A remote sensing test observation and processing system based on 5G network

Abstract

The present invention discloses a remote sensing test observation processing system based on a 5G network. The processing system records and processes the time and action instructions of users logging into the processing system, thereby obtaining the common login time periods of each user and the common action instructions within each common login time period. In the subsequent working process, the corresponding instructions within the corresponding time period are executed in advance using the time period supported by the network conditions, and the output data after execution is stored in a cache module, so that the corresponding users can quickly search and obtain part of the desired output data, thereby improving the smoothness of system operation.

Description

Technical Field

[0001] The present invention belongs to the field of remote sensing technology, and specifically relates to a remote sensing test observation and processing system based on a 5G network. Background Art

[0002] Remote sensing technology uses remote sensors to detect the properties of ground objects from the air. It identifies various types of ground objects based on the principle that different objects respond differently to the spectrum. In practical applications, remote sensors on aircraft, spacecraft, satellites and other flying objects above the ground are used to collect ground data and transmit it to ground-based data processing equipment for processing.

[0003] In actual operation, after different users log in to the remote sensing test observation system, they will collect, view and process different data according to their own work needs. However, the amount of data in the remote sensing test observation is large, and the processing and transmission time is long. The user's operation time after logging in is long, and the operation experience is poor. In order to solve the above problems, the present invention provides the following technical solutions. Summary of the Invention

[0004] The purpose of the present invention is to provide a remote sensing test observation and processing system based on a 5G network to solve the problem in the prior art that the large amount of data in remote sensing test observations leads to long processing and transmission time, affecting the operating experience.

[0005] The purpose of the present invention can be achieved through the following technical solutions:

[0006] A remote sensing test observation and processing system based on a 5G network, comprising:

[0007] 5G signal receiving module, communicating with the remote sensor;

[0008] The processed image storage module is used to store the output image information generated after the image processing module processes;

[0009] User login module, through which users log in to the observation and processing system and then read the data in the original information storage module and the image processing module;

[0010] Cache module, used for temporary storage of information;

[0011] The working method of the above-mentioned remote sensing test observation and processing system based on 5G network includes the following steps:

[0012] The first step is to obtain the stable monitoring segment of each account;

[0013] S1. Evenly divide a day into several login status monitoring segments, and obtain the login time of each account in each login status monitoring segment over the past m days. Taking the data of one account in one login status monitoring segment as an example, obtain a set of login time data t1, t2, ..., tm;

[0014] Where tj represents the login time of the user in the corresponding login status monitoring segment. If the user is in the login state in the corresponding login status monitoring segment but has not logged in since the login status monitoring segment, or the user is in the non-logged-in state in the corresponding login status monitoring segment, tj is marked as 0.

[0015] S2. Obtain the number of non-zero data between t1 and tm in the login time data, and obtain a ratio b of the number to m. If the ratio is greater than a preset value, the login status monitoring segment is considered to be a stable monitoring segment for the corresponding account. If b is less than or equal to the preset value, the login status monitoring segment is considered to be a chaotic monitoring segment for the corresponding account.

[0016] S3. Calibrate all stable monitoring segments of each user according to the method in steps S1 to S2;

[0017] S4. Obtain the remaining m1 login time data in the stable monitoring segment, excluding the values of 0, and obtain the time differences tc1, tc2, ..., tcm1 between the start time of the m1 login time data and the start time of the stable monitoring segment;

[0018] According to the formula Calculate the discrete value S of these m1 login time data. If S is greater than the preset value A1, delete the corresponding tci values in descending order according to |tci-tcp|. Then continue to calculate the discrete value S of the remaining login time data according to the above formula until S is less than or equal to the preset value A1. At this time, record the number of deleted tci values r1 and calculate the value of r1 / m1. If r1 / m1>γ, the stable monitoring segment is considered to be a decentralized stable monitoring segment. If r1 / m1≤γ, the stable monitoring segment is considered to be a concentrated stable monitoring segment.

[0019] Where 1≤i≤m1, tcp is the average value of tci participating in the calculation of discrete value S, and γ is a preset value;

[0020] The second step is to obtain the first-level, second-level, and third-level instructions of each account in different stable monitoring segments;

[0021] Step 3: For several accounts, within the preset T time period before a login status monitoring period:

[0022] If the login status monitoring segment is a chaotic monitoring segment, no data processing is performed;

[0023] If the login status monitoring segment is a stable monitoring segment, data processing is performed in the following order: the priority of the clustered stable monitoring segment is higher than that of the dispersed stable monitoring segment, the priority of the first-level instruction in the same stable monitoring segment is higher than that of the second-level instruction, and the priority of the second-level instruction is higher than that of the third-level instruction;

[0024] Data processing refers to executing corresponding action instructions and storing the results of execution into the cache module for temporary storage.

[0025] As a further solution of the present invention, the specific steps of the second step are:

[0026] SS1. Taking a stable monitoring segment corresponding to an account as an example, obtain the action instructions executed by the corresponding account in the stable monitoring segment in the past m days;

[0027] SS2. Obtain the number of times c that the same action instruction has appeared in the past m days, and obtain the value of c / m. If c / m>α, the action instruction is considered to be a first-level instruction. If α1<c / m≤α, the action instruction is considered to be a second-level instruction. If c / m≤α1, the action instruction is considered to be a third-level instruction, and α>α1.

[0028] If the same action instruction appears twice or more in the stable monitoring segment on the same day, it will only be counted once;

[0029] SS3. Obtain the first-level instructions, second-level instructions, and third-level instructions of each account in the corresponding stable monitoring segment according to the method in steps SS1 to SS2.

[0030] As a further solution of the present invention, in the third step, after the corresponding account executes the corresponding action instruction and logs out or the corresponding account does not execute the corresponding action instruction after the preset T1 time, the cache module deletes the corresponding content stored therein.

[0031] As a further solution of the present invention, in the third step, when an account logs in and inputs an action instruction, the priority of the action instruction is greater than the priority of the data processing.

[0032] As a further solution of the present invention, in the third step, for the clustered stable monitoring segment, the tcp value is obtained when it is calculated that r1 / m1≤γ; for the discrete stable monitoring segment, the discrete value S of its m1 login time data is obtained. During operation, for the clustered stable monitoring segment, the smaller the tcp value, the higher its priority; for the discrete stable monitoring segment, the smaller the discrete value S, the higher its priority.

[0033] Beneficial effects of the present invention:

[0034] (1) The present invention records and processes the time and action instructions of user login processing system, thereby obtaining the common login time period of each user and the common action instructions in each common login time period. In the subsequent working process, the corresponding instructions in the corresponding time period are executed in advance using the time period supported by the network status, and the output data after execution is stored in the cache module, so that the corresponding user can quickly search and obtain part of the desired output data, thereby improving the smoothness of system operation;

[0035] (2) The present invention collects the login time information of each account user, obtains the user's regular login time period and unusual login time period after processing, and then processes the data in the regular login time period to determine whether the user's login time in the regular login time period has further regularity, thereby further narrowing the judgment space. In the subsequent pre-caching action execution process, the action instructions with more concentrated regular historical login time are pre-processed first. Furthermore, by calculating the center point of the historical login time, the priority of the action instructions is further allocated according to the chronological order, thereby ensuring that most customers can process some action instructions and cache the results in advance without affecting the normal operation of the system network, thereby improving the smoothness of the system operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] The present invention will be further described below with reference to the accompanying drawings.

[0037] Figure 1 This is a schematic diagram of the framework structure of a remote sensing test observation and processing system based on 5G network in the present invention. DETAILED DESCRIPTION

[0038] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0039] A remote sensing test observation and processing system based on 5G network, such as Figure 1 Shown, including:

[0040] 5G signal receiving module, which communicates with the remote sensor and receives the original image information collected and transmitted by the remote sensor;

[0041] The original information storage module is used to store the original image information sent by the remote sensor and also to transmit the original image information to the image processing module;

[0042] The image processing module is used to process the original image information uploaded by the original information storage module and output a picture format that is convenient for the corresponding staff to observe. Specifically, the image processing module processes the original image information differently according to the purpose of the processed image and the observer, thereby obtaining different output picture information;

[0043] The processed image storage module is used to store the output image information generated after the image processing module processes;

[0044] User login module, through which users log in to the observation and processing system and then read the data in the original information storage module and the image processing module;

[0045] The cache module is used to temporarily store part of the information in the processed image storage module and the original information storage module. The cache module can be set to temporarily store part of the data to enable the subsequent rapid query of the target information by the corresponding account;

[0046] The above-mentioned working method of a remote sensing test observation and processing system based on a 5G network includes the following steps:

[0047] The first step is to monitor the login status of each account through the controller and obtain the active time of each account;

[0048] S1. Evenly divide a day into several login status monitoring segments, and obtain the login time of each account in each login status monitoring segment over the past m days. Taking the data of one account in one login status monitoring segment as an example, obtain a set of login time data t1, t2, ..., tm;

[0049] Where tj represents the login time of the user in the corresponding login status monitoring segment. If the user is in the login state in the corresponding login status monitoring segment but has not logged in since the login status monitoring segment, or the user is in the non-logged-in state in the corresponding login status monitoring segment, tj is marked as 0.

[0050] In one embodiment of the present invention, the duration of a login status monitoring segment is 30 minutes;

[0051] S2. Obtain the number of non-zero data between t1 and tm in the login time data, and obtain a ratio b of this number to m. If the ratio is greater than a preset value, the login status monitoring segment is considered to be a stable monitoring segment for the corresponding account. Conversely, if b is less than or equal to the preset value, the login status monitoring segment is considered to be a chaotic monitoring segment for the corresponding account, and no subsequent processing is performed on the chaotic monitoring segment.

[0052] S3. Calibrate all stable monitoring segments of each user according to the method in steps S1 to S2;

[0053] S4. Obtain the remaining m1 login time data in the stable monitoring segment, excluding the values of 0, and obtain the time differences tc1, tc2, ..., tcm1 between the start time of the m1 login time data and the start time of the stable monitoring segment;

[0054] According to the formula Calculate the discrete value S of these m1 login time data. If S is greater than the preset value A1, delete the corresponding tci values in descending order according to |tci-tcp|. Then continue to calculate the discrete value S of the remaining login time data according to the above formula until S is less than or equal to the preset value A1. At this time, record the number of deleted tci values r1 and calculate the value of r1 / m1. If r1 / m1>γ, then the stable monitoring segment is considered to be a decentralized stable monitoring segment. Conversely, if r1 / m1≤γ, then the stable monitoring segment is considered to be a concentrated stable monitoring segment.

[0055] Where 1≤i≤m1, tcp is the average value of tci participating in the calculation of discrete value S, and γ is a preset value;

[0056] The user login time points corresponding to the concentrated stable monitoring segment are relatively concentrated, while the user login time points corresponding to the dispersed stable monitoring segment are relatively dispersed;

[0057] This step collects login time information from each account user and processes it to obtain the user's common login time period and unusual login time period. The data within the common login time period is then processed to determine whether there are further patterns in the user's login time within the common login time period, thereby further narrowing the judgment space.

[0058] The second step is to obtain the hot operations of each account during different active periods;

[0059] SS1. Taking a stable monitoring segment corresponding to an account as an example, obtain the action instructions executed by the corresponding account in the stable monitoring segment in the past m days;

[0060] The action instruction includes the processing method of the original image information by the image processing module and the query content of the user;

[0061] The image processing module processes the original image information in the following ways: classifying different information collected by the remote sensor and calibrating the time of different types of remote sensor collection. When processing the original image information, the image processing module will read different types of remote sensing information collected at the same time and process them in a corresponding manner, or read the same type of remote sensing information at different time points and process them in a corresponding manner.

[0062] SS2. Obtain the number of times c that the same action instruction has appeared in the past m days, and obtain the value of c / m. If c / m>α, the action instruction is considered a first-level instruction. If α1<c / m≤α, the action instruction is considered a second-level instruction. If c / m≤α1, the action instruction is considered a third-level instruction.

[0063] Where α>α1;

[0064] If the same action instruction appears twice or more in the stable monitoring segment on the same day, it will only be counted once;

[0065] SS3. Obtain the first-level instructions, second-level instructions, and third-level instructions for each account in the corresponding stable monitoring segment according to the method in steps SS1 and SS2;

[0066] Based on the first step, this step obtains the hotspot action instructions in each stable monitoring segment and classifies the action instructions according to the frequency of use to facilitate subsequent priority judgment;

[0067] Step 3: Within T time before a login status monitoring period, for several accounts:

[0068] If the login status monitoring segment is a chaotic monitoring segment, no data processing is performed;

[0069] If the login status monitoring segment is a stable monitoring segment, data processing is performed in the following order: the priority of the clustered stable monitoring segment is higher than that of the dispersed stable monitoring segment, the priority of the first-level instruction in the same stable monitoring segment is higher than that of the second-level instruction, and the priority of the second-level instruction is higher than that of the third-level instruction;

[0070] Data processing refers to executing the corresponding action instruction and storing the result of the execution in the cache module for temporary storage. After the corresponding account executes the corresponding action instruction and logs out, or after the preset T1 time, the corresponding account does not execute the corresponding action instruction, the cache module deletes the corresponding content stored in it;

[0071] It should be noted that when an account logs in and enters an action command, the priority of the action command is greater than the priority of the above data processing.

[0072] In one embodiment of the present invention, for a clustered stable monitoring segment among the stable monitoring segments, the TCP value when r1 / m1≤γ is calculated is obtained. For a discrete stable monitoring segment among the stable monitoring segments, the discrete value S of the m1 login time data is obtained. When operating in the third step, for a clustered stable monitoring segment, the smaller the TCP value, the higher its priority. For a discrete stable monitoring segment, the smaller the discrete value S, the higher its priority.

[0073] In this step, for clustered stable monitoring segments, taking into account the instability of network conditions, the action instructions corresponding to users who may have logged in earlier are prioritized. For discrete stable monitoring segments, the action instructions corresponding to users with more concentrated login times are prioritized.

[0074] The present invention records and processes the time and action instructions of user login processing system, and then obtains the common login time period of each user and the common action instructions within each common login time period. In the subsequent working process, the corresponding instructions within the corresponding time period are executed in advance using the time period supported by the network conditions, and the output data after execution is stored in the cache module, so that the corresponding user can quickly search and obtain some of the desired output data, thereby improving the smoothness of system operation.

[0075] Throughout the specification, references to terms such as "one embodiment," "example," or "specific example" indicate that the specific features, structures, materials, or characteristics described in conjunction with that embodiment or example are included in at least one embodiment or example of the present invention. In this specification, schematic representations of these terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0076] The above contents are merely examples and explanations of the present invention. Those skilled in the art may make various modifications or additions to the described specific embodiments or replace them in similar ways. As long as they do not deviate from the invention or exceed the scope defined by the claims, they should all fall within the scope of protection of the present invention.

Claims

1. A remote sensing test observation and processing system based on 5G network, characterized in that: include: 5G signal receiving module, communicating with the remote sensor; The processed image storage module is used to store the output image information generated after the image processing module processes; User login module, through which users log in to the observation and processing system and then read the data in the original information storage module and the image processing module; Cache module, used for temporary storage of information; The working method of the above-mentioned remote sensing test observation and processing system based on 5G network includes the following steps: The first step is to obtain the stable monitoring segment of each account; S1. Evenly divide a day into several login status monitoring segments, and obtain the login time of each account in each login status monitoring segment over the past m days. Taking the data of one account in one login status monitoring segment as an example, obtain a set of login time data t1, t2, ..., tm; Where tj represents the login time of the user in the corresponding login status monitoring segment. If the user is in the login state in the corresponding login status monitoring segment but has not logged in since the login status monitoring segment, or the user is in the non-logged-in state in the corresponding login status monitoring segment, tj is marked as 0. S2. Obtain the number of non-zero data between t1 and tm in the login time data, and obtain a ratio b of the number to m. If the ratio is greater than a preset value, the login status monitoring segment is considered to be a stable monitoring segment for the corresponding account. If b is less than or equal to the preset value, the login status monitoring segment is considered to be a chaotic monitoring segment for the corresponding account. S3. Calibrate all stable monitoring segments of each user according to the method in steps S1 to S2; S4. Obtain the remaining m1 login time data in the stable monitoring segment, excluding the values of 0, and obtain the time differences tc1, tc2, ..., tcm1 between the start time of the m1 login time data and the start time of the stable monitoring segment; According to the formula Calculate the discrete value S of these m1 login time data. If S is greater than the preset value A1, delete the corresponding tci values in descending order according to |tci-tcp|. Then continue to calculate the discrete value S of the remaining login time data according to the above formula until S is less than or equal to the preset value A1. At this time, record the number of deleted tci values r1 and calculate the value of r1 / m1. If r1 / m1>γ, the stable monitoring segment is considered to be a decentralized stable monitoring segment. If r1 / m1≤γ, the stable monitoring segment is considered to be a concentrated stable monitoring segment. Where 1≤i≤m1, tcp is the average value of tci participating in the calculation of discrete value S, and γ is a preset value; The second step is to obtain the first-level, second-level, and third-level instructions of each account in different stable monitoring segments; Step 3: For several accounts, within the preset T time period before a login status monitoring period: If the login status monitoring segment is a chaotic monitoring segment, no data processing is performed; If the login status monitoring segment is a stable monitoring segment, data processing is performed in the following order: the priority of the clustered stable monitoring segment is higher than that of the dispersed stable monitoring segment, the priority of the first-level instruction in the same stable monitoring segment is higher than that of the second-level instruction, and the priority of the second-level instruction is higher than that of the third-level instruction; Data processing refers to executing corresponding action instructions and storing the results of execution into the cache module for temporary storage.

2. A remote sensing test observation and processing system based on 5G network according to claim 1, characterized in that: The specific steps of the second step are: SS1. Taking a stable monitoring segment corresponding to an account as an example, obtain the action instructions executed by the corresponding account in the stable monitoring segment in the past m days; SS2. Obtain the number of times c that the same action instruction has appeared in the past m days, and obtain the value of c / m. If c / m>α, the action instruction is considered to be a first-level instruction. If α1<c / m≤α, the action instruction is considered to be a second-level instruction. If c / m≤α1, the action instruction is considered to be a third-level instruction, and α>α1. If the same action instruction appears twice or more in the stable monitoring segment on the same day, it will only be counted once; SS3. Obtain the first-level instructions, second-level instructions, and third-level instructions of each account in the corresponding stable monitoring segment according to the method in steps SS1 to SS2.

3. A remote sensing test observation and processing system based on 5G network according to claim 2, characterized in that: In the third step, after the corresponding account executes the corresponding action instruction and logs out or the corresponding account does not execute the corresponding action instruction after the preset T1 time, the cache module deletes the corresponding content stored therein.

4. A remote sensing test observation and processing system based on 5G network according to claim 3, characterized in that: In the third step, when an account logs in and inputs an action instruction, the priority of the action instruction is greater than the priority of the data processing.

5. The remote sensing test observation and processing system based on 5G network according to claim 1, characterized in that: In the third step, for the clustered stable monitoring segment, obtain the tcp value when r1 / m1≤γ is calculated. For the discrete stable monitoring segment, obtain the discrete value S of its m1 login time data. During operation, for the clustered stable monitoring segment, the smaller the tcp value, the higher its priority. For the discrete stable monitoring segment, the smaller the discrete value S, the higher its priority.

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