Data updating method suitable for natural resource right confirmation registration database

Abstract

The invention relates to the technical field of database updating, in particular to a data updating method suitable for a natural resource right confirmation registration database. According to the method, the ownership change degree is obtained according to the boundary area difference of collection and preorder collection in a natural resource interval, and the change prominence degree is obtained through the deviation condition of the ownership change degree under historical collection and the adjacent distribution of the natural resource interval; a feature analysis area is screened out through the ownership change degree; determining a change credible weight by combining the matching condition of the continuously updated feature analysis areas in the database and the change prominence; and lagging conditions of remote sensing data updating for acquisition are analyzed, and updating levels are obtained for updating in combination with the change credible weight and the ownership change degree. According to the method, the updating frequency is adaptively and dynamically adjusted by analyzing the local change between the acquired remote sensing data and the updated remote sensing data of the database in combination with the historical updating lagging condition of the database, so that more timely and reliable data support is provided during subsequent efficient management.

Description

Technical Field

[0001] The present invention relates to the technical field of database updating, and in particular to a data updating method applicable to a natural resource rights confirmation and registration database. Background Art

[0002] The confirmation and registration of natural resource ownership is a crucial measure to ensure the rational use and protection of resources and is fundamental to achieving sustainable development and promoting ecological progress. With the rapid development of remote sensing technology, the use of remote sensing imagery for natural resource monitoring and management has become a crucial tool. Remote sensing technology, through satellites, drones, and other equipment, can acquire large-scale, high-resolution image data, enabling dynamic monitoring of natural resources and timely identification of resource changes. This provides crucial data support for the confirmation and registration of natural resource ownership.

[0003] Existing natural resource rights confirmation and registration databases usually include: data collection, data processing, data entry, information query and management, etc. However, existing natural resource rights confirmation and registration databases usually lack an effective update reminder mechanism for data updates. Frequent registration and updates when natural resources do not change much will burden the database, increase management costs and time costs, but when there is a lag in updates, it cannot reflect changes in natural resources in a timely manner, resulting in a lack of real-time and reliable data support in policy making and resource management. Summary of the Invention

[0004] In order to solve the technical problems of excessive or delayed data updates in existing natural resource rights confirmation and registration databases, which increase management costs or lead to low data timeliness, the purpose of the present invention is to provide a data update method applicable to natural resource rights confirmation and registration databases. The technical solutions adopted are as follows: The present invention provides a data updating method applicable to a natural resource rights confirmation and registration database, the method comprising: Obtain current remote sensing data of the collection area, as well as historical remote sensing data and collection time; obtain historical remote sensing data of each update of the collection area from the natural resource rights confirmation and registration database; determine the natural resource area in each remote sensing data; Between each acquisition and the previous acquisition, the ownership change degree of each natural resource area at each acquisition is obtained based on the differences in area and shape of each natural resource area; the change prominence of each natural resource area at the current time is obtained through the adjacent distribution of each natural resource area in the current remote sensing data and the deviation from the ownership change degree at the time of historical acquisition; Based on the degree of ownership change of the current natural resource area, the characteristic analysis area is selected; the matching of the ownership change degree of the characteristic analysis area in the historical remote sensing data between each two updates is analyzed, and the change credibility weight of each characteristic analysis area is obtained by combining the change prominence of each characteristic analysis area; Analyze the deviation between the update registration time and the acquisition time when updating historical remote sensing data to obtain the update hysteresis; combine the ownership change degree of the natural resource area and the change credibility weight of the feature analysis area in the current remote sensing data, as well as the update hysteresis, to obtain the update level of the current remote sensing data; and provide update reminders based on the update level.

[0005] Furthermore, the method for obtaining the ownership change degree includes: For any natural resource area collected each time, determine the natural resource area at the same location in the previous collection; The area difference between the natural resource area and the natural resource area at the same location is used as the area change degree of the natural resource area; the difference in shape index between the natural resource area and the natural resource area at the same location is used as the shape change degree of the natural resource area; The ownership change degree of the natural resource area is obtained by combining the area change degree and shape change degree of the natural resource area.

[0006] Furthermore, the method for obtaining the change prominence includes: For any natural resource area in the current remote sensing data, obtain the ownership change degree of the natural resource area at each historical acquisition; After calculating the difference between the ownership change degree of the natural resource area at the current collection and the ownership change degree at each historical collection, the mean of all differences is calculated to obtain the change significance of the natural resource area; Count the number of other natural resource areas that share a common boundary with the natural resource area in the current remote sensing data to obtain the number of boundaries of the natural resource area; The change prominence of the natural resource area is obtained by combining the number of boundaries and the change significance of the natural resource area.

[0007] Furthermore, the screening method of the characteristic analysis area includes: In the current remote sensing data, the ownership change degrees of all natural resource areas are arranged in order from small to large to obtain the change sequence; Difference the change sequence, and take the smallest ownership change degree among the two ownership change degrees corresponding to the maximum difference value as the partition change degree; Natural resource areas where the degree of ownership change is greater than the degree of division change are taken as characteristic analysis areas.

[0008] Furthermore, the method for obtaining the variable trust weight includes: In the natural resource rights registration database, between two consecutive updates, the ownership change difference of each feature analysis area is calculated as the change difference of each feature analysis area between the two consecutive updates; the change difference of all feature analysis areas between the two consecutive updates is combined and negative correlation mapping is performed to obtain the matching degree between the two consecutive updates; The average of the matching degrees between all two adjacent updates in history is calculated as the matching correlation degree; the product of the change prominence of each feature analysis area and the matching correlation degree is used as the change credibility weight of each feature analysis area.

[0009] Furthermore, the method for obtaining the update hysteresis includes: Calculate the difference between the update registration time and the acquisition time corresponding to the historical remote sensing data at each update as the update deviation; negatively correlate the update level time of each update with the current time difference and normalize them to obtain the update weight; The update hysteresis is obtained by taking the weighted average of the update deviations of all updates based on the update weights.

[0010] Furthermore, the method for obtaining the update level includes: In the current remote sensing data, the ownership change degree of each feature analysis area is weighted and averaged based on the change credibility weight to obtain the current change update degree; The average value of ownership change of all natural resource areas outside the characteristic analysis area is taken as the average change impact; The product of the current change update degree, average change impact and update hysteresis is normalized and used as the current update level.

[0011] Furthermore, the update reminder based on the update level includes: When the update level is greater than the preset update threshold, an update notification reminder will be issued.

[0012] Furthermore, the method for obtaining the natural resource area at the same location includes: Obtain the center position of the natural resource area; when the natural resource area in the previous collection is closest to the center position of this natural resource area, the natural resource area corresponding to the previous collection will be used as the natural resource area at the same location as this natural resource area.

[0013] Furthermore, the determining of the natural resource area in each remote sensing data includes: Based on the deep learning model, semantic segmentation is performed on each remote sensing data, and each segmented area is regarded as a natural resource area.

[0014] The present invention has the following beneficial effects: The present invention first determines the ownership change degree based on the difference in boundary area between natural resource areas in actual remote sensing data and previously collected data, characterizing the degree of change in different types of natural resource areas. The prominence of natural resource change is determined by analyzing the deviation of ownership change degrees under historical collection and the adjacent distribution of natural resource areas. The degree of change in natural resource areas is analyzed based on continuous collection. Considering the historical updates of the database, the ownership change degree is first used to select characteristic analysis areas with significant current changes. Then, the matching between characteristic analysis areas of continuously updated remote sensing data in the natural resource rights confirmation and registration database is combined to reflect the degree of change in the analysis area during historical updates. Combined with the current prominence of change, a change credibility weight is determined for each characteristic analysis area, representing the weight of the regional change degree on the update after the historical update analysis of the database. Furthermore, considering the lag of remote sensing data during historical database updates, the current update may be regulated to improve the timeliness of the update. Finally, the change credibility weight, update hysteresis, and ownership change degree are combined to determine the update level for the update. The present invention analyzes the local changes between the collected remote sensing data and the database update remote sensing data, and adaptively and dynamically adjusts the update frequency in combination with the historical update lag of the database, so as to provide more timely and reliable data support in subsequent efficient management. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the prior art descriptions. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0016] Figure 1 A flow chart of a data updating method applicable to a natural resource rights confirmation and registration database provided by one embodiment of the present invention; Figure 2 A partial schematic diagram of a base map provided by one embodiment of the present invention; Figure 3 A schematic diagram of collecting remote sensing data of an area at different times provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0017] To further illustrate the technical means and effects employed by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, describes in detail a data updating method for a natural resource rights confirmation and registration database, including its specific implementation, structure, features, and effects. In the following description, references to different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.

[0018] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0019] The following describes in detail a specific scheme of a data updating method applicable to a natural resource rights confirmation and registration database provided by the present invention in conjunction with the accompanying drawings.

[0020] The natural resource rights confirmation and registration database is mainly based on remote sensing images as the basis for updating the natural resource rights confirmation and registration database. That is, the land use status results of natural resource surveys are used as the base map. In combination with various natural resource surveys or investigation results, through internal and external verification, the types, boundaries, areas, quantities and qualities of various natural resources in the unit are clarified. Figure 2 , which shows a partial schematic diagram of a base map provided by an embodiment of the present invention.

[0021] In this example, based on the latest orthophotos and the current land use map from the Third National Land Survey, remote sensing and GIS technologies were used to plot registered collective land ownership, state-owned construction land use rights, state-owned agricultural land use rights, and other parcels, as well as urban built-up area boundaries, urban planning area boundaries, major functional area boundaries, and ecological protection red lines, onto the current land use map and orthophotos to create a survey base map. GIS technology was also used to extract the natural resource boundaries required for natural resource area registration based on natural resource type data and data provided by various departments, such as water conservancy survey data (rivers, reservoirs, lakes), a single water system map, wetland survey data, state-owned forest farms, public welfare forests and forest parks, nature reserves, geological parks, forestry "one map" data, national census data, and a current land use database.

[0022] By analyzing and updating the changes in remote sensing data, we can achieve real-time monitoring of natural resource conditions and ensure the consistency of database information with actual conditions. To ensure the appropriateness and reliability of changes and updates, please refer to Figure 1 , which shows a flow chart of a data updating method applicable to a natural resource rights confirmation and registration database provided by one embodiment of the present invention, the method comprising the following steps: S1: Obtain the current remote sensing data of the collection area, as well as the historical remote sensing data and collection time; obtain the historical remote sensing data of each update of the collection area from the natural resource rights registration database; determine the natural resource area in each remote sensing data.

[0023] Remote sensing data is collected for an area that needs to be analyzed. Since database updates require certain actual changes in natural resources, the frequency of remote sensing data updated and registered in the natural resource rights registration database is not equal to the historical collection frequency of the corresponding remote sensing data.

[0024] In an embodiment of the present invention, a suitable remote sensing platform, such as a satellite, drone, or aerial photography, is selected according to monitoring requirements, and the required remote sensing data is downloaded through relevant remote sensing data provision platforms (such as NASA, USGS, ESA, etc.), ensuring that the acquired data meets the required resolution and band requirements, and performing geometric correction and radiation correction on the downloaded remote sensing data to eliminate errors caused by factors such as sensors, climate, and terrain.

[0025] Register different remote sensing data collected historically across the region to ensure they are in the same coordinate system for comparison and subsequent change analysis. At the same time, obtain historical remote sensing images of the collection area for each update in the natural resource rights registration database to analyze database update needs.

[0026] Since remote sensing data contains various types of natural resources, they are divided into different natural resource areas for subsequent change analysis. In an embodiment of the present invention, semantic segmentation is performed on each remote sensing data based on a deep learning model, and each segmented area is used as a natural resource area. Among them, deep learning models, such as U-Net, FCN, SegNet, etc., can automatically learn features through training, thereby achieving high-precision segmentation, and the geographical location coordinates of different natural resource areas can be obtained through GIS software. It should be noted that the semantic segmentation method of the deep learning model is a technical means well known to those skilled in the art, and will not be elaborated or limited here. Please refer to Figure 3 , which shows a schematic diagram of remote sensing data of a collection area at different times provided by an embodiment of the present invention.

[0027] S2: Between each acquisition and the previous acquisition, the ownership change degree of each natural resource area at each acquisition is obtained based on the differences in area and shape of each natural resource area; the change prominence of each natural resource area at the current time is obtained through the adjacent distribution of each natural resource area in the current remote sensing data and the deviation from the ownership change degree at the time of historical acquisition.

[0028] Based on currently acquired remote sensing data, natural resource boundaries may expand or new natural resource areas may emerge within existing natural resource areas due to the influence of climate and social behavior. These changes in natural resources provide important data support for updating the title registration database.

[0029] Therefore, the differences in changes in each natural resource area during the collection process are analyzed, and the ownership change degree is obtained to reflect the ownership changes that occurred in the natural resource area during the collection. Preferably, in an embodiment of the present invention, the method for obtaining the ownership change degree includes: For each natural resource area collected, the natural resource area in the previous collection is determined to be co-located with the natural resource area. This ensures that when analyzing changes in the natural resource area, the natural resource area in the previous collection corresponds to the same location. In this embodiment of the present invention, the center position of the natural resource area is obtained, and co-location is determined based on the regional center position. If the natural resource area in the current collection is closest to the center position of the natural resource area, the natural resource area corresponding to the previous collection is used as the co-located natural resource area of ​​the natural resource area.

[0030] Furthermore, the difference in area between the natural resource zone and the naturally located natural resource zone is used as the area change degree of the natural resource zone, reflecting the potential contraction and expansion changes to the natural resource zone. Furthermore, the difference in shape index between the natural resource zone and the naturally located natural resource zone is used as the shape change degree of the natural resource zone, reflecting the potential changes in boundary influences to the natural resource zone. In this embodiment of the present invention, the shape index is the ratio of the square of the perimeter to the area, and represents the boundary morphology of the region.

[0031] Finally, the ownership change degree of the natural resource area is obtained by combining the area change degree and the shape change degree of the natural resource area. In this embodiment of the present invention, the product of the area change degree and the shape change degree of the natural resource area is used as the ownership change degree of the natural resource area. The degree of change in the regional morphology is comprehensively considered to reflect the ownership change of the natural word. The higher the change, the higher the need for update.

[0032] The prominence of changes in current remote sensing data can be determined by comparing them with changes in historical data. More significant changes require more analysis and consideration. Therefore, the prominence of changes in current natural resource areas is determined by comparing the boundary information of the current natural resource area with the prominence of ownership changes relative to historical data.

[0033] Preferably, in an embodiment of the present invention, the method for obtaining the change prominence includes: For any natural resource area in the current remote sensing data, the ownership change degree of the natural resource area at each historical acquisition is obtained, and the changes in the natural resource area in the previous historical acquisition are analyzed. Furthermore, after calculating the difference between the ownership change degree of the natural resource area in the current acquisition and the ownership change degree of each historical acquisition, the mean of all differences is calculated to obtain the change significance of the natural resource area. The difference reflects the deviation between the current acquisition and each previous acquisition. The change significance is obtained by combining all differences. The higher the change significance, the more prominent the change in the natural resource area is compared with the changes in the historical data.

[0034] Considering the complex boundaries of natural resource zones, when a natural resource zone has many adjacent zones, the local species are more complex and changes in a single zone can lead to more other impacts. Therefore, we counted the number of other natural resource zones that share a border with the natural resource zone in the current remote sensing data to obtain the boundary number of the natural resource zone. This is the number of natural resource zones adjacent to the natural resource zone, which we then use as the boundary number.

[0035] Finally, the prominence of change in the natural resource area is obtained by combining the number of boundaries and the significance of change in the natural resource area. In this embodiment of the present invention, the product of the number of boundaries and the significance of change in the natural resource area is used as the prominence of change in the natural resource area. When the ownership change of the natural resource area is more prominent than that of historical data and the local distribution types are more complex, it means that the change prominence in the natural resource area is higher, that is, the prominence of change is greater.

[0036] S3: Based on the degree of ownership change of the current natural resource area, the characteristic analysis area is selected; the matching of the degree of ownership change of the characteristic analysis area in the historical remote sensing data between each two updates is analyzed, and the change credibility weight of each characteristic analysis area is obtained by combining the prominence of the change of each characteristic analysis area.

[0037] Taking into account the inconsistency of changes in natural resources, there may be some areas with large information changes. By screening out the areas with large changes, the natural resource rights confirmation and registration database focuses on matching and analyzing the impact of the areas with large changes during the update. Taking into account the differential tolerance of database information updates, the impact weight of the natural resource areas with large changes in the update is analyzed to improve the accuracy of subsequent update assessments based on changes.

[0038] Preferably, in an embodiment of the present invention, the characteristic analysis area is screened according to the ownership change degree of the current natural resource area. The method for screening the characteristic analysis area includes: In the current remote sensing data, the ownership change degrees of all natural resource areas are arranged in order from small to large to obtain a change sequence. The change sequence is differentiated, and the smallest ownership change degree of the two ownership change degrees corresponding to the maximum difference value is used as the partition change degree. The maximum difference value indicates that the ownership change degree can be divided into two parts, smaller and larger. In order to facilitate the screening of a larger part of the natural resource areas, the smaller value of the two ownership change degrees involved in the maximum difference calculation is used as the partition change degree.

[0039] At this time, the natural resource area where the ownership change degree is greater than the division change degree is taken as the feature analysis area. The feature analysis area is the area where the natural resource area information changes more. The proportion of the subsequent update assessment is adjusted based on the impact of the changes in this part on the update.

[0040] The matching of changes in the feature analysis area between consecutive updates of the database reflects the consistency of information changes in the feature analysis area during updates, indicating that the higher the relevance of the update to this part, combined with the prominence of the change, when the matching is higher and the prominence of the change is higher, the greater the attention paid to the feature change area, and the greater the proportion of its changes when considering updates.

[0041] Preferably, in an embodiment of the present invention, the method for obtaining a variable trust weight includes: First, in the natural resource rights registration database, the difference in ownership change degree of each characteristic analysis area is calculated between two adjacent updates as the change difference of each characteristic analysis area between two adjacent updates. Between each two updates, the more consistent the ownership change degree of the characteristic analysis area is, the more consistent the changes considered in the characteristic analysis area under the two updates are.

[0042] The degree of matching between the two updates is obtained by combining the change differences of all feature analysis areas between two adjacent updates and performing a negative correlation mapping. The change differences of all feature analysis areas under the two updates are comprehensively calculated. The smaller the comprehensive difference, the more consistent the overall changes in the feature analysis areas under the two updates, and the higher the degree of matching between the updates. In an embodiment of the present invention, the mean of the change differences of all feature analysis areas between two adjacent updates is negatively correlated to obtain the degree of matching between the two adjacent updates. It should be noted that negative correlation mapping is a technical means well known to those skilled in the art, and can be implemented in forms such as inverse proportion or negative exponential power, and is not limited or elaborated upon herein.

[0043] By analyzing the temporal sequence of consecutive updates and the prominence of changes in a single feature analysis area, we calculated the average matching degree between all two consecutive updates. This was used as the matching correlation, reflecting the overall matching performance under continuous updates. Furthermore, the product of the prominence of change and the matching correlation for each feature analysis area was used as the change credibility weight for each feature analysis area. The more consistent the changes in a feature analysis area under continuous updates, and the higher the prominence of the changes, the greater the impact of the feature analysis area on update analysis and the greater the weight of subsequent update considerations.

[0044] S4: Analyze the deviation between the update registration time and the acquisition time when updating historical remote sensing data to obtain the update hysteresis; combine the ownership change degree of the natural resource area in the current remote sensing data and the change credibility weight of the feature analysis area, as well as the update hysteresis, to obtain the update level of the current remote sensing data; and provide update reminders based on the update level.

[0045] During the process of updating the natural resource title registration database, there will be some data update lags. There is a certain time difference between the information changes of natural resources in the registration database and the actual collection of remote sensing data. Therefore, for the existing update lags, the more serious the update lag, the more necessary it is to increase the reminder level.

[0046] In an embodiment of the present invention, the deviation between historical update and acquisition time is analyzed to obtain update hysteresis, including: The difference between the update registration time and the acquisition time corresponding to the historical remote sensing data at each update is calculated as the update deviation, which characterizes the time lag of each update. The update level time of each update is negatively correlated with the current time difference and normalized to obtain the update weight. The closer the lag is to the current time, the higher the degree of consideration is. The smaller the time difference is from the current time difference, the more lag is considered, so the update weight is greater. It should be noted that normalization is a technical means well known to those skilled in the art. The normalization can be linear normalization or standard normalization, etc. The specific normalization method is not limited here.

[0047] Finally, based on the update weight, the update deviation of all updates is weighted and averaged to obtain the update hysteresis. That is, the update deviation of each update is weighted by the update weight, and the mean of the weighted products of all updates is calculated to obtain the update hysteresis, which reflects the time lag effect of database registration updates.

[0048] Finally, the update level is comprehensively evaluated by considering the current ownership changes of natural resources, the change credibility weight of the feature analysis area, and the update hysteresis. The higher the update level, the greater the necessity of the update. Preferably, in an embodiment of the present invention, the method for obtaining the update level includes: In the current remote sensing data, the ownership change degree of each feature analysis area is weighted and averaged based on the change credibility weight to obtain the current change update degree. This is done by multiplying the change credibility weight of each feature analysis area by the ownership change degree, and then calculating the average of the weighted products across all feature analysis areas to obtain the change update degree. Feature analysis areas with higher change credibility have a higher proportion of change analysis, and the impact of changes in feature analysis areas is comprehensively considered.

[0049] Outside the characteristic analysis area with larger changes, other natural resource areas will also undergo certain changes. Then, the impact of the changes on other natural resource areas will be directly and comprehensively analyzed, and the average of the ownership change degrees of all natural resource areas outside the characteristic analysis area will be used as the average change impact degree to reflect the impact of changes in other natural resource areas.

[0050] Finally, the product of the current change update degree, average change impact and update lag is normalized as the current update level, taking into account the change impact and lag situation. The greater the change impact and the more serious the current lag situation, the higher the update reminder level.

[0051] Therefore, update reminders are issued based on the update level. In this embodiment of the present invention, when the update level exceeds the preset update threshold, an update notification reminder is issued, where the preset update threshold is set to 0.6. The specific value can be adjusted by the implementer according to the specific implementation scenario and is not limited here. Based on the reminder, a specific update process can be formulated later, including steps such as data review, update implementation, and information feedback, to ensure the standardization and effectiveness of the update; In an embodiment of the present invention, the effectiveness of database updates through update level reminders is regularly evaluated, including the timeliness, quality, and support for business decisions of the updates, and the thresholds, reminder methods, and update processes are continuously optimized based on the evaluation results, making the entire system more efficient and reliable.

[0052] In summary, the present invention first determines the ownership change degree based on the difference in boundary area between the natural resource areas in the actual remote sensing data collected and the previously collected data, thereby characterizing the degree of change in different types of natural resource areas. The prominence of natural resource change is determined by analyzing the deviation of the ownership change degree under historical collection and the adjacent distribution of natural resource areas. The degree of change in natural resource areas is analyzed based on continuous collection. Considering the historical update history of the database, the ownership change degree is first used to filter out feature analysis areas with significant current changes. Then, the matching between feature analysis areas of continuously updated remote sensing data in the natural resource rights confirmation and registration database is combined to reflect the degree of change in the analysis area during historical updates. Combined with the current prominence of change, a change credibility weight is determined for each feature analysis area, representing the weight of the impact of the regional change degree on the update after the historical update analysis of the database. Furthermore, considering the lag of remote sensing data during historical database updates, the current update may be regulated to improve the timeliness of the update. Finally, the change credibility weight, update lag, and ownership change degree are combined to determine the update level for the update. The present invention analyzes the local changes between the collected remote sensing data and the database update remote sensing data, and adaptively and dynamically adjusts the update frequency in combination with the historical update lag of the database, so as to provide more timely and reliable data support in subsequent efficient management.

[0053] It should be noted that the order in which the embodiments of the present invention are described above is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0054] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments.

Claims

1. A data updating method applicable to a natural resource rights registration database, characterized in that: The method comprises: Obtain current remote sensing data of the collection area, as well as historical remote sensing data and collection time; obtain historical remote sensing data of each update of the collection area from the natural resource rights confirmation and registration database; determine the natural resource area in each remote sensing data; Between each acquisition and the previous acquisition, the ownership change degree of each natural resource area at each acquisition is obtained based on the differences in area and shape of each natural resource area; the change prominence of each natural resource area at the current time is obtained through the adjacent distribution of each natural resource area in the current remote sensing data and the deviation from the ownership change degree at the time of historical acquisition; Based on the degree of ownership change of the current natural resource area, the characteristic analysis area is selected; the matching of the ownership change degree of the characteristic analysis area in the historical remote sensing data between each two updates is analyzed, and the change credibility weight of each characteristic analysis area is obtained by combining the change prominence of each characteristic analysis area; Analyze the deviation between the update registration time and the acquisition time when updating historical remote sensing data to obtain the update hysteresis; combine the ownership change degree of the natural resource area and the change credibility weight of the feature analysis area in the current remote sensing data, as well as the update hysteresis, to obtain the update level of the current remote sensing data; and provide update reminders based on the update level.

2. A data updating method applicable to a natural resource rights confirmation and registration database according to claim 1, characterized in that: The method for obtaining the ownership change degree includes: For any natural resource area collected each time, determine the natural resource area at the same location in the previous collection; The area difference between the natural resource area and the natural resource area at the same location is used as the area change degree of the natural resource area; the difference in shape index between the natural resource area and the natural resource area at the same location is used as the shape change degree of the natural resource area; The ownership change degree of the natural resource area is obtained by combining the area change degree and shape change degree of the natural resource area.

3. A data updating method applicable to a natural resource rights confirmation and registration database according to claim 1, characterized in that: The method for obtaining the change prominence includes: For any natural resource area in the current remote sensing data, obtain the ownership change degree of the natural resource area at each historical acquisition; After calculating the difference between the ownership change degree of the natural resource area at the current collection and the ownership change degree at each historical collection, the mean of all differences is calculated to obtain the change significance of the natural resource area; Count the number of other natural resource areas that share a common boundary with the natural resource area in the current remote sensing data to obtain the number of boundaries of the natural resource area; The change prominence of the natural resource area is obtained by combining the number of boundaries and the change significance of the natural resource area.

4. A data updating method applicable to a natural resource rights confirmation and registration database according to claim 1, characterized in that: The screening method of the characteristic analysis area comprises: In the current remote sensing data, the ownership change degrees of all natural resource areas are arranged in order from small to large to obtain the change sequence; Difference the change sequence, and take the smallest ownership change degree among the two ownership change degrees corresponding to the maximum difference value as the partition change degree; Natural resource areas where the degree of ownership change is greater than the degree of division change are taken as characteristic analysis areas.

5. A data updating method applicable to a natural resource rights confirmation and registration database according to claim 1, characterized in that: The method for obtaining the variable trust weight includes: In the natural resource rights registration database, between two consecutive updates, the ownership change difference of each feature analysis area is calculated as the change difference of each feature analysis area between the two consecutive updates; the change difference of all feature analysis areas between the two consecutive updates is combined and negative correlation mapping is performed to obtain the matching degree between the two consecutive updates; The average of the matching degrees between all two adjacent updates in history is calculated as the matching correlation degree; the product of the change prominence of each feature analysis area and the matching correlation degree is used as the change credibility weight of each feature analysis area.

6. A data updating method applicable to a natural resource rights confirmation and registration database according to claim 1, characterized in that: The method for obtaining the update hysteresis includes: Calculate the difference between the update registration time and the acquisition time corresponding to the historical remote sensing data at each update as the update deviation; negatively correlate the update level time of each update with the current time difference and normalize them to obtain the update weight; The update hysteresis is obtained by taking the weighted average of the update deviations of all updates based on the update weights.

7. A data updating method applicable to a natural resource rights confirmation and registration database according to claim 1, characterized in that: The method for obtaining the update level includes: In the current remote sensing data, the ownership change degree of each feature analysis area is weighted and averaged based on the change credibility weight to obtain the current change update degree; The average value of ownership change of all natural resource areas outside the characteristic analysis area is taken as the average change impact; The product of the current change update degree, average change impact and update hysteresis is normalized and used as the current update level.

8. A data updating method applicable to a natural resource rights confirmation and registration database according to claim 1, characterized in that: The update reminder based on the update level includes: When the update level is greater than the preset update threshold, an update notification reminder will be issued.

9. A data updating method applicable to a natural resource rights confirmation and registration database according to claim 2, characterized in that: The method for obtaining the natural resource area at the same location includes: Obtain the center position of the natural resource area; when the natural resource area in the previous collection is closest to the center position of this natural resource area, the natural resource area corresponding to the previous collection will be used as the natural resource area at the same location as this natural resource area.

10. A data updating method applicable to a natural resource rights confirmation and registration database according to claim 1, characterized in that: The determining of the natural resource area in each remote sensing data comprises: Based on the deep learning model, semantic segmentation is performed on each remote sensing data, and each segmented area is regarded as a natural resource area.

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