Also, assume that subtask C does not generate metadata.

[0072] at this time, Figure 2C The state corresponds to: subtask C has been completed and is about to execute subtask B. At this time, the ineffective metadata7~metadata11 have been cleared by subtask D, and the ineffective metadata2 and metadata4 have been cleared by subtask C.

[0073] It is not difficult to find that although there is little metadata left in each data pile, Figure 2C The three data heaps in the data cannot be recycled (because none of them are empty). Assuming the length of the data heap is 50, this means that a piece of metadata occupies a storage space 50 times its own size, so it is very likely to cause (or not cause) a storage module storage crash.

[0074] Understandably, storage crash specifically means: it is precisely because there is a large amount of "metadata occupying storage space 50 times its own size", so there may be a head that has been occupying the storage module for a long time, which makes the storage module unable to allocate space for other heads. The essence of storage crash is that the metadata in a certain head occupies multiple data heaps, which makes it impossible for other heads to be allocated to data heaps, thus causing storage crash. Understandably, if there is only one head in the storage module, there will be no storage crash.

[0075] In real-world scenarios, when the number of subtasks is large enough, the order of tasks will become exponentially complex, which is likely to lead to the above-mentioned disordered storage problem.

[0076] In fact, as long as the metadata in the data pile meets the principle of continuous storage, the above storage crash will not occur. Based on this, the present invention discloses a data storage method for a drone used for atmosphere and carbon emission monitoring. The core of the method is: based on the relevance of subtasks, a secondary branch is set under the head, and each secondary branch is connected between the head and the data pile to replace the original head pointing to the data pile, and at the same time ensure that each secondary branch meets the principle of continuous storage, such as Figure 3 shown.

[0077] Figure 3The storage method under the secondary branch is shown as an example. Understandably, under the principle of continuous storage, each secondary branch, such as ch1 to ch6, can mount at most one data pile; and the mounting method can only be: from the middle of the data pile to the end of the data pile (for example: the data pile mounted by ch2), or from the beginning of the data pile to the middle of the data pile (for example: the data pile mounted by ch4). Therefore, in this case, it is impossible for a large amount of "a piece of metadata occupying a storage space 50 times the size of its own volume" to exist.

[0078] In some embodiments, the secondary branches are preferably constructed in the form of vectors.

[0079] It is easy to imagine that, in some embodiments, the number of secondary branches under each head can be set to be equal to the number of tail tasks associated with the head. Note: The tail task in this paragraph generally refers to a subtask that describes the clearing of metadata or a combination of the two; and each secondary branch corresponds to exactly one tail task, that is, the secondary branch corresponds to the tail task one by one.

[0080] However, this inevitably leads to more secondary branches under each head, which not only affects the storage rate but also significantly increases the storage management cost. In order to minimize the number of secondary branches, in the first embodiment, the secondary branches can be constructed based on the Cattelan number to obtain the minimum number of secondary branches.

[0081] Therefore, based on the relevance of subtasks, secondary branches are set under the head, including:

[0082] Step 1: Determine the valid Cattleya sequence based on the relevance of the subtasks.

[0083] Assume that the subtasks associated with a head are t1~t7, and the relevance of the subtasks is: t1~t4; t1~t6; t2~t3; t2~t6; t3~t5; t3~t7; t5~t7; then the corresponding Cattleya sequence is "12343561572467".

[0084] The Cattleya sequence can be considered as a "stitching" of Cattleya markers. Each Cattleya marker corresponds to a subtask in sequence. The steps of the Cattleya sequence are as follows:

[0085] (1) Subtask t1 is associated with t4 and t6, so the corresponding Cattelan marks (marked in ascending order of Arabic numerals) are “1” and “2”;

[0086] (2) Subtask t2 is associated with t3 and t6, so its corresponding Cattleya marks are “3” and “4”;

[0087] (3) Subtask t3 is associated with t2, and since the Cattleya corresponding to subtask t2 is marked as 3, subtask t3 is associated with the Cattleya corresponding to t2 and marked as "3";

[0088] (4) Subtask t3 is associated with t5 and t7, so its corresponding Cattleya marks are “5” and “6”;

[0089] (5) Subtask t4 is associated with t1, and since the Cattleya flag corresponding to subtask t1 associated with t4 is 1, subtask t4 is associated with the Cattleya flag corresponding to t1 as “1”;

[0090] (6) Subtask t5 is associated with t3, and since the Cattleya label corresponding to subtask t3 is 5, subtask t5 is associated with the Cattleya label corresponding to t3 as “5”;

[0091] (7) Subtask t5 is associated with t7, so its corresponding Cattelan mark is “7”;

[0092] (8) Subtask t6 is associated with t1 and t2, and since subtask t1 is associated with t6 and subtask t2 is associated with t6, the corresponding Cattleya labels are 2 and 4 respectively, then subtask t6 is associated with the Cattleya labels corresponding to t1 and t2 as “2” and “4”;

[0093] (9) Subtask t7 is associated with t3 and t5, and since subtask t3 is associated with t7 and subtask t5 is associated with t7, the corresponding Cattleya labels are 6 and 7 respectively, then subtask t7 is associated with the Cattleya labels corresponding to t3 and t5 as “6” and “7”;

[0094] By splicing the Cattleya markers mentioned in (1) to (9) above, we can obtain the Cattleya sequence "12343561572467".

[0095] It is not difficult to find that in the above Cattelan sequence, each number appears exactly once, and the largest number is equal to the number of associations of the subtasks.

[0096] Delete the first number that appears in the Cattleya sequence (i.e., delete the first occurrences of 1 to 7), and keep the remaining Cattleya sequence, which is the final valid Cattleya sequence, i.e., "3152467".

[0097] In summary, the effective Cattleya sequence calculated in step 1 can be calculated based on the constructed Cattleya number.

[0098] Step 2: Split the valid Cattleya sequence into the least number of sequential sequences.

[0099] A sequential sequence represents a monotonically increasing or monotonically decreasing sequence of numbers.

[0100] If the valid Cattleya sequence is "3152467", it is understandable that the least number of sequences it can be split into are "3567" and "124"; if the valid Cattleya sequence is "3154672", it is understandable that the least number of sequences it can be split into are "3567", "14" and "2"; or "356", "12" and "47"; or "35", "146" and "72", etc., but it cannot be split into 2 sequences;

[0101] In some embodiments, the method of splitting the valid Cattleya sequence in step 2 can be calculated based on dynamic programming or greedy algorithm.

[0102] Step 3, setting the number of secondary branches to be equal to the number of sequential sequences; and the secondary branches correspond to the sequential sequences one by one.

[0103] It can be verified that, in this case, in each secondary branch, the metadata meets the principle of continuous storage.

[0104] In step 3, the secondary branches correspond to the sequential sequences one by one, which means that each secondary branch is only responsible for (storing) the data of the subtask corresponding to the sequential sequence. For example, assuming that the valid Cattleya sequence is "3152467", and assuming that the minimum continuous sequential sequences are "3567" and "124", the number of secondary sequences under head is 2, that is, there are only ch1 (corresponding to the sequential sequence "3567") and ch2 (corresponding to the sequential sequence "124"). In addition, from step (2) of the Cattleya sequence, it can be seen that subtask t2 is associated with t3, and its corresponding Cattleya tag is "3". Since "3" is under the sequential sequence "3567", the metadata associated with t3 in subtask t2 can be stored under ch1, and the rest can be deduced in the same way.

[0105] It is understandable that the logic analysis module should store: the relevance of the subtasks (a matrix consisting of 0s and 1s), the valid Cattelan sequence and the sequential sequence.

[0106] Correspondingly, the present invention also discloses a data storage system for a drone used for atmosphere and carbon emission monitoring, comprising: a sensor module, a storage module, a logic analysis module and a transmission module;

[0107] The sensor module is used to obtain metadata and save it to the storage module;

[0108] The logic analysis module is used to determine the logical order of tasks and package the sorted metadata and send it to the transmission module. In addition, it is also used to set up secondary branches under the head based on the relevance of subtasks.

[0109] In the storage module, each secondary branch replaces the original head to point to the data stack, and ensures that each secondary branch meets the principle of continuous storage;

[0110] The transmission module is used to send the packaged metadata to the corresponding server;

[0111] Each subtask is used to describe the storage or clearing of metadata or a combination of the two; each head corresponds to metadata of a specific size.

[0112] The applicant of the present invention has made a detailed explanation and description of the implementation examples of the present invention in conjunction with the drawings in the specification. However, those skilled in the art should understand that the above implementation examples are only preferred implementation schemes of the present invention, and the detailed description is only to help readers better understand the spirit of the present invention, but not to limit the scope of protection of the present invention. On the contrary, any improvements or modifications based on the inventive spirit of the present invention should fall within the scope of protection of the present invention.

Claims

1. A data storage method for an unmanned aerial vehicle used for atmosphere and carbon emission monitoring, characterized in that: Based on the relevance of subtasks, a secondary branch is set under the head. Each secondary branch connects the head and the data pile, and ensures that each secondary branch meets the principle of continuous storage. Each subtask is used to describe the storage or clearing of metadata or a combination of the two; each head corresponds to metadata of a specific size.

2. The data storage method for a drone used for atmosphere and carbon emission monitoring according to claim 1, characterized in that: The secondary branch is constructed as a vector.

3. The data storage method for a drone for atmosphere and carbon emission monitoring according to claim 1, characterized in that: The relevance of the subtasks is predetermined based on the flight path of the UAV and the weather monitoring mission.

4. The data storage method for a drone for atmosphere and carbon emission monitoring according to claim 1, characterized in that: Based on the relevance of subtasks, set up secondary branches under head, including: Set the number of secondary branches under each head to be equal to the number of tail tasks associated with the head; and the secondary branches correspond to the tail tasks one by one; The tail task refers to the subtask describing the clearing of metadata or a combination of the two.

5. The data storage method for a drone used for atmosphere and carbon emission monitoring according to claim 1, characterized in that: Based on the relevance of subtasks, set up secondary branches under head, including: Step 1, determine the valid Cattleya sequence based on the relevance of the subtasks; Step 2, split the valid Cattleya sequence into the least sequential sequences; Step 3, setting the number of secondary branches to be equal to the number of sequential sequences; and the secondary branches correspond to the sequential sequences one by one.

6. The data storage method for a drone used for atmosphere and carbon emission monitoring according to claim 5, characterized in that: The effective Cattleya sequence calculated in step 1 is calculated based on the constructed Cattleya number.

7. The data storage method for a drone used for atmosphere and carbon emission monitoring according to claim 5, characterized in that: The method of splitting the valid Cattleya sequence in step 2 is calculated based on dynamic programming or greedy algorithm.

8. A data storage system for an unmanned aerial vehicle for atmosphere and carbon emission monitoring, used to execute the method according to any one of claims 1 to 7, characterized in that: The system includes: a sensor module, a storage module, a logic analysis module and a transmission module; The sensor module is used to obtain metadata and save it to the storage module; The logic analysis module is used to determine the logical order of tasks and package the sorted metadata and send it to the transmission module. In addition, it is also used to set up secondary branches under the head based on the relevance of subtasks. In the storage module, each secondary branch replaces the original head to point to the data stack, and ensures that each secondary branch meets the principle of continuous storage; The transmission module is used to send the packaged metadata to the corresponding server; Each subtask is used to describe the storage or clearing of metadata or a combination of the two; each head corresponds to metadata of a specific size.

9. A terminal comprising a processor and a storage medium; characterized in that: The storage medium is used to store instructions; The processor is configured to operate according to the instructions to execute the steps of the method according to any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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