A data updating method suitable for natural resource right registration database

Abstract

The present application relates to the technical field of database updating, and in particular to a data updating method suitable for natural resource right registration database. According to the boundary area difference between the current collection and the previous collection in the natural resource area, the right ownership change degree is obtained, and through the deviation of the right ownership change degree under the historical collection and the adjacent distribution of the natural resource area, the change prominence is obtained. The characteristic analysis area is screened out through the right ownership change degree. The change credible weight is determined by combining the matching condition of the characteristic analysis area between the continuous updating in the database and the change prominence. The lag condition of the remote sensing data updating for the collection is analyzed, and the updating grade is obtained by combining the change credible weight and the right ownership change degree to perform updating. Through the local change analysis between the collection remote sensing data and the database updating remote sensing data, the updating frequency is dynamically adjusted adaptively in combination with the historical updating lag condition of the database, so that more timely and reliable data support is provided for subsequent efficient management.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of database updating, and particularly relates to a data updating method suitable for natural resource right registration database. BACKGROUND

[0002] Natural resource right registration is an important measure to ensure the rational use and protection of resources, and is the basis for realizing sustainable development and ecological civilization construction. With the rapid development of remote sensing technology, using remote sensing images for natural resource monitoring and management has become an important means. Remote sensing technology can obtain large-scale, high-resolution image data through satellites, unmanned aerial vehicles and other devices, dynamically monitor natural resources, and timely discover resource changes. This provides important data support for natural resource right registration.

[0003] The existing natural resource right registration database usually includes data collection, data processing, data entry, information query and management, etc. However, the existing natural resource right registration database usually lacks an effective update reminder mechanism in data updating. When the natural resources change little, frequent registration and updating will cause a burden on the database and increase the management cost and time cost. However, when the update is lagging, the changes in natural resources cannot be timely reflected, resulting in a lack of real-time and reliable data support in policy making and resource management. SUMMARY

[0004] In order to solve the technical problems of over or lagging in data updating in the existing natural resource right registration database, increase the management cost or lead to low data timeliness, the purpose of the present application is to provide a data updating method suitable for natural resource right registration database, and the technical scheme adopted is as follows:

[0005] The present application provides a data updating method suitable for natural resource right registration database, which comprises:

[0006] Obtaining current remote sensing data of the collection area, as well as historical remote sensing data and collection time; obtaining historical remote sensing data of each update of the collection area from the natural resource right registration database; determining the natural resource area in each remote sensing data;

[0007] Between each collection and previous collection, obtaining the right ownership change degree of each natural resource area at each collection according to the area shape difference of each natural resource area; obtaining the change prominence of each natural resource area at present by the adjacency distribution of each natural resource area in the current remote sensing data and the deviation of the right ownership change degree at the time of historical collection;

[0008] Screening a characteristic analysis area based on the size of the ownership change degree of the current natural resource area; analyzing the matching condition of the ownership change degree of the characteristic analysis area in the historical remote sensing data between each two updates, and obtaining the change credible weight of each characteristic analysis area by combining the change prominence of each characteristic analysis area;

[0009] Obtaining the update lag by analyzing the deviation between the update registration time and the collection time when updating the historical remote sensing data; obtaining the update level of the current remote sensing data by combining the ownership change degree of the natural resource area and the change credible weight of the characteristic analysis area in the current remote sensing data, and the update lag; and performing update reminding based on the update level.

[0010] Further, the method for obtaining the ownership change degree comprises:

[0011] For any one natural resource area collected each time, determining a same-position natural resource area of the natural resource area in the previous collection;

[0012] Taking the area difference between the natural resource area and the same-position natural resource area as the area change degree of the natural resource area; and taking the difference between the shape index of the natural resource area and the same-position natural resource area as the shape change degree of the natural resource area;

[0013] Obtaining the ownership change degree of the natural resource area by combining the area change degree and the shape change degree of the natural resource area.

[0014] Further, the method for obtaining the change prominence comprises:

[0015] For any one natural resource area in the current remote sensing data, obtaining the ownership change degree of the natural resource area in each historical collection;

[0016] After calculating the difference between the ownership change degree of the natural resource area in the current collection and the ownership change degree of the natural resource area in each historical collection, obtaining the change prominence of the natural resource area by calculating the mean value of all the differences;

[0017] Obtaining the boundary quantity of the natural resource area by counting the number of other natural resource areas in the current remote sensing data which have a common boundary with the natural resource area;

[0018] Obtaining the change prominence of the natural resource area by combining the boundary quantity and the change prominence of the natural resource area.

[0019] Further, the method for screening the characteristic analysis area comprises:

[0020] Arranging the ownership change degrees of all the natural resource areas in the current remote sensing data in the order from small to large to obtain a change sequence;

[0021] Differencing the change sequence, taking the minimum of the two weight change degrees corresponding to the maximum difference value as the division change degree;

[0022] Taking the natural resource area with the weight change degree greater than the division change degree as a feature analysis area.

[0023] Further, the method for obtaining the variable credible weight comprises:

[0024] In the natural resource right registration database, the weight change degree difference of each feature analysis area between adjacent two updates is calculated as the change difference of each feature analysis area between adjacent two updates; the change differences of all feature analysis areas between adjacent two updates are combined and negatively correlated to obtain the matching degree between adjacent two updates.

[0025] The average of the matching degrees between all adjacent two updates in history is calculated as the matching correlation degree; the product of the change prominence degree of each feature analysis area and the matching correlation degree is taken as the variable credible weight of each feature analysis area.

[0026] Further, the method for obtaining the update lag comprises:

[0027] The difference between the update registration time and the collection time of the historical remote sensing data corresponding to each update is calculated as the update bias degree; the update level time of each update is negatively correlated to the current time difference and normalized to obtain the update weight.

[0028] The update bias degrees of all updates are weighted and averaged based on the update weight to obtain the update lag.

[0029] Further, the method for obtaining the update level comprises:

[0030] In the current remote sensing data, the weight change degree of each feature analysis area is weighted and averaged based on the variable credible weight to obtain the current variable update degree.

[0031] The average of the weight change degrees of all natural resource areas outside the feature analysis area is taken as the average change influence degree.

[0032] The product of the current variable update degree, the average change influence degree and the update lag is normalized as the current update level.

[0033] Further, the update reminding based on the update level comprises:

[0034] When the update level is greater than a preset update threshold, an update notification reminder is performed.

[0035] Further, the method for obtaining the same location natural resource area comprises:

[0036] obtaining a center position of the natural resource area; and taking the natural resource area corresponding to the previous acquisition as the same-position natural resource area of the natural resource area between the nearest time of the natural resource area in the center position and the natural resource area in the current acquisition.

[0037] Further, the determination of the natural resource area in each remote sensing data comprises:

[0038] performing semantic segmentation on each remote sensing data based on a deep learning model, and taking each segmented region as a natural resource area.

[0039] The present application has the following beneficial effects:

[0040] The present application first obtains the ownership change degree according to the boundary area difference between the natural resource area in the actually acquired remote sensing data and the previous acquisition data, representing the change degree of the current different types of natural resource area. The change prominence of the natural resource is obtained through the deviation of the ownership change degree in the historical acquisition and the adjacency distribution of the natural resource area, and the change degree of the natural resource area is analyzed from the continuous acquisition. Considering the historical update of the database, the feature analysis area with more significant change is first screened out through the ownership change degree. Then, the matching condition between the feature analysis areas in the continuous update remote sensing data in the natural resource right registration database is combined to reflect the change degree of the analysis area in the historical update, and the change credible weight of each feature analysis area is determined according to the current change prominence, representing the weight of the influence of the degree of regional change on the update based on the analysis of the historical update of the database. Further considering the lag of the remote sensing data in the historical update of the database, the current update can be regulated to improve the timeliness of the update, and finally the update grade is obtained for updating by combining the change credible weight, the update lag and the ownership change degree. The present application analyzes the local change between the acquired remote sensing data and the database updated remote sensing data, and dynamically adjusts the update frequency according to the historical update lag of the database, so as to provide more timely and reliable data support for subsequent efficient management. BRIEF DESCRIPTION OF DRAWINGS

[0041] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art and the advantages thereof, the drawings needed to be used in the embodiments or the prior art description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and those skilled in the art can also obtain other drawings according to these drawings without any creative effort.

[0042] Figure 1 A data update method flowchart suitable for a natural resource right registration database provided by an embodiment of the present application;

[0043] Figure 2A partial view of a base map provided by one embodiment of the present application;

[0044] Figure 3 A schematic view of remote sensing data of different times in a collection area provided by one embodiment of the present application. DETAILED DESCRIPTION

[0045] In order to further illustrate the technical means and effects taken by the present application to achieve the predetermined purposes, the following describes in detail the specific implementation, structure, features and effects of a data updating method for a natural resource right registration database according to the present application, with reference to the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures or characteristics in one or more embodiments can be combined in any suitable form.

[0046] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the present application belongs.

[0047] The following specifically describes a specific scheme of a data updating method for a natural resource right registration database according to the present application, with reference to the accompanying drawings.

[0048] The natural resource right registration database mainly relies on remote sensing images for updating, that is, the land use status results of natural resource investigation are used as base maps, and the types, boundaries, areas, quantities and qualities of various natural resources in units are checked through internal and external verification, combined with various natural resource census or investigation results. Figure 2 Fig. 1 shows a partial view of a base map provided by one embodiment of the present application.

[0049] In the present embodiment, based on the latest orthographic image map and the land use status map of the third national land survey, the registered collective land ownership, state-owned construction land use right, state-owned agricultural land use right and other land plots, as well as the boundary of urban built-up area, the boundary of urban planning area, the boundary of main function area, the ecological protection red line and the like are plotted on the land use status map and the orthographic image map by using remote sensing and GIS technology, to make a survey work base map. According to the natural resource type data and information provided by various departments, such as water conservancy census data (rivers, reservoirs, lakes), water system map, wetland investigation data, state-owned forest, public forest and forest park, nature reserve, geological park, forestry "one map" data, national conditions census data and land use status database, the GIS technology is applied to extract the natural resource boundary to be registered during the registration of the natural resource area.

[0050] Through the analysis of the changes in remote sensing data, the real-time monitoring of the natural resource status can be realized, and the consistency between the database information and the actual situation is ensured. In order to ensure the appropriateness and reliability of the change update, please refer to Figure 1 Fig. 1 shows a flow chart of a data updating method suitable for a natural resource right registration database according to an embodiment of the present application, which comprises the following steps:

[0051] S1: obtaining the current remote sensing data of the collection area, and the historical remote sensing data and the collection time; obtaining the historical remote sensing data of each update of the collection area from the natural resource right registration database; determining the natural resource area in each remote sensing data.

[0052] The remote sensing data is obtained by collecting a certain area that needs to be analyzed. Since the update of the database requires a certain actual change of the natural resources, the historical collection frequency of the remote sensing data corresponding to the updated remote sensing data in the natural resource right registration database is not equal.

[0053] In the embodiment of the present application, a suitable remote sensing platform is selected according to the monitoring requirements, such as satellite, unmanned aerial vehicle or aerial photography, etc., the required remote sensing data is downloaded through the related remote sensing data providing platform (such as NASA, USGS, ESA, etc.), the data obtained is ensured to meet the required resolution and band requirements, and the geometric correction and radiation correction of the downloaded remote sensing data are performed to eliminate the errors caused by factors such as sensor, climate and terrain.

[0054] The different remote sensing data collected in the region are registered to ensure that they are compared in the same coordinate system for subsequent change analysis. At the same time, the historical remote sensing images of each update of the collection area in the natural resource right registration database are obtained for analyzing the database update requirements.

[0055] Since the remote sensing data contains various natural resources, subsequent change analysis is performed by dividing them into different natural resource areas. In the embodiment of the present application, a deep learning model is used for semantic segmentation of each remote sensing data, and each region after segmentation is taken as a natural resource area. The deep learning model, such as U-Net, FCN, SegNet, etc., can automatically learn features through training, thereby realizing high-precision segmentation, and the geographic position 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 technology known to those skilled in the art, and will not be repeated or limited here. Please refer to Figure 3 Fig. 2 shows a schematic diagram of the remote sensing data of the collection area at different times according to an embodiment of the present application.

[0056] S2: according to the area shape difference of each natural resource area, the ownership change degree of each natural resource area in each acquisition is obtained between each acquisition and the previous acquisition; through the adjacency distribution of each natural resource area in the current remote sensing data and the deviation of the ownership change degree in the historical acquisition, the change highlight degree of each natural resource area is obtained.

[0057] On the basis of the current acquired remote sensing data, due to the influence of climate and social behavior, the boundary of the natural resource area will expand or new natural resource areas will appear in the current natural resource area and the like. These changes of the natural resources are important data support for the update of the right registration database.

[0058] Therefore, the change difference of each natural resource area in the acquisition process is analyzed, and the ownership change degree is obtained to reflect the ownership change of the natural resource area in the acquisition. Preferably, in the embodiment of the application, the method for obtaining the ownership change degree comprises:

[0059] For any natural resource area in each acquisition, the same position natural resource area of the natural resource area in the previous acquisition is determined, so that when analyzing the change of the natural resource area, the corresponding natural resource area in the previous acquisition is ensured to be the same position natural resource area in the previous acquisition. In the embodiment of the application, the center position of the natural resource area is obtained, and the same position is determined through the center position of the area. The natural resource area in the current acquisition is closest to the center position of the natural resource area, and the natural resource area in the previous acquisition corresponding to the natural resource area is taken as the same position natural resource area of the natural resource area.

[0060] Further, the area difference between the natural resource area and the same position natural resource area is taken as the area change degree of the natural resource area, reflecting the possible shrinkage and expansion change of the natural resource area. Further, the difference between the shape index of the natural resource area and the same position natural resource area is taken as the shape change degree of the natural resource area, reflecting the possible boundary influence change of the natural resource area. In the embodiment of the application, the shape index is the ratio of the square of the perimeter to the area, representing the boundary shape of the area.

[0061] Finally, the area change degree and the shape change degree of the natural resource area are combined to obtain the ownership change degree of the natural resource area. In the embodiment of the application, the product of the area change degree and the shape change degree of the natural resource area is taken as the ownership change degree of the natural resource area, which comprehensively reflects the change degree of the area shape and reflects the ownership change of the natural resource area. For the higher change, the higher the demand for update.

[0062] For the current change of remote sensing data, by comparing the change of historical collection, the prominence of change is reflected, and more analysis is needed for more significant changes. Therefore, from the boundary information of the current natural resource area and the deviation of the change of right of use in the relative historical collection, the change prominence of the current natural resource area is obtained.

[0063] Preferably, in the embodiment of the application, the change prominence acquisition method comprises:

[0064] For any natural resource area in the current remote sensing data, the right of use change degree of the natural resource area at each historical collection is obtained, and the change of the natural resource area at the previous historical collection is analyzed. Further, the difference between the right of use change degree of the natural resource area in the current collection and the right of use change degree at each historical collection is calculated, and the mean of all differences is obtained, to obtain the change significance of the natural resource area. The deviation between the current collection and each collection is reflected by the difference, and the change significance is obtained by synthesizing all differences. When the change significance is higher, it means that the change of the current natural resource area is more prominent compared with the change in the historical data.

[0065] Further considering the boundary complexity of the natural resource area, when there are more adjacent different areas in the natural resource area itself, it reflects that the local type is more complex, and the influence caused by single area change is more. Therefore, the number of other natural resource areas with common boundaries with the natural resource area in the current remote sensing data is counted, to obtain the boundary number of the natural resource area, that is, the number of natural resource areas adjacent to the natural resource area as the boundary number.

[0066] Finally, the change prominence of the natural resource area is obtained by combining the boundary number and the change significance of the natural resource area. In the embodiment of the application, the product of the boundary number and the change significance of the natural resource area is taken as the change prominence of the natural resource area. When the right of use change degree of the natural resource area is more prominent relative to the historical collection, and the local distribution type is more complex, it means that the change prominence of the natural resource area is higher, that is, the change prominence is greater.

[0067] S3: Based on the right of use change degree of the current natural resource area, the feature analysis area is selected out; the matching of the right of use change degree of the feature analysis area in the historical remote sensing data between each two updates is analyzed, and the change prominence of each feature analysis area is obtained to obtain the change credible weight of each feature analysis area.

[0068] Considering the inconsistency of natural resource changes, the current information change of some regions may be larger. By screening the region with larger changes, the influence of the region with larger changes on the natural resource right registration database is analyzed, the difference tolerance of database information update is considered, the influence weight of the region with larger changes in the natural resource right registration database is analyzed, and the accuracy of subsequent update evaluation according to the change is improved.

[0069] Preferably, in the embodiment of the application, the characteristic analysis region is screened by the right change degree of the current natural resource region, and the screening method of the characteristic analysis region comprises:

[0070] In the current remote sensing data, the right change degrees of all natural resource regions are arranged in order from small to large to obtain a change sequence, and the change sequence is differentiated. The smallest right change degree of the two right change degrees corresponding to the maximum difference value is taken as the division change degree. The maximum difference value represents that the right change degree can be divided into two parts of smaller and larger. In order to screen the natural resource region with larger part, the smaller value of the two right change degrees participating in the maximum difference calculation is taken as the division change degree.

[0071] At this time, the natural resource region with a right change degree greater than the division change degree is taken as the characteristic analysis region. The characteristic analysis region is a region with higher information change. The influence of the change of this part on the update is adjusted to the proportion of the subsequent update evaluation.

[0072] Through the change matching of the characteristic analysis region between the continuous updates of the database, the consistency of the information change of the characteristic analysis region during the update is reflected. The higher the correlation of the update to this part, the higher the change prominence degree. When the matching is higher and the change prominence degree is higher, the attention to the characteristic change region is greater, and the change proportion during the update is considered to be more.

[0073] Preferably, in the embodiment of the application, the method for obtaining the variable confidence weight comprises:

[0074] First, in the natural resource right registration database, the right change degree difference of each characteristic analysis region between adjacent two updates is calculated as the change difference of each characteristic analysis region between adjacent two updates. When the right change degree of the characteristic analysis region is more consistent between each two updates, it is indicated that the change considered by the characteristic analysis region under two updates is more consistent.

[0075] The change difference of all feature analysis areas between adjacent two updates is combined and negatively correlated to obtain the matching degree between adjacent two updates, and the change difference of all feature analysis areas under two updates is comprehensively considered, and when the comprehensive difference is smaller, the change of the whole feature analysis area under the two updates is more consistent, and the matching degree between the updates is higher. In the embodiment of the present application, the mean value of the change difference of all feature analysis areas between adjacent two updates is negatively correlated to obtain the matching degree between adjacent two updates, and it should be noted that the negative correlation mapping is a technology known to those skilled in the art, which can be in the form of inverse proportion or negative exponential power, and the like, which is not limited and described here.

[0076] Through the adjacent update situation continuously in time sequence, the matching degree between all adjacent two updates in history is calculated as the matching correlation degree by combining the change prominence analysis of single feature analysis area, which reflects the overall matching situation under continuous update. Further, the product of the change prominence of each feature analysis area and the matching correlation degree is taken as the change credible weight of each feature analysis area, and when the change of the feature analysis area under continuous update is more consistent, and the change prominence is higher, it is indicated that the feature analysis area has higher participation and influence on update analysis, and has larger proportion in subsequent update consideration.

[0077] S4: Analyzing the deviation of update registration time and collection time in the update history remote sensing data to obtain the update lag; combining the ownership change degree of natural resource area in the current remote sensing data and the change credible weight of feature analysis area, and the update lag, to obtain the update level of the current remote sensing data; and performing update reminding based on the update level.

[0078] In the process of updating the natural resource right registration database, there may be some data update lag, and there is a certain time difference between the information change of natural resources in the registration database and the actual remote sensing data collection, so for the existing update lag, when the update lag is more serious, the necessity of improving the reminding level is higher.

[0079] In the embodiment of the present application, the deviation of historical update and collection time is analyzed to obtain the update lag, including:

[0080] The difference between the update registration time and the collection time corresponding to the historical remote sensing data at each update is calculated as an update deviation degree, representing the time lag degree of each update. The update level time of each update is negatively correlated with the current time difference and normalized to obtain an update weight. The closer to the current lag situation, the higher the degree of consideration. The smaller the time difference from the current time difference, the more lag situation is considered, so the update weight is larger. It should be noted that normalization is a well-known technical means for those skilled in the art, and the selection of normalization can be linear normalization or standard normalization, and the specific normalization method is not limited here.

[0081] Finally, the update deviation degrees of all updates are weighted and averaged based on the update weight to obtain the update lag, that is, the update deviation degree and the update weight of each update are multiplied to obtain the update lag, and the mean of the weighted product of all updates is calculated to obtain the update lag, reflecting the time lag influence of database registration update.

[0082] Finally, the current natural resource ownership change situation, the feature analysis area change credible weight, and the update lag are considered to comprehensively evaluate the update level. The higher the update level, the greater the update necessity. Preferably, in the embodiment of the present application, the method for obtaining the update level comprises:

[0083] In the current remote sensing data, the ownership change degree of each feature analysis area is weighted and averaged based on the change credible weight to obtain the current change update degree, that is, the change credible weight and the ownership change degree of each feature analysis area are multiplied to obtain the change update degree, and the mean of the weighted product of all feature analysis areas is calculated to obtain the change update degree. The feature analysis area with higher change credibility has a higher analysis proportion of change, and the change influence of the feature analysis area is comprehensively considered.

[0084] Outside the feature analysis area with large changes, other natural resource areas will also change, and the change influence of other natural resource areas is directly analyzed and integrated. The mean of the ownership change degree of all natural resource areas outside the feature analysis area is taken as the average change influence degree, reflecting the change influence of other natural resource areas.

[0085] Finally, the product of the current change update degree, the average change influence degree, and the update lag is normalized as the current update level, considering the change influence and the lag situation. The greater the change influence, the more serious the current lag situation, and the higher the update prompt level.

[0086] Therefore, the updating reminder is performed by updating the level, and in the embodiment of the present application, when the updating level is greater than a preset updating threshold, the updating notification reminder is performed, wherein the preset updating threshold is set to 0.6, and the specific value can be controlled by the implementer according to the specific implementation scene, which is not limited herein. According to the reminder, the specific updating process can be formulated subsequently, including the data auditing, updating implementation, information feedback and other steps, to ensure the normativity and effectiveness of the updating;

[0087] In the embodiment of the present application, the effect of the database updating performed by the updating level reminder is periodically evaluated, including the timeliness, quality and support to the business decision of the updating, and the threshold, the reminding mode and the updating process are continuously optimized according to the evaluation result, so that the whole system is more efficient and reliable.

[0088] To sum up, the present application first obtains the right change degree according to the boundary area difference between the natural resource area in the actually collected remote sensing data and the previous collected data, representing the change degree of the current different types of natural resource area. The change prominence of the natural resource is obtained through the deviation of the right change degree in the historical collection and the adjacency distribution of the natural resource area, and the change degree of the natural resource area is analyzed from the continuous collection. Considering the historical updating of the database, the feature analysis area with more significant change is first selected by the right change degree. Then, the matching condition between the feature analysis areas in the continuous updating remote sensing data in the natural resource right registration database is combined to reflect the change degree of the analysis area in the historical updating, and the change credible weight of each feature analysis area is determined according to the current change prominence, representing the weight of the influence of the region change degree on the updating after the historical updating analysis based on the database. Further considering the lag condition of the remote sensing data in the historical updating of the database, the current updating can be controlled to improve the timeliness of the updating, and finally the updating level is obtained by combining the change credible weight, the updating lag and the right change degree. The present application analyzes the local change between the collected remote sensing data and the database updating remote sensing data, and dynamically adjusts the updating frequency adaptively combined with the historical updating lag of the database, so as to provide more timely and reliable data support for subsequent efficient management.

[0089] It should be noted that the above-mentioned embodiments of the present application are only for description, and do not represent the advantages and disadvantages of the embodiments. The processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multi-task processing and parallel processing are also possible or can be advantageous.

[0090] Each embodiment in the specification is described in a progressive manner, and the same and similar parts between each embodiment can be referred to each other, and each embodiment mainly describes the difference from other embodiments.

Claims

1. A data update method applicable to a natural resource ownership registration database, characterized in that, The method includes: Acquire current remote sensing data of the acquisition area, as well as historical remote sensing data and acquisition time; retrieve historical remote sensing data of the acquisition area for each update from the natural resource ownership registration database; determine the natural resource zone in each remote sensing data set; For any natural resource area collected in each survey, identify the natural resource area in the same location in the previous survey; the area difference between the natural resource area and the natural resource area in the same location is taken as the area change degree of the natural resource area; the difference in the shape index between the natural resource area and the natural resource area in the same location is taken as the shape change degree of the natural resource area; combining the area change degree and the shape change degree of the natural resource area, the ownership change degree of the natural resource area is obtained. For any natural resource area in the current remote sensing data, obtain the degree of ownership change of the natural resource area in each historical acquisition; calculate the difference between the degree of ownership change of the natural resource area in the current acquisition and the degree of ownership change in each historical acquisition, and then calculate the mean of all differences to obtain the significance of the change of the natural resource area; count the number of other natural resource areas in the current remote sensing data that share a boundary with the natural resource area to obtain the number of boundaries of the natural resource area; combine the number of boundaries and the significance of the change of the natural resource area to obtain the prominence of the change of the natural resource area. In the current remote sensing data, all natural resource zones are arranged in ascending order of ownership change degree to obtain a change sequence. The change sequence is then differentially analyzed, and the smallest ownership change degree among the two corresponding to the largest difference is used as the division change degree. Natural resource zones with ownership change degrees greater than the division change degree are designated as feature analysis zones. In the natural resource ownership registration database, the ownership change degree difference of each feature analysis zone is calculated between two adjacent updates, serving as the change difference between each feature analysis zone between two adjacent updates. The change differences of all feature analysis zones between two adjacent updates are combined and negatively correlated to obtain the matching degree between two adjacent updates. The mean of the matching degrees between all historical adjacent updates is calculated as the matching correlation degree. The product of the change prominence of each feature analysis zone and the matching correlation degree is used as the change confidence weight for each feature analysis zone. The deviation between the update registration time and the acquisition time when updating historical remote sensing data is analyzed to obtain the update lag. Combining the ownership change degree of natural resource areas and the change confidence weight of the feature analysis areas in the current remote sensing data, along with the update lag, the update level of the current remote sensing data is obtained. This includes: in the current remote sensing data, the ownership change degree of each feature analysis area is weighted and averaged based on the change confidence weight to obtain the current change update degree; the average ownership change degree of all natural resource areas outside the feature analysis areas is used as the average change impact degree; and the product of the current change update degree, the average change impact degree, and the update lag is normalized to obtain the current update level. Update reminders will be sent based on the update level.

2. The data update method for a natural resource ownership registration database according to claim 1, characterized in that, Methods for obtaining update lag include: The difference between the update registration time and the acquisition time of the historical remote sensing data at each update is calculated as the update bias; the update level time of each update is negatively correlated with the current time difference and normalized to obtain the update weight. The update lag is obtained by weighting and averaging the update biases of all updates based on the update weights.

3. The data update method for a natural resource ownership registration database according to claim 1, characterized in that, Update alerts are provided based on update priority levels, including: When the update level exceeds the preset update threshold, an update notification will be sent.

4. The data update method for a natural resource ownership registration database according to claim 1, characterized in that, Methods for obtaining natural resource zones in the same location include: Obtain the center location of the natural resource area; when the natural resource area in the current collection is closest to the center location of this natural resource area, the natural resource area corresponding to the previous collection is taken as the natural resource area at the same location of this natural resource area.

5. A data update method for a natural resource ownership registration database according to claim 1, characterized in that, Identify the natural resource zones in each remote sensing dataset, including: Semantic segmentation is performed on each remote sensing data based on a deep learning model, and each segmented region is designated as a natural resource area.

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