Power grid data warehousing method based on metadata and remote sensing data

Abstract

The application provides a power grid data warehousing method based on metadata and remote sensing data, and belongs to the technical field of data processing, which comprises the following steps: performing preliminary processing on the obtained remote sensing data about the power grid; dividing the remote sensing data into basic items, change items and change items according to the deviation of the remote sensing data from a standard set; determining the range of the basic items, the change items and the change items in the remote sensing data in sequence, and determining the deviation degree of the change items and the change items relative to the standard set; uploading the range and the deviation degree of the basic items, the change items and the change items to a database for data analysis. The power grid data warehousing method based on metadata and remote sensing data provided by the application simplifies the remote sensing data by dividing the remote sensing data, determines the key factors in the remote sensing data by analyzing the change items and the change items, improves the data processing efficiency, and ensures the rapid extraction and analysis of the remote sensing data.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of data processing, and more particularly relates to a power grid data warehousing method based on metadata and remote sensing data. BACKGROUND

[0002] With the continuous improvement of technology, aerial remote sensing data rapid production systems are becoming the mainstream of the industry, and their division of labor is becoming increasingly refined and their scale is becoming increasingly large. The aerial remote sensing data produced by these systems has characteristics such as a large number of types and a large amount of data, and how to coordinate the relationship between the data required for production and the data production process and enable it to effectively serve the data production system is a problem currently faced. The traditional aerial remote sensing data production process often only focuses on the input and output of data, and ignores the internal relationship between metadata and product data in the production process, so on the one hand, the reuse of metadata is restricted,

[0003] The diversification of imaging methods and the enhancement of remote sensing data acquisition capabilities have led to the diversification and massification of remote sensing data. In order to effectively utilize these massive data in various remote sensing application systems in a timely manner, it is necessary to first organize and manage them in an orderly manner. At present, spatial database management is the mainstream management method for remote sensing data, and in the face of the constantly generated massive multi-source heterogeneous remote sensing data, how to quickly and accurately extract and analyze the data in the first time and improve the processing efficiency of remote sensing data has become an urgent problem to be solved. SUMMARY

[0004] The purpose of the present application is to provide a power grid data warehousing method based on metadata and remote sensing data, which aims to solve the problem of low remote sensing data processing efficiency and inability to quickly extract and analyze.

[0005] To achieve the above purpose, the technical solution adopted by the present application is to provide a power grid data warehousing method based on metadata and remote sensing data, comprising:

[0006] performing preliminary processing on the obtained remote sensing data about the power grid;

[0007] dividing the remote sensing data into a basic item, a change item and a change item according to the deviation of the remote sensing data from the standard set;

[0008] determining the range of the basic item, the change item and the change item in the remote sensing data in sequence, and determining the deviation degree of the change item and the change item relative to the standard set;

[0009] uploading the range of the basic item, the change item and the change item and the deviation degree to a database, and performing data analysis by the database.

[0010] In a possible implementation, the dividing the remote sensing data into the base item, the change item and the change item includes:

[0011] Dividing the same area in the remote sensing data as the standard set into the base item.

[0012] In a possible implementation, the preliminary processing of the acquired remote sensing data of the power grid includes:

[0013] Setting the first remote sensing data in a time interval as a reference, comparing the rest of the remote sensing data in the time interval with the reference, and determining the respective different areas;

[0014] Uploading, storing and transmitting the reference and all the different areas.

[0015] In a possible implementation, the dividing the remote sensing data into the base item, the change item and the change item according to the deviation of the remote sensing data from the standard set includes:

[0016] Before uploading, the acquired remote sensing data is divided by the local host computer in combination with the standard set.

[0017] In a possible implementation, the dividing the acquired remote sensing data by the local host computer in combination with the standard set includes:

[0018] Covering the same area of the standard set with the remote sensing data, setting a change interval and a change interval based on the standard set;

[0019] Comparing the remote sensing data with the information in the same position in the standard set, according to the deviation of the two and in combination with the change interval and the change interval, marking the information as one of the base item, the change item and the change item.

[0020] In a possible implementation, the uploading the range of the base item, the change item and the change item and the deviation degree to the database and performing data analysis by the database includes:

[0021] According to the operation of the power grid and the standard set, simulating the range of the change item and the change item and the deviation degree under each abnormal condition, and establishing a corresponding file;

[0022] According to the actually uploaded change item and the change item and in combination with the file, preliminarily judging the state of the power grid.

[0023] In a possible implementation, the data analysis by the database includes:

[0024] restore the data corresponding to the basic item according to the standard set and in combination with the range of the basic item;

[0025] restore the corresponding data according to the standard set and in combination with the range of the change item and the change item and the deviation degree;

[0026] integrate the restored data of the basic item, the change item and the change item to obtain the remote sensing data.

[0027] In a possible implementation, the uploading of the range of the basic item, the change item and the change item and the deviation degree to the database and the data analysis by the database comprise:

[0028] In a spatial region, the deviation degree corresponding to the basic item, the change item and the change item is visualized.

[0029] In a possible implementation, after the visualizing of the deviation degree corresponding to the basic item, the change item and the change item comprises:

[0030] The result of the visualizing is converted into data of other specifications.

[0031] In a possible implementation, after the visualizing of the change item and the change item comprises:

[0032] Different states of the power grid are set with corresponding codes, and the codes are labeled according to the result of the visualizing, so as to further reduce the amount of data required for transmission and analysis.

[0033] The power grid data warehousing method based on metadata and remote sensing data provided by the application has the beneficial effects that compared with the prior art, in the power grid data warehousing method based on metadata and remote sensing data, the obtained remote sensing data about the power grid is first preliminarily processed, after the processing, the remote sensing data is divided into a basic item, a change item and a change item according to the deviation between the remote sensing data and a standard set.

[0034] After the division, the range of the basic item, the change item and the change item in the remote sensing data is determined, and the deviation degree of the change item and the change item from the standard set is also determined. Finally, the range of the basic item, the change item and the change item in the remote sensing data and the determined deviation degree are uploaded, so that subsequent data analysis is performed.

[0035] In the application, the remote sensing data is simplified by dividing the remote sensing data, the key factors in the remote sensing data are determined by analyzing the change item and the change item, the efficiency of data processing is improved, and the rapid extraction and analysis of remote sensing data are ensured. Attached Figure Description

[0036] To more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0037] Figure 1 A flowchart illustrating the method for storing power grid data based on metadata and remote sensing data, provided in an embodiment of the present invention. Detailed Implementation

[0038] To make the technical problems to be solved, the technical solutions, and the beneficial effects of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present invention and are not intended to limit the present invention.

[0039] Please see Figure 1 The present invention will now describe the method for power grid data entry based on metadata and remote sensing data. The method for power grid data entry based on metadata and remote sensing data includes:

[0040] The acquired remote sensing data about the power grid was initially processed.

[0041] Based on the deviation between remote sensing data and the standard set, the remote sensing data is divided into basic items, altered items, and modified items.

[0042] The ranges of the basic, altered, and modified items in the remote sensing data are determined sequentially, and the degree of deviation of the altered and modified items relative to the standard set is determined.

[0043] The scope and degree of deviation of the basic items, changed items, and modified items are uploaded to the database for data analysis.

[0044] The beneficial effects of the power grid data entry method based on metadata and remote sensing data provided by the present invention are as follows: Compared with the prior art, the power grid data entry method based on metadata and remote sensing data of the present invention first performs preliminary processing on the acquired remote sensing data about the power grid. After the processing is completed, the remote sensing data is divided into basic items, changed items and modified items according to the deviation between the remote sensing data and the standard set.

[0045] After the division is completed, the ranges of the base item, the change item and the change item in the remote sensing data are determined, and the deviation degree of the change item and the change item from the standard set also needs to be determined. Finally, the ranges of the base item, the change item and the change item in the remote sensing data, and the determined deviation degree are uploaded, so that subsequent data analysis is carried out.

[0046] In the present application, the remote sensing data is simplified by dividing the remote sensing data, and the key factors in the remote sensing data are determined by analyzing the change item and the change item, so as to improve the efficiency of data processing and ensure the rapid extraction and analysis of remote sensing data.

[0047] Since the existing remote sensing data processing system is not perfect, in the traditional remote sensing infrastructure construction, each link of remote sensing processing is considered as an independent individual, the data cannot be mutually beneficial and interconnected, and even the data associated with each other often needs to be distributed by artificial distribution, so that the whole processing flow is very lagging and inefficient, and the automatic resource processing system has not been really established, and at the same time, the management personnel cannot supervise and control the working condition of the data processing flow in time, which has become an important problem hindering the production and application of remote sensing, and how to face and solve it is a higher requirement for modern remote sensing product processing system.

[0048] Since the 1960s, with the rise of space remote sensing technology, the acquisition technology of remote sensing data has become more and more perfect, and a stereoscopic, multi-angle, all-directional and all-weather observation network is being formed. In recent years, with the commercialization of domestic and foreign sub-meter high-resolution remote sensing satellites, the increase of aerial remote sensing operations, the popularization of ground mobile remote sensing equipment, and the rapid development of computer, network and communication technologies, the explosive growth of remote sensing data production has been promoted.

[0049] With the continuous development of aerial remote sensing technology, the acquisition capability of aerial remote sensing data has been significantly improved, and the collection of data is developing towards scale and intensification. In addition, aerial remote sensing images themselves have large data volume and rich product types, which puts forward higher requirements for the storage and production processing system of aerial remote sensing data.

[0050] Compared with the relatively mature satellite remote sensing data storage and distribution management technology, the current storage and distribution management level of aerial remote sensing data is relatively lagging behind, and most of them are in the stage of manual management, which seriously affects the rapid extraction and utilization of aerial remote sensing data information.

[0051] In the extraction and entry of the original remote sensing data into the system database process, the following disadvantages may occur: first, some original data is missing in the batch, resulting in the failure of extraction of all the remaining remote sensing data in the batch, which needs to be processed and then extracted again; second, the batch remote sensing data is too large, resulting in a long extraction period of the basic information of the entire batch remote sensing data, and if an exception occurs after the extraction is completed and the data is saved to the system data, the entire batch needs to be rectified, which is a large amount of work; third, if the data published to the system database in a single batch is incorrect, it is not convenient to determine the specific error items, and it is not convenient to process and trace the error data; fourth, due to the large amount of data submitted at a time, the submission to the warehouse may fail; fifth, the system database may crash due to operation errors, and the period for recovering the system database is long and the amount of data to be recovered is large.

[0052] Of course, in order to facilitate the processing and tracing of error data, a plurality of to-be-shared metadata can also be published in multiple times, but this will inevitably increase the number of server operations on the system database. However, frequent operations on the server of the system database will not only cause a serious burden on user access links, user link quantity, and memory, and in severe cases, it may even cause the server to crash.

[0053] With the continuous enrichment of earth observation technology and the continuous improvement of spatial information acquisition capability, people obtain extremely rich remote sensing data, and the richness of data is accompanied by the demand for rapid data service. In spatial information processing, the storage and management of remote sensing data is very important.

[0054] In spatial information processing, remote sensing data is always grid processed according to needs, forming grid data, and the storage and management of remote sensing data are performed based on the grid data. In the satellite data storage and management method based on grid, a core step is to use a spatial grid set to describe satellite remote sensing data, also known as grid subdivision of remote sensing data, which is a key technology and premise of grid satellite storage.

[0055] Only after the satellite remote sensing data is grid subdivided can the grid storage and management be performed. In the grid subdivision process of remote sensing data, a fixed scale grid (time or space) is commonly used to subdivide the remote sensing data. The characteristic of this subdivision method is that the subdivision process is simple, the computing power requirement is low, and the same scale spatial network is used to represent the remote sensing data.

[0056] In some embodiments of the power grid data warehousing method based on metadata and remote sensing data provided in the application, dividing the remote sensing data into a basic item, a change item, and a change item includes:

[0057] The area in the remote sensing data that is the same as the standard set is divided into a basic item.

[0058] Remote sensing data itself is relatively large, if the remote sensing data is not processed, will not only affect the work efficiency, more importantly, will occupy a very much resource, causing unnecessary waste.

[0059] In order to solve the above problems, the remote sensing data is divided into three in the application, respectively, the basic item, change item and change item. The basic item indicates the part of the remote sensing data in the non key area or the key area in the key area without change. The basic item is not the focus of data analysis, and is more as a part of supplementing the change item and the change item.

[0060] In the division of the basic item, a standard set can be set in advance, and the standard set is the state of the power grid under normal operation. In actual application and before data uploading, the current obtained remote sensing data is compared with the standard set by the local analysis module, and the part of the remote sensing data in the standard set is divided into the basic item.

[0061] It should be noted that the position and distribution of the basic item in the remote sensing data are indicated when the division is indicated, so as to facilitate the subsequent data recovery and analysis.

[0062] In some embodiments of the power grid data warehousing method based on metadata and remote sensing data provided in the application, the preliminary processing of the obtained remote sensing data about the power grid comprises:

[0063] The first remote sensing data in a time interval is set as a reference, the remaining remote sensing data in the time interval is compared with the reference, and the respective different regions are determined.

[0064] The reference and all different regions are uploaded, stored and transmitted.

[0065] The traditional method does not process the remote sensing data when uploading the remote sensing data, resulting in low efficiency of data transmission and processing. As for the application, in addition to the basic item, the change item and the change item, a stable item is also set. The stable item refers to the part of the data without change in a specific time interval.

[0066] In order to explain in more detail, if there are many same parts in the remote sensing data in a time interval, the first remote sensing data in the time interval is taken as a reference, and the next adjacent remote sensing data is compared to determine the same part of the two remote sensing data, and the same part of the next remote sensing data is set as the stable item.

[0067] At this time, it needs to be determined that the stable item not only includes the content, but also needs to mark the proportion and position of the remote sensing data belonging to other same interval.

[0068] It should be particularly pointed out that the stable item can cover the basic item, the change item and the change item, and the proposal of the stable item is mainly to reduce the amount of data required to be processed. In actual application, the earliest remote sensing data in the same interval, that is, the reference, can be uploaded first, and only the content and range different from the reference need to be uploaded for the remaining remote sensing data in the same interval.

[0069] In some embodiments of the power grid data warehousing method based on metadata and remote sensing data provided in the application, according to the deviation of the remote sensing data from the standard set, the remote sensing data is divided into a basic item, a change item and a change item, which comprises:

[0070] Before uploading, the remote sensing data obtained is divided by the local host computer in combination with the standard set.

[0071] The basic item, the change item and the change item are determined according to the standard set. The basic item indicates that part of the remote sensing data is in the standard set, and this part of data is not the focus of analysis. Two intervals, a change interval and a change interval, are set based on the standard set.

[0072] It should be particularly pointed out that although the change interval is different from the standard set, the degree of deviation is small, so it is only auxiliary judgment information. The change item has a larger deviation from the standard set. If the power grid is abnormal, the change item is mainly analyzed.

[0073] The specific implementation is that before uploading, the local host computer will first compare the remote sensing data with the standard set, and divide the part meeting the standard set as the basic item, the part in the change interval as the change item, and the part in the change interval as the change item.

[0074] In some embodiments of the power grid data warehousing method based on metadata and remote sensing data provided in the application, the remote sensing data obtained is divided by the local host computer in combination with the standard set, which comprises:

[0075] The standard set and the remote sensing data cover the same area, and the change interval and the change interval are set based on the standard set.

[0076] The information of the remote sensing data and the standard set in the same position is compared, and according to the deviation of the two and in combination with the change interval and the change interval, the information is marked as one of the basic item, the change item and the change item.

[0077] In order to simplify the remote sensing data, the standard set, the change interval and the change interval are all uploaded to the database, that is, the standard set and the interval of the corresponding area are stored in the database. After obtaining the remote sensing data, the local host computer compares the remote sensing data with the standard set, and the change interval and the change interval indicate how much the actual remote sensing data deviates from the standard set.

[0078] The base item indicates the same part in the remote sensing data as the standard set, and the position information of the same part is determined as the base item. The remaining part is the change item and the change item, and the boundaries of the change item and the change item can be determined by comparing the standard set and the remote sensing data and combining the change interval and the change interval. After the boundaries are determined, the deviation degree of each unit in the remote sensing data from the standard set needs to be determined.

[0079] Specifically, the smallest measurement unit of the remote sensing data is taken as a standard, and the deviation degree of the same position from the standard set is recorded one by one. Then, the deviation result is combined with the position of each measurement unit in the remote sensing data to generate a set, which can be uploaded as the change item and the change item.

[0080] In some embodiments of the power grid data warehousing method based on metadata and remote sensing data provided in the application, the ranges and deviation degrees of the base item, the change item and the change item are uploaded to the database, and the database performs data analysis, including:

[0081] According to the operation of the power grid and the standard set, the ranges and deviation degrees of the change item and the change item under each abnormal condition are simulated, and the corresponding archives are established.

[0082] According to the actually uploaded change item and change item and the archives, the state of the power grid is preliminarily judged.

[0083] In actual application, the state of the current power grid needs to be analyzed and judged according to the content reflected by the remote sensing data. Because the base item is the same as the standard set, the base item indicates that it is in a normal state. In order to judge the abnormal condition, the change item, especially the change item, needs to be analyzed.

[0084] In actual application, the standard set is analyzed as a basis. Specifically, the distribution and deviation of the change item and the change item under various conditions are analyzed based on the standard set, and then the corresponding archives are generated.

[0085] The simulated archives are compared with the information fed back by the actual remote sensing data. When the characteristics in the archives are met, it indicates that the operation of the current power grid is normal.

[0086] In some embodiments of the power grid data warehousing method based on metadata and remote sensing data provided in the application, the database performs data analysis, including:

[0087] According to the standard set and the range of the base item, the data corresponding to the base item is restored.

[0088] According to the standard set and in combination with the range of change items and change items and the degree of deviation, the corresponding data is restored.

[0089] The restored data of the basic item, the change item and the change item are integrated to obtain remote sensing data.

[0090] For some scenarios, remote sensing data needs to be restored based on the basic item, the change item and the change item in combination with the standard set. When the remote sensing data needs to be restored, first, the standard set at the corresponding position is intercepted as the actual remote sensing data according to the range indicated by the basic item based on the standard set.

[0091] According to the position indicated in the change item and the change item and the deviation from the standard set, the remaining part of the remote sensing data is determined, and finally the restored data of the plurality of parts is integrated to generate the actual detected remote sensing data.

[0092] In some embodiments of the power grid data warehousing method based on metadata and remote sensing data provided in the application, the range of the basic item, the change item and the change item and the degree of deviation are uploaded to the database, and the database performs data analysis, including:

[0093] In the spatial region, the deviation degree corresponding to the basic item, the change item and the change item is visualized.

[0094] The change item and the change item both represent the deviation from the standard set. For the power grid, different abnormal situations will cause corresponding changes in the corresponding regions of the remote sensing data. In the present application, only the change item and the change item are considered, because the change item and the change item represent the regions different from the standard set.

[0095] In order to more accurately analyze the change item and the change item, the change item and the change item need to be visualized in the present application.

[0096] The specific implementation is to take the standard set as the reference and place the standard set in a spatial coordinate system. The change item and the change item contain the position information of each unit, and the change item and the change item are also placed in the spatial coordinate system. The embodiment is to mark the units at the same position in the standard set, the change item and the change item at the same X and Y position, take the deviation recorded in the change item and the change item as the Z coordinate, and finally visualize the simulation of the deviation of the actual remote sensing information from the standard set in the coordinate system.

[0097] After visualization is completed, the range, extreme value and deviation of the simulated shape are analyzed, so that the state of the power grid can be judged.

[0098] In some embodiments of the power grid data warehousing method based on metadata and remote sensing data provided in the application, the deviation degree corresponding to the basic item, the change item and the change item is visualized after the visualization processing includes:

[0099] The result of the visualization processing is converted into other specification data.

[0100] When the visualization modeling of the change item and the change item is completed, the deviation of the remote sensing data from the standard set can be intuitively analyzed, and after visualization, not only the deviation is analyzed, but also the remote sensing data is converted into other types of data.

[0101] A specific embodiment is to set the basic item as a color, and the corresponding color is different as the deviation from the standard set is different, and finally the entire remote sensing data can be represented as a plane analysis diagram showing the deviation.

[0102] In some embodiments of the power grid data warehousing method based on metadata and remote sensing data provided in the application, the change item and the change item are visualized after the visualization processing includes:

[0103] Different states of the power grid are set corresponding to the corresponding code, and the codes are calibrated according to the result of the visualization processing, so as to further reduce the amount of data required for transmission and analysis.

[0104] By dividing the remote sensing data into basic items, change items and change items, and through visualization analysis, the current running state of the power grid can be determined. In order to facilitate the transmission and conversion between different types of data, different states of the power grid can be set corresponding to the corresponding code, and after the remote sensing data is analyzed and visualized, the corresponding code is generated, and the current power grid data can be converted into other forms of data through the code, and finally the exchange between different data is completed.

[0105] The above is only a preferred embodiment of the application, and is not used to limit the application, any modification, equivalent replacement and improvement made within the spirit and principle of the application should be included in the protection scope of the application.

Claims

1. A method for storing power grid data based on metadata and remote sensing data, characterized in that: include: Preliminary processing of the acquired remote sensing data on the power grid; Based on the deviation between the remote sensing data and the standard set, the remote sensing data is divided into basic items, changed items, and modified items; The ranges of the basic item, the changed item, and the altered item in the remote sensing data are determined sequentially, and the degree of deviation of the changed item and the altered item relative to the standard set is determined. The range of the basic items, the changed items, and the altered items, as well as the degree of deviation, are uploaded to the database for data analysis.

2. The method for power grid data import based on metadata and remote sensing data as described in claim 1, characterized in that, The process of dividing the remote sensing data into basic items, altered items, and modified items includes: The regions in the remote sensing data that are identical to the standard set are classified as the basic items.

3. The method for power grid data import based on metadata and remote sensing data as described in claim 2, characterized in that, The preliminary processing of the acquired remote sensing data on the power grid includes: The first remote sensing data within a time interval is set as the baseline, and the remaining remote sensing data within the time interval are compared with the baseline to determine their respective different regions. The benchmark and all different regions are uploaded, stored, and transmitted.

4. The method for power grid data import based on metadata and remote sensing data as described in claim 1, characterized in that, The step of dividing the remote sensing data into basic items, changed items, and modified items based on the deviation between the remote sensing data and the standard set includes: Before uploading, the local host computer divides the acquired remote sensing data using the standard set.

5. The method for storing power grid data based on metadata and remote sensing data as described in claim 4, characterized in that, The process of dividing the acquired remote sensing data by the local host computer in conjunction with the standard set includes: Make the standard set and the remote sensing data cover the same area, and set the change interval and the alteration interval based on the standard set; The remote sensing data is compared with information in the same location in the standard set. Based on the deviation between the two and combined with the change interval and the alteration interval, the information is labeled as one of the basic item, the change item, and the alteration item.

6. The method for power grid data import based on metadata and remote sensing data as described in claim 1, characterized in that, The step of uploading the range of the basic items, the changed items, and the altered items, as well as the degree of deviation, to the database for data analysis includes: Based on the power grid's operating conditions and the aforementioned standard set, the range of changes and modifications under various abnormal conditions, as well as the degree of deviation, are simulated, and corresponding records are established. Based on the actual uploaded changes and modifications, and in conjunction with the archive, a preliminary assessment of the power grid's status is made.

7. The method for power grid data import based on metadata and remote sensing data as described in claim 1, characterized in that, The data analysis performed by the database includes: The data corresponding to the basic items is restored based on the standard set and the range of the basic items; The corresponding data is restored based on the standard set, combined with the range of the changes and modifications, and the degree of deviation. The remote sensing data is obtained by integrating the data restored from the basic items, the changed items, and the modified items.

8. The method for power grid data import based on metadata and remote sensing data as described in claim 1, characterized in that, The step of uploading the range of the basic items, the changed items, and the altered items, as well as the degree of deviation, to the database for data analysis includes: Within the spatial region, the degree of deviation corresponding to the basic item, the changed item, and the alteration item is visualized.

9. The method for storing power grid data based on metadata and remote sensing data as described in claim 1, characterized in that, After visualizing the degree of deviation corresponding to the basic item, the changed item, and the modified item, the process includes: Convert the results of visualization processing into data of other specifications.

10. The method for storing power grid data based on metadata and remote sensing data as described in claim 9, characterized in that, After visualizing the changes and modifications, the process includes: Different states of the power grid are assigned corresponding codes, and each code is calibrated based on the results of visualization processing, so as to further reduce the amount of data required for transmission and analysis.

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