A Method and System for Natural Resource Asset Valuation Based on Multi-Source Data

Abstract

This invention discloses a method and system for natural resource asset assessment based on multi-source data, belonging to the field of resource data processing technology. The method includes: performing spatial coordinate unification and resource semantic mapping processing on multi-source natural resource data to form resource semantic mapping data; extracting spatially continuous resource area data with the same resource semantic labels from the resource semantic mapping data to form a resource object set; identifying spatial adjacency relationships, attribute matching relationships, and state evolution relationships among resource objects in the resource object set; deconstructing the association path and determining the association strength of the hierarchical association structure data to output resource asset assessment data; and matching the resource asset assessment data with pre-stored resource management rules to generate resource asset decision data. This invention, through the synergy of constructing resource association structure data and resource association assessment sequences, achieves orderly support for resource management decisions from natural resource asset assessment results.

Description

Technical Field

[0001] This invention relates to the field of resource data processing technology, and in particular to a method and system for natural resource asset assessment based on multi-source data. Background Technology

[0002] In recent years, with the continuous development of remote sensing monitoring methods, geographic information processing capabilities, and multi-source data acquisition methods, natural resource asset assessment has gradually evolved from single-data statistics to comprehensive multi-source data analysis. In resource management and value assessment scenarios, remote sensing monitoring data, geospatial data, environmental monitoring data, and resource utilization data are typically collected to analyze resource distribution, trends, and utilization. Spatial data processing and data organization methods are used to identify resource objects and express their value. Simultaneously, the application of multi-source data in spatial representation and information fusion is continuously deepening, enabling resource assessment to gradually form a processing system centered on data collection, spatial organization, and result output. This system is widely used in ecological assessment, resource accounting, and management decision-making.

[0003] However, existing methods have shortcomings in the unified representation of multi-source data and the utilization of resource relationships. Existing methods often focus on independent processing or simple overlay of multi-source data, lacking methods for semantically unifying and spatially continuous organizing resource data. Furthermore, they do not fully utilize the spatial adjacency relationships, attribute matching relationships, and state evolution relationships between resource objects, making it difficult to form resource relationship expressions with hierarchical structure and path association characteristics. This results in an incomplete reflection of the inter-resource relationships during resource asset assessment, thus affecting the application effectiveness of assessment results in resource management decision-making. Summary of the Invention

[0004] In view of the aforementioned existing problems, the present invention is proposed.

[0005] Therefore, this invention provides a natural resource asset assessment method based on multi-source data to address the shortcomings in the unified expression of multi-source data and the utilization of resource correlation relationships.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: In a first aspect, the present invention provides a method for natural resource asset assessment based on multi-source data, comprising: collecting multi-source natural resource data; performing spatial coordinate unification and resource semantic mapping processing on the multi-source natural resource data to form resource semantic mapping data; extracting spatially continuous resource region data with the same resource semantic labels from the resource semantic mapping data; merging and marking the resource region data to output a resource region set; and obtaining resource attribute information and resource status information from the resource semantic mapping data and appending them to the resource region set to form a resource object set; identifying the spatial adjacency relationship, attribute matching relationship, and status evolution relationship among the resource objects in the resource object set, and... Each resource object undergoes directional relationship mapping, outputting resource association structure data. A graph hierarchical layout algorithm is used to organize the resource association structure data hierarchically, generating hierarchical association structure data. The hierarchical association structure data undergoes association path deconstruction and association strength determination, outputting a resource association evaluation sequence. This sequence is then used in conjunction with resource attribute and status information from the resource object set for collaborative value assessment and association arrangement, outputting resource asset evaluation data. Finally, the resource asset evaluation data is matched with pre-stored resource management rules to identify the resource management methods, resource utilization restrictions, and resource protection measures corresponding to each resource object, and integrated to generate resource asset decision data.

[0007] As a preferred embodiment of the natural resource asset assessment method based on multi-source data described in this invention, the specific steps for forming resource semantic mapping data are as follows: Spatial coordinate analysis is performed on multi-source natural resource data to determine the spatial location data corresponding to each resource data item in the multi-source natural resource data. Spatial location data is transformed into spatial coordinates according to a unified spatial coordinate system to form coordinate unified resource data; Perform resource semantic annotation on the coordinate unified resource data, output resource semantic tags, and classify the coordinate unified resource data into semantic categories according to the resource semantic tags to form resource semantic mapping data; The multi-source natural resource data includes remote sensing monitoring data, geospatial data, environmental monitoring data, and resource utilization data.

[0008] As a preferred embodiment of the natural resource asset assessment method based on multi-source data described in this invention, the specific steps for outputting the resource region set are as follows: Extract spatial distribution information corresponding to each resource semantic tag from the resource semantic mapping data, perform continuous identification on the spatial distribution information, and output continuously distributed resource data; Aggregate spatial regions with the same resource semantic labels in continuously distributed resource data and output resource region data; Write region identifier information for each resource region in the resource region data, organize each resource region centrally according to the region identifier information, and output the resource region set.

[0009] As a preferred embodiment of the natural resource asset assessment method based on multi-source data described in this invention, the resource object set refers to the resource data items corresponding to the spatial range of each resource region, which are selected from the resource semantic mapping data, the resource attribute information and resource status information of the resource data items are read, and the resource attribute information and resource status information are written into the record of the corresponding resource region.

[0010] As a preferred embodiment of the natural resource asset assessment method based on multi-source data described in this invention, the specific steps for forming resource-related structural data are as follows: Extract the spatial location data, resource attribute information, and resource status information corresponding to each resource object from the resource object set, and combine them into object relationship combination data; Relationship identification is performed on the combined object relationship data to identify spatial adjacency relationships, attribute matching relationships, and state evolution relationships among various resource objects; The relationship direction between resource objects is determined by state evolution relationship, the relationship range between resource objects is limited by spatial adjacency relationship, attribute matching objects are filtered by attribute matching relationship, and the directional relationship connection data is integrated and output. Connect the resource objects sequentially according to the directional relationship connection data, and output the resource association structure data.

[0011] As a preferred embodiment of the natural resource asset assessment method based on multi-source data described in this invention, the hierarchical organization refers to using the relationship direction in the data connected by directional relationships as the hierarchical basis, and arranging resource objects without prior connections in the upper layer and resource objects with prior connections in the lower layer through a graph hierarchical layout algorithm, thereby forming a hierarchical association structure data with a clear upper and lower hierarchical relationship.

[0012] As a preferred embodiment of the natural resource asset assessment method based on multi-source data described in this invention, the specific steps for outputting the resource-related assessment sequence are as follows: Expand the paths of each resource object in the hierarchical relational structure data and output a set of relational paths; The number and position of connections in each associated path in the associated path set are statistically analyzed to determine the association strength of each associated path. The associated path set is then ordered by the associated paths and association strength to output a resource association evaluation sequence.

[0013] As a preferred embodiment of the natural resource asset assessment method based on multi-source data described in this invention, the collaborative value assessment and association arrangement refers to mapping the resource attribute information and resource status information in the resource object set to each association path according to the association path order in the resource association assessment sequence, performing value statistics on resource objects within the same association path, and arranging the value statistics results in order according to the association path order to form resource asset assessment data.

[0014] As a preferred embodiment of the natural resource asset assessment method based on multi-source data described in this invention, the specific steps for generating resource asset decision data are as follows: The resource asset assessment data is matched with the pre-stored resource management rules to determine the resource management rules for each resource object in the resource asset assessment data; The resource management rules identify the resource management methods, resource utilization restrictions, and resource protection measures for resource objects, and then process these into decision-making data to generate resource asset decision-making data.

[0015] Secondly, this invention provides a natural resource asset assessment system based on multi-source data, including: The semantic processing module is used to collect multi-source natural resource data, perform spatial coordinate unification and resource semantic mapping processing on the multi-source natural resource data, and form resource semantic mapping data. The object construction module is used to extract spatially continuous resource region data with the same resource semantic labels from the resource semantic mapping data, merge and mark the resource region data, output a resource region set, and obtain resource attribute information and resource status information from the resource semantic mapping data and attach them to the resource region set to form a resource object set. The resource association module is used to identify the spatial adjacency relationship, attribute matching relationship and state evolution relationship between resource objects in the resource object set, perform directional relationship mapping on each resource object, output resource association structure data, and organize the resource association structure data into association hierarchy through graph hierarchical layout algorithm to generate hierarchical association structure data. The asset valuation module is used to deconstruct the association path and determine the strength of the association effect in the hierarchical association structure data, output the resource association valuation sequence, and perform collaborative value assessment and association arrangement with the resource attribute information and resource status information in the resource object set, outputting resource asset valuation data. The resource decision-making module is used to match resource asset assessment data with pre-stored resource management rules, identify the resource management methods, resource utilization restrictions and resource protection measures corresponding to resource objects, and integrate them to generate resource asset decision-making data.

[0016] The beneficial effects of this invention are as follows: By constructing resource association structure data and coordinating with resource association assessment sequences, it achieves orderly support for resource management decisions from natural resource asset assessment results. By identifying the spatial adjacency relationships, attribute matching relationships, and state evolution relationships among resource objects in a resource object set and mapping these relationships to form resource association structure data, a connection structure with directional and hierarchical relationships is formed between resource objects. This provides a stable foundation for subsequent deconstruction of association paths. The resource association assessment sequence is then collaboratively value-assessed and associated with the resource attribute information and resource state information in the resource object set. This enables resource asset assessment data to reflect the process of association between resources, thereby improving the accuracy of resource management rule matching and decision generation. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. 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.

[0018] Figure 1 This is a flowchart of a natural resource asset assessment method based on multi-source data.

[0019] Figure 2 This is a schematic diagram of a natural resource asset assessment system based on multi-source data.

[0020] Figure 3 This is a flowchart for outputting a set of resource regions.

[0021] Figure 4 This is a flowchart for outputting the resource association evaluation sequence. Detailed Implementation

[0022] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0023] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0024] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0025] Reference Figures 1-4 This is one embodiment of the present invention, which provides a method for natural resource asset assessment based on multi-source data, including the following steps: S1. Collect multi-source natural resource data, perform spatial coordinate unification and resource semantic mapping processing on the multi-source natural resource data, and form resource semantic mapping data.

[0026] Collect multi-source natural resource data, perform spatial coordinate analysis on the multi-source natural resource data, and determine the spatial location data corresponding to each resource data item in the multi-source natural resource data.

[0027] Specifically, the collection of multi-source natural resource data involves acquiring remote sensing monitoring data through satellite remote sensing imagery, geospatial data through geographic surveying and mapping results, environmental monitoring data through fixed monitoring stations, mobile monitoring points, and online monitoring terminals, and resource utilization data through natural resource survey ledgers, utilization status records, and management registration materials. The image number, pixel row and column number, and geographic reference parameters from the remote sensing monitoring data, the point coordinates, line node coordinates, and area boundary coordinates from the geospatial data, the monitoring point number and monitoring location record from the environmental monitoring data, and the parcel number, region number, and location name from the resource utilization data are written into the corresponding resource data items.

[0028] When performing spatial coordinate analysis on multi-source natural resource data, image coordinates, pixel resolution, and coordinate reference information are read from remote sensing monitoring data. The coordinate position of each pixel is determined according to the pixel row and column number. Point coordinates, line node coordinates, and area boundary coordinates are read from geospatial data and the coordinate values ​​are extracted. The latitude and longitude coordinates in the monitoring point location table are retrieved from the environmental monitoring data according to the monitoring point number. The corresponding coordinates in cadastral coordinates, administrative division coordinates, or place name coordinates are retrieved from the resource utilization data according to the parcel number, region number, or location name. The coordinate values ​​are written into the location field corresponding to each resource data item. The spatial location data corresponding to each resource data item in the multi-source natural resource data is determined by the correspondence between the coordinate values, coordinate reference information, and location fields.

[0029] Spatial location data is transformed into spatial coordinates according to a unified spatial coordinate system to form unified coordinate resource data.

[0030] Specifically, based on spatial location data, the coordinate reference information in each resource data item is read to determine whether the coordinate value belongs to latitude and longitude coordinates or planar coordinates. For latitude and longitude coordinates, the latitude and longitude values ​​in degrees, minutes, and seconds are converted to decimal degrees. Based on the projection parameters corresponding to the unified spatial coordinate system, the difference between the longitude value and the central meridian is converted into a horizontal distance value, and the latitude value is converted into a vertical distance value to obtain the planar coordinate values. For existing planar coordinates, the coordinate values ​​are adjusted to a unified length unit according to the unit conversion relationship of the unified spatial coordinate system, and the coordinate values ​​are corrected according to the coordinate reference of the unified spatial coordinate system. The converted coordinate values ​​are written into the corresponding location field of each resource data item according to the coordinate order of the unified spatial coordinate system, thereby forming unified coordinate resource data.

[0031] The formula for converting decimal values ​​is as follows: ; in, The decimal values ​​converted from latitude and longitude in degrees, minutes, and seconds. The degree value in latitude and longitude values ​​expressed in degrees, minutes, and seconds. The minute value in latitude and longitude values ​​expressed in degrees, minutes, and seconds. The second value in latitude and longitude values ​​in degrees, minutes, and seconds.

[0032] The formula for converting horizontal distance to vertical distance is: ; ; in, This represents the horizontal distance value. Indicates the vertical distance value. This represents the projection scale parameter corresponding to a unified spatial coordinate system. This represents the reference radius value corresponding to a unified spatial coordinate system. This represents the difference between the longitude value and the value of the central meridian. Representing the dimensional values ​​in decimal system form. This represents the cosine value corresponding to the latitude value.

[0033] Furthermore, a unified spatial coordinate system is established through a unified coordinate datum, unified coordinate units, and unified projection parameters. During spatial coordinate transformation, the projection zone to which the latitude and longitude coordinates belong is determined based on the coordinate reference information in the spatial location data. The longitude difference is then determined according to the central meridian corresponding to the projection zone, converting the latitude and longitude values ​​into planar coordinate values. During the conversion process, a consistent expression method is used for the latitude and longitude values, and the lateral and longitudinal distance values ​​are scaled according to a unified scaling factor. Simultaneously, a unified offset is applied to the planar coordinate values, ensuring that spatial location data from different sources form a consistent spatial expression under the same coordinate datum. For spatial location data from different regions, the converted planar coordinate values ​​are continuously corrected according to the unified spatial coordinate system, ensuring that the spatial location of each resource data item remains consistent across the entire scope.

[0034] Resource semantic annotation is performed on the coordinate unified resource data, output resource semantic tags, and the coordinate unified resource data is classified into semantic categories according to the resource semantic tags to form resource semantic mapping data.

[0035] Specifically, resource attribute fields, resource status fields, spatial location fields, and data source fields are read from each resource data item in the unified coordinate resource data. The land cover name, land type name, monitoring indicator name, and utilization name in the resource attribute fields are compared item by item with the resource name terms, attribute range terms, status description terms, and utilization description terms in the preset resource classification data. Terms that match are written into the corresponding resource semantic tags. For resource data items with multiple candidate terms, the resource semantic tag is selected based on the combined matching result of the resource attribute field and the resource status field. That is, when multiple candidate terms exist, the resource attribute field is compared item by item with the attribute description of each candidate term, and the resource status field is compared item by item with the status description of each candidate term. The candidate term that simultaneously satisfies the correspondence between the attribute description and the status description is used as the resource semantic tag. When multiple candidate terms meet the conditions, the candidate term with the most matching terms is selected as the resource semantic tag, and the selected resource semantic tag is written into the semantic tag position of the corresponding resource data item.

[0036] Resource data items with the same semantic tags are grouped into the same semantic category set. Resource data items in different semantic category sets are then arranged according to the spatial location field and the data source field. Specifically, resource data items in different semantic category sets are first sorted by the spatial location field, with the coordinate values ​​in the spatial location field determining the arrangement order from smallest to largest. If the spatial location fields are the same, they are then sorted by the data source field, so that resource data items under the same spatial location are arranged sequentially according to the data source field. The correspondence between resource data items and resource semantic tags in the semantic category set is checked item by item, and resource data items that match the check and their corresponding resource semantic tags are retained to form resource semantic mapping data. Resource semantic mapping data refers to a data set with semantic identifiers formed by establishing a correspondence between resource attribute information and resource status information in resource data items and corresponding semantic tags after the spatial coordinates are unified.

[0037] Furthermore, the pre-defined resource classification data is extracted from natural resource survey data, land category labeling data, environmental monitoring records, and resource utilization registration data, which contain clearly recorded resource names, attribute descriptions, status descriptions, and usage descriptions. For words with the same meaning but different expressions, the names are unified. After the unified names are formed, a fixed correspondence is established between the resource names and attribute descriptions, status descriptions, and usage descriptions.

[0038] S2. Extract spatially continuous resource region data with the same resource semantic label from the resource semantic mapping data, merge and mark the resource region data, output the resource region set, and obtain resource attribute information and resource status information from the resource semantic mapping data and attach them to the resource region set to form a resource object set.

[0039] Extract the spatial distribution information corresponding to each resource semantic label from the resource semantic mapping data, perform continuous identification on the spatial distribution information, and output continuously distributed resource data.

[0040] Specifically, using resource semantic tags in the resource semantic mapping data as the classification basis, resource data items with the same resource semantic tags are grouped into the same group. Within each group, a coordinate sequence is established based on the coordinate values ​​in the spatial location field. The coordinate sequences are sorted according to the numerical order, and the distance between adjacent coordinate values ​​in the sorted coordinate sequences is statistically analyzed item by item. Adjacent coordinate values ​​with continuously changing distances and no gaps are connected. Coordinate sequence segments with continuously extending connections are divided into the same spatial distribution fragments. All spatial distribution fragments are aggregated according to resource semantic tags to form spatial distribution information. Continuity identification of spatial distribution information involves checking the coordinate sequences in each spatial distribution fragment item by item based on the spatial distribution information. Locations where there are gaps between adjacent coordinate values ​​are marked as breakpoints, and the coordinate sequences are segmented according to the breakpoint locations. Coordinate sequence segments without breakpoints and with consistent resource semantic tags are marked as continuous distribution data. All continuous distribution data are aggregated to output continuous distribution resource data.

[0041] Furthermore, continuity identification includes: based on the coordinate values ​​in the spatial location field, comparing the spacing between adjacent coordinates point by point in coordinate order; sorting all adjacent coordinate spacings under the same semantic category according to the numerical size, and selecting the spacing value that appears most frequently after sorting as the baseline spacing; when the spacing between adjacent coordinates is equal to the baseline spacing, marking the corresponding coordinate pairs as adjacent connections; when the spacing between adjacent coordinates is greater than the baseline spacing, dividing the coordinate sequence into different continuous segments at the current position; when establishing adjacent connections, horizontally, vertically, and diagonally adjacent coordinates are uniformly included in the connection judgment; for areas within a continuous segment that are not covered by coordinates, a gap mark is retained at the corresponding position so that the boundary of the continuous segment can reflect the true spatial distribution structure.

[0042] Aggregate spatial regions with the same resource semantic labels in continuously distributed resource data and output resource region data.

[0043] Specifically, based on the continuous distribution data in the continuous distribution resource data, the continuous distribution data is classified and organized according to resource semantic tags. Continuous distribution data with consistent resource semantic tags are aggregated to form corresponding category sets. Within the corresponding category sets, adjacency relationships are established based on the coordinate values ​​in the spatial location field. Adjacent continuous distribution data that can form a spatially connected structure are combined. The outer boundary coordinates of the combined continuous distribution data are extracted, and the boundary coordinates are closed and connected according to the spatial location order to form a spatially closed structure. Continuous distribution data within the same spatially closed structure are merged as a whole, and the merged spatially closed structure is organized according to the resource semantic tags to output resource area data.

[0044] Furthermore, extracting the outer boundary coordinates from the combined continuous distribution data involves determining a coordinate set based on the coordinate values ​​in the spatial location field of the continuous distribution data, sorting the coordinate set according to the horizontal and vertical coordinate values, selecting the coordinate values ​​located on the outermost side of the coordinate set as the boundary coordinates, and arranging the boundary coordinates sequentially according to the adjacent position relationship between the coordinates. The arranged boundary coordinates are then connected end to end according to the spatial position order, so that the first boundary coordinate and the last boundary coordinate form a closed relationship, thereby forming a spatially closed structure.

[0045] Region aggregation includes: based on continuous segments in continuously distributed resource data, grouping coordinate sets with adjacent connections into the same connected region; extracting the outer coordinates of the coordinate sets in each connected region, arranging the outer coordinates sequentially according to spatial location, and connecting the first and last outer coordinates to form a closed boundary; during the boundary formation process, replacing positions with abrupt changes in direction in the boundary coordinates with the continuous connection order of adjacent coordinates to maintain continuous boundary variation; when there are multiple unconnected coordinate sets, forming independent closed regions for each coordinate set; when a coordinate set is located inside another closed region, marking the current coordinate set as an internal region and retaining it separately; for coordinate sets from different sources or with different distribution densities, first sorting the coordinate values ​​uniformly according to the spatial location field, and then grouping them into the corresponding connected regions according to adjacent connection relationships to avoid incorrect connections due to differences in coordinate distribution.

[0046] Write region identifier information for each resource region in the resource region data, organize each resource region centrally according to the region identifier information, and output the resource region set.

[0047] Specifically, each resource region in the resource region data is taken as a region unit. Based on the coordinate boundary information in the spatial location field and the resource semantic tag, the corresponding region number is written into the identifier field of the corresponding resource region. A correspondence is established between the region number and the spatial location field and resource semantic tag of the resource region. The resource regions are centrally organized according to the region identifier information. The resource regions in the resource region data are numbered and sorted according to the region number. That is, each resource region is organized according to a fixed field order. The fixed field order includes the region number, resource semantic tag, spatial location field, boundary coordinate sequence and internal resource data item set. During the arrangement process, the resource regions are sorted in ascending order of region number. The sorted resource regions are written into the same data structure in sequence. Each resource region occupies an independent record position in the data structure. The record positions are arranged continuously according to the region number order to form a resource region set with consistent structure and clear order.

[0048] Resource attribute information and resource status information are obtained from resource semantic mapping data and appended to the resource region set to form a resource object set.

[0049] Specifically, based on each resource region in the resource region set, the spatial range corresponding to the resource region is determined using the boundary coordinate sequence in the spatial location field. In the resource semantic mapping data, resource data items falling within the corresponding spatial range are filtered according to the spatial inclusion relationship between the coordinate values ​​in the spatial location field and the boundary coordinate sequence. The resource attribute fields and resource status fields of the filtered resource data items are extracted and written into the associated position of the resource region according to the position correspondence of the resource data items in the resource data item set within the resource region. This establishes a correspondence between the resource attribute fields and resource status fields and the region number, resource semantic label, spatial location field, and boundary coordinate sequence. The resource attribute fields and resource status fields in the same resource region are integrated to output a resource object set. The resource object set refers to the data set formed by organizing the resource attribute information, resource status information, and spatial location within the same spatial range, with each resource object corresponding to one resource region.

[0050] S3. Identify the spatial adjacency relationships, attribute matching relationships, and state evolution relationships among resource objects in the resource object set, perform directional relationship mapping on each resource object, output resource association structure data, and organize the resource association structure data into association hierarchy through a graph hierarchical layout algorithm to generate hierarchical association structure data.

[0051] Extract the spatial location data, resource attribute information, and resource status information corresponding to each resource object from the resource object set, and combine them into object relationship combination data.

[0052] Specifically, taking each resource object in the resource object set as the record source, the region number corresponding to the resource object is located and the corresponding record position is entered. The coordinate boundary sequence in the spatial location field is obtained from the corresponding record position. The coordinate values ​​in the coordinate boundary sequence are organized into a location description sequence (that is, the coordinate values ​​in the coordinate boundary sequence are sorted according to the combination relationship between the horizontal coordinate values ​​and the vertical coordinate values, the coordinates with the same horizontal coordinate values ​​are arranged in ascending order of the vertical coordinate values, the coordinates with different horizontal coordinate values ​​are arranged in ascending order of the horizontal coordinate values, and the sorted coordinate values ​​are written into the location description sequence in sequence so that the coordinate values ​​in the location description sequence have a continuous order relationship).

[0053] Retrieve the contents of each attribute field from the resource attribute information of the same resource object, and form an attribute description sequence according to the attribute field name and attribute field value. Retrieve the contents of each status field from the resource status information of the same resource object and arrange them in chronological order to form a status description sequence. Concatenate and arrange the location description sequence, attribute description sequence and status description sequence, and write the concatenated result as a complete relation record into the record position of the corresponding resource object. Collect and arrange all relation records according to the order of the resource objects, and output the object relation combination data.

[0054] Relationship identification is performed on the combined object relationship data, identifying spatial adjacency relationships, attribute matching relationships, and state evolution relationships among various resource objects.

[0055] Specifically, any two relationship records are selected as comparison objects in the object relationship combination data. The coordinate boundary sequences in the spatial location data are extracted from the two relationship records. The two coordinate boundary sequences are compared point by point. When the coordinate values ​​in the two coordinate boundary sequences have overlapping boundaries or adjacent boundary connections, the corresponding resource objects are marked as spatial adjacency relationships. For example, if there are the same coordinate points or consecutive adjacent coordinate segments in the boundary coordinate sequence of one resource object as in the boundary coordinate sequence of another resource object, a spatial adjacency relationship is formed.

[0056] Extract the attribute description sequence from the resource attribute information in the same pair of relation records. Compare the attribute field names and attribute field values ​​in the two attribute description sequences item by item. When the attribute field names are the same and the attribute field values ​​are the same or belong to the same attribute category, the corresponding resource objects are marked as attribute matching relationships. For example, if two resource objects have the same resource type and the same use, an attribute matching relationship is formed.

[0057] Extract the state description sequence from the resource state information of the same pair of relation records. That is, under the same semantic category, select resource data with adjacent time order as the comparison range, and determine the overlap relationship of resource objects based on the boundary range in the spatial location field. Resource objects with spatial overlap are selected as candidate corresponding objects. Among the candidate corresponding objects, the resource type field in the resource attribute information is compared, and resource objects with the same resource type are identified as cross-time correspondences of the same resource object. When one resource object corresponds to multiple objects, the corresponding objects are established as separate relationships. When multiple resource objects correspond to the same object, the corresponding objects are established as merge relationships.

[0058] Two state description sequences are arranged in chronological order. The contents of the previous chronological state description sequence are compared with those of the next chronological state description sequence. When there is a change relationship between the state content in the state field (a change relationship means that in the two chronologically arranged state description sequences, the value of the same state field is inconsistent between the previous and next time positions, and the value at the next time position shows a change in state type or state degree relative to the previous time position, then it is determined to be a change relationship), the corresponding resource object is marked as a state evolution relationship. The identified spatial adjacency relationship, attribute matching relationship, and state evolution relationship are written into the object relationship combination data to complete the identification of the association relationship.

[0059] The relationship direction between resource objects is determined by state evolution, the relationship scope between resource objects is limited by spatial adjacency, attribute matching objects are filtered by attribute matching, and the directional relationship connection data is integrated and output.

[0060] Specifically, the resource objects in the state description sequence with the previous time sequence are mapped to the relationship start position, and the resource objects in the state description sequence with the next time sequence are mapped to the relationship end position, thus determining the relationship direction between resource objects. The marked spatial adjacency relationships are read from the object relationship combination data. Resource object combinations with spatial adjacency relationships are retained in the same candidate set, while resource object combinations without spatial adjacency relationships are removed from the candidate set, thus limiting the relationship range between resource objects. The marked attribute matching relationships are read from the limited candidate set. Resource object combinations that simultaneously satisfy both spatial adjacency and attribute matching relationships are filtered and retained, while resource object combinations that do not satisfy attribute matching relationships are removed from the candidate set. The filtered resource objects are then paired and connected according to the relationship start position and relationship end position. All pairing and connection results are aggregated and arranged according to a unified record structure, outputting the directional relationship connection data.

[0061] Connect the resource objects sequentially according to the directional relationship connection data, and output the resource association structure data.

[0062] Specifically, each connection record in the directional relationship connection data is read, and the resource objects corresponding to the start and end positions of the relationship in the connection record are located accordingly. The resource object at the start position is taken as the predecessor node, and the resource object at the end position is taken as the successor node. A sequential connection relationship between predecessor and successor nodes is established in the same connection record. Predecessor and successor nodes with the same resource object in multiple connection records are concatenated according to the connection order. Predecessor and successor nodes of the same resource object in different connection records are continuously spliced ​​together, so that multiple resource objects form a continuous connection structure according to the relationship direction. All connection relationships are organized according to the order of resource objects, and the resource association structure data is output. Directional relationship mapping refers to determining the connection direction based on the state evolution relationship between resource objects and writing the directional connection relationship between the corresponding resource objects, so that the resource objects form a directed connection structure.

[0063] Connecting preceding and subsequent nodes with the same resource object in multiple connection records according to the connection order involves sorting the preceding and subsequent nodes with the same resource object in multiple connection records according to the correspondence between the start and end positions of the relationship. The resource object corresponding to the start position of the relationship is used as the sorting starting point, and the resource object that has a relationship ending with the sorting starting point is used as the next connection node. The corresponding resource object is then searched and connected sequentially according to the relationship ending position, so that all resource objects form a continuous arrangement relationship according to the transmission order from the start position to the end position of the relationship.

[0064] The resource association structure data is organized hierarchically by using a graph-based hierarchical layout algorithm to generate hierarchical association structure data.

[0065] Specifically, a graph hierarchical layout algorithm is executed based on the resource association structure data. The graph hierarchical layout algorithm is used to arrange resource objects in layers according to the direction of the connection relationship and form a directed hierarchical structure. The resource association structure data reads the connection data of each resource object and the directional relationship between resource objects. Resource objects without a relationship start position are identified as resource objects without a preceding connection and are used as the starting node of the graph hierarchical layout algorithm. Resource objects with a relationship end position are identified as resource objects with a preceding connection relationship.

[0066] The process unfolds hierarchically according to the relationship direction of the directional connection data. Resource objects without prior connections are arranged into the first level. Resource objects connected to the first level are then sequentially assigned to the next level according to the termination position of the relationship. Resource objects in each level are arranged horizontally according to resource semantic tags and spatial location fields, so that natural resource objects in the same level form a corresponding relationship in spatial distribution. Resource objects in different levels are connected vertically according to the directional connection data to form a hierarchical association structure data with a clear hierarchical relationship and natural resource association path structure. The hierarchical association structure data is generated by organizing the association hierarchy and generating the hierarchical association structure data. The hierarchical association structure data is based on the directional connection, which arranges resource objects in layers according to the connection direction and records the data structure formed by the resource objects and their connection relationships in each level.

[0067] By combining and identifying relationships among sets of resource objects, spatial adjacency relationships, attribute matching relationships, and state evolution relationships among natural resource objects are constructed, realizing a structured expression of natural resource relationships. Furthermore, hierarchical organization is carried out through directional relationship connections and graph hierarchical layout algorithms, enabling natural resource objects to form a clear hierarchical structure and association path according to the relationship direction, thereby providing a data foundation with clear relationships and hierarchical structure for resource asset assessment.

[0068] Furthermore, the graph hierarchical layout includes: constructing a relational structure using resource objects in the resource association structure data as nodes and directed relation connection data as directed connections; for node sequences with closed connections, checking the connection order sequentially according to the relation direction; when a connection path back to an existing node is detected, retaining the connection with the earlier time sequence in the current path and disconnecting the connection pointing to the existing node, thereby eliminating the closed structure; after the closed structure is eliminated, resource objects without prior connections are determined as initial level nodes, and subsequent level nodes are determined level by level according to the relation direction, so that each resource object determines its level position based on the number of its prior connections; when the same resource object has multiple prior connections, the current resource object is assigned to the lowest level position in the level where all prior nodes are located; when a resource object has no connections, it is divided into an independent level; after each level is determined, resource objects in the same level are horizontally sorted according to the spatial location field, so that resource objects with adjacent spatial locations remain adjacent in the same level, forming a hierarchical association structure data with clear hierarchical relationships and no closed loops in the connection relationships.

[0069] S4. Deconstruct the association path and determine the association strength of the hierarchical association structure data, output the resource association assessment sequence, and perform collaborative value assessment and association arrangement with the resource attribute information and resource status information in the resource object set to output resource asset assessment data.

[0070] Expand the paths of each resource object in the hierarchical relational data and output a set of relational paths.

[0071] Specifically, based on the hierarchical order in the hierarchical association structure data, the top-level resource object is taken as the path starting node. Starting from the path starting node, lower-level resource objects connected to the path starting node in the directional relationship connection data are read. Sequential connections are established between the path starting node and the corresponding lower-level resource objects to form an initial path sequence. At the end of the initial path sequence, the next-level resource objects connected to the current end resource object are read and appended to the end of the path sequence, so that the path sequence extends downwards level by level according to the hierarchical order. During the path sequence extension process, when the current end resource object has no lower-level connection, the current path sequence is recorded as a complete path. The path extension process is repeated for the remaining top-level resource objects in the hierarchical association structure data, and the path sequences formed by different path starting nodes are recorded one by one. All the recorded path sequences are collected and arranged to output a set of associated paths.

[0072] The number and position of connections in each associated path in the associated path set are statistically analyzed to determine the association strength of each associated path. The associated path set is then ordered by the associated paths and association strength to output a resource association evaluation sequence.

[0073] Specifically, each associated path is extracted from the associated path set. The connection relationship between adjacent resource objects is identified according to the arrangement order of resource objects in the associated path. The number of connection relationships between adjacent resource objects in each associated path is counted item by item. The hierarchical position of each connection relationship in each associated path is marked item by item according to the hierarchical position in the hierarchical association structure data to form the connection position distribution result.

[0074] After the distribution of the number of relational connections and their locations is determined, the number of relational connections for each association path and the corresponding hierarchical range covered by the connection location are recorded. The number of relational connections and the hierarchical range covered by the connection location are used as components of the association strength. The components of each association path are written into the identifier field of the association path to complete the determination of the association strength of each association path. After the association strength of each association path is determined, the association paths and their corresponding association strengths are marked and sorted from largest to smallest according to the number of relational connections in the association strength. If the number of relational connections is the same, they are sorted from largest to smallest according to the hierarchical range covered by the connection location. If both the number of relational connections and the hierarchical range covered by the connection location are the same, they are sorted according to the order of the association paths in the association path set. The sorted association paths are aggregated and arranged according to a unified structure to output the resource association evaluation sequence.

[0075] Furthermore, the correlation strength is used to characterize the degree of correlation influence between resource objects in the correlation path. After the resource correlation assessment sequence is formed, a correspondence is established between the correlation strength of each correlation path and the resource attribute information and resource status information in the correlation path. For each correlation path, the resource quantity field, resource utilization field, and resource quality field of the resource objects in the path are weighted according to the correlation strength, so that the correlation path with a greater correlation strength corresponds to a higher weight in the resource contribution content. The weight allocation result is corrected in combination with the status change in the resource status information to form the resource value representation result corresponding to each correlation path.

[0076] The resource-related assessment sequence is collaboratively value-assessed and associated with the resource attribute information and resource status information in the resource object set, and the resource asset assessment data is output.

[0077] Specifically, each associated path is extracted from the resource association assessment sequence. Based on the order of resource objects within the associated path, the corresponding resource object is located in the resource object set. From the corresponding resource object, the content of each attribute field in the resource attribute information and the content of each status field in the resource status information are extracted. The resource attribute information and resource status information are then sequentially written into the position sequence of the corresponding associated path according to the order of the resource objects in the associated path, so that each associated path forms a resource attribute information sequence and a resource status information sequence consistent with the order of the resource objects. After the resource attribute information sequence and resource status information sequence are formed, the resource attribute information sequences in the same associated path are aggregated and organized according to attribute field names, and the resource status information sequences are arranged and organized according to time order. The resource attribute information sequences and resource status information sequences are then combined correspondingly within the same associated path to form the value statistical record of the corresponding associated path (referring to the data record used to describe the resource contribution and resource stability after aggregating resource attribute information and resource status information within a single associated path). After the value statistical records of all associated paths are formed, each value statistical record is sequentially arranged according to the order of the associated paths in the resource association assessment sequence to form a value statistical record set.

[0078] Furthermore, the specific process of collaborative value assessment is as follows: following the order of the association paths in the resource association assessment sequence, resource objects in each association path are extracted one by one. Resource attribute information and resource status information of the corresponding resource objects are retrieved from the resource object set. The resource attribute information and resource status information of each resource object are written into the same position in the association path. Along the arrangement order of the association path, the resource quantity field of each resource object is counted, the resource utilization field and resource quality field of each resource object are aggregated, and the status category field and status change field of each resource object are arranged. The statistical results, aggregated results, and arrangement results are merged and written into the value record of the corresponding association path. The resource quantity field, resource utilization field, and resource quality field are mapped to the resource contribution content, and the status change field is mapped to the resource stability degree, so that the value statistical record has a comprehensive expression of resource contribution and status change.

[0079] After the value records corresponding to each associated path are formed, the resource contribution content and resource stability are extracted from the value records. The resource contribution content is integrated according to the correspondence between the resource quantity field and the resource quality field. The resource utilization field is used as the expression of utilization degree. Combined with the resource stability degree, the value records of the corresponding associated paths are comprehensively judged. That is, according to the correspondence between resource contribution content, utilization degree and resource stability degree, each element is compared item by item. The resource contribution content is used as the main judgment basis. When the resource contribution content is the same, the comprehensive resource value of the corresponding associated path is determined by combining the utilization degree and resource stability degree, so as to obtain the comprehensive resource value corresponding to each associated path. According to the order of the associated paths in the resource association assessment sequence, the comprehensive resource values ​​corresponding to each associated path are arranged in order to form resource asset assessment data.

[0080] Determining the comprehensive resource value of a corresponding associated path by combining utilization level and resource stability level when resource contribution content is the same means combining utilization level and resource stability level according to their corresponding relationship: when the resource utilization field value corresponding to utilization level is greater than that of another associated path and the state change field corresponding to resource stability level shows decreasing change or remains stable, the comprehensive resource value of the current associated path is determined to be higher than that of the other associated path; when the resource utilization field value corresponding to utilization level is the same and the state change field corresponding to resource stability level shows decreasing change or remains stable, the comprehensive resource value of the current associated path is determined to be higher than that of the associated path with continuously increasing state change; when the resource utilization field value corresponding to utilization level is less than that of another associated path and the state change field corresponding to resource stability level shows increasing change, the comprehensive resource value of the current associated path is determined to be lower than that of the other associated path, thus completing the determination of the comprehensive resource value of the corresponding associated path.

[0081] S5. Match the resource asset assessment data with the pre-stored resource management rules to identify the resource management methods, resource utilization restrictions and resource protection measures corresponding to the resource objects, and integrate them to generate resource asset decision data.

[0082] The resource asset assessment data is matched with the pre-stored resource management rules to determine the resource management rules for each resource object in the resource asset assessment data.

[0083] Specifically, matching resource asset assessment data with pre-stored resource management rules involves extracting resource type, value, and status information for each resource object from the resource asset assessment data. Then, corresponding rule categories are established within the pre-stored resource management rules based on resource type information (this means using resource type information as the classification basis to create a set of rules corresponding to each resource type, ensuring each resource type corresponds to a set of rule entries containing management methods, utilization restrictions, and protection measures, with resource type information used as an index to distinguish and call the rule set). Resource value information is then compared item by item with the corresponding value ranges in the pre-stored resource management rules, and resource status information is compared item by item with the corresponding status categories in the rules. Rule content that simultaneously satisfies the correspondence conditions of resource type, value, and status information is written into the rule identifier position of the corresponding resource object. This ensures that each resource object forms a resource management rule correspondence consistent with the resource asset assessment data, thereby determining the resource management rules for each resource object in the resource asset assessment data.

[0084] Furthermore, the pre-stored resource management rules are compiled from the rule entries in the natural resource classification standards, resource utilization normative documents, resource protection requirement documents, and resource management records. The resource type descriptions, resource value range divisions, and resource status classifications in various rule entries are uniformly organized. The corresponding relationships between the management methods corresponding to resource types, the utilization restrictions corresponding to resource value ranges, and the protection measures corresponding to resource status are established, and the organized correspondences are written into a unified rule table to form pre-stored resource management rules for matching.

[0085] The resource management rules identify the resource management methods, resource utilization restrictions, and resource protection measures for resource objects, and then process these into decision-making data to generate resource asset decision-making data.

[0086] Specifically, the resource management rules corresponding to each resource object are located one by one in the resource asset assessment data. The resource management method field, resource utilization restriction field, and resource protection measure field corresponding to the resource object are read from the resource management rules. The resource management method field, resource utilization restriction field, and resource protection measure field are written into the decision record position of the corresponding resource object, so that the resource management method field, resource utilization restriction field, and resource protection measure field correspond to the resource type information, resource value information, and resource status information of the resource object. After the resource management method field, resource utilization restriction field, and resource protection measure field of each resource object are written, the decision records of each resource object are arranged in order according to the arrangement order of the resource objects in the resource asset assessment data, and the resource asset decision data is output.

[0087] This embodiment also provides a natural resource asset assessment system based on multi-source data, including: The semantic processing module is used to collect multi-source natural resource data, perform spatial coordinate unification and resource semantic mapping processing on the multi-source natural resource data, and form resource semantic mapping data. The object construction module is used to extract spatially continuous resource region data with the same resource semantic labels from the resource semantic mapping data, merge and mark the resource region data, output a resource region set, and obtain resource attribute information and resource status information from the resource semantic mapping data and attach them to the resource region set to form a resource object set. The resource association module is used to identify the spatial adjacency relationship, attribute matching relationship and state evolution relationship between resource objects in the resource object set, perform directional relationship mapping on each resource object, output resource association structure data, and organize the resource association structure data into association hierarchy through graph hierarchical layout algorithm to generate hierarchical association structure data. The asset valuation module is used to deconstruct the association path and determine the strength of the association effect in the hierarchical association structure data, output the resource association valuation sequence, and perform collaborative value assessment and association arrangement with the resource attribute information and resource status information in the resource object set, outputting resource asset valuation data. The resource decision-making module is used to match resource asset assessment data with pre-stored resource management rules, identify the resource management methods, resource utilization restrictions and resource protection measures corresponding to resource objects, and integrate them to generate resource asset decision-making data.

[0088] In summary, this invention achieves orderly support for resource management decisions by constructing resource-related structural data and coordinating resource-related assessment sequences. By identifying spatial adjacency, attribute matching, and state evolution relationships among resource objects in a resource object set and mapping these relationships to form resource-related structural data, a directional and hierarchical connection structure is created between resource objects. This provides a stable foundation for subsequent deconstruction of related paths. The resource-related assessment sequence is then collaboratively value-assessed and associated with resource attribute and state information in the resource object set. This enables resource asset assessment data to reflect the process of inter-resource interaction, thereby improving the accuracy of resource management rule matching and decision generation.

[0089] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for natural resource asset valuation based on multi-source data, characterized in that: include, Collect multi-source natural resource data, perform spatial coordinate unification and resource semantic mapping processing on the multi-source natural resource data, and form resource semantic mapping data; Extract spatially continuous resource region data with the same resource semantic labels from resource semantic mapping data, merge and mark the resource region data, output a resource region set, and obtain resource attribute information and resource status information from resource semantic mapping data and attach them to the resource region set to form a resource object set. The process involves identifying spatial adjacency relationships, attribute matching relationships, and state evolution relationships among resource objects in a resource object set, performing directional relationship mapping on each resource object, and outputting resource association structure data. The specific steps are as follows: Extract the spatial location data, resource attribute information, and resource status information corresponding to each resource object from the resource object set, and combine them into object relationship combination data; Relationship identification is performed on the combined object relationship data, identifying spatial adjacency relationships, attribute matching relationships, and state evolution relationships among various resource objects; The relationship direction between resource objects is determined by state evolution relationship, the relationship range between resource objects is limited by spatial adjacency relationship, attribute matching objects are filtered by attribute matching relationship, and the directional relationship connection data is integrated and output. Connect each resource object sequentially according to the directional relationship connection data, and output the resource association structure data; The resource association structure data is organized hierarchically using a graph hierarchical layout algorithm to generate hierarchical association structure data. The hierarchical data structure is deconstructed to determine the association path and the strength of association effects. The resource association assessment sequence is then output. The resource association assessment sequence is then used in conjunction with the resource attribute information and resource status information in the resource object set for collaborative value assessment and association arrangement. Finally, resource asset assessment data is output. The resource asset assessment data is matched with the pre-stored resource management rules to identify the resource management methods, resource utilization restrictions and resource protection measures corresponding to the resource objects, and then integrated to generate resource asset decision data.

2. The natural resource asset assessment method based on multi-source data as described in claim 1, characterized in that: The specific steps for forming resource semantic mapping data are as follows: Spatial coordinate analysis is performed on multi-source natural resource data to determine the spatial location data corresponding to each resource data item in the multi-source natural resource data. Spatial location data is transformed into spatial coordinates according to a unified spatial coordinate system to form coordinate unified resource data; Perform resource semantic annotation on the coordinate unified resource data, output resource semantic tags, and classify the coordinate unified resource data into semantic categories according to the resource semantic tags to form resource semantic mapping data; The multi-source natural resource data includes remote sensing monitoring data, geospatial data, environmental monitoring data, and resource utilization data.

3. The natural resource asset assessment method based on multi-source data as described in claim 1, characterized in that: The specific steps for defining the output resource region set are as follows: Extract spatial distribution information corresponding to each resource semantic tag from the resource semantic mapping data, perform continuous identification on the spatial distribution information, and output continuously distributed resource data; Aggregate spatial regions with the same resource semantic labels in continuously distributed resource data and output resource region data; Write region identifier information for each resource region in the resource region data, organize each resource region centrally according to the region identifier information, and output the resource region set.

4. The natural resource asset assessment method based on multi-source data as described in claim 1, characterized in that: The resource object set refers to the set of resource data items that are selected from the resource semantic mapping data according to the spatial range of each resource region, the resource attribute information and resource status information of the resource data items are read, and the resource attribute information and resource status information are written into the record of the corresponding resource region.

5. The natural resource asset assessment method based on multi-source data as described in claim 1, characterized in that: The aforementioned hierarchical organization refers to using the relationship direction in the data connected by directional relationships as the hierarchical basis, and arranging resource objects without prior connections in the upper layer and resource objects with prior connections in the lower layer through a graph hierarchical layout algorithm, forming a hierarchical association structure data with a clear upper and lower hierarchical relationship.

6. The natural resource asset assessment method based on multi-source data as described in claim 1, characterized in that: The specific steps for the output resource association evaluation sequence are as follows: Expand the paths of each resource object in the hierarchical relational structure data and output a set of relational paths; The number and position of connections in each associated path in the associated path set are statistically analyzed to determine the association strength of each associated path. The associated path set is then ordered by the associated paths and association strength to output a resource association evaluation sequence.

7. The method for natural resource asset assessment based on multi-source data as described in claim 1, characterized in that: The aforementioned collaborative value assessment and association arrangement refers to mapping the resource attribute information and resource status information in the resource object set to each association path according to the association path order in the resource association assessment sequence, performing value statistics on resource objects within the same association path, and arranging the value statistics results in order according to the association path order to form resource asset assessment data.

8. The method for natural resource asset assessment based on multi-source data as described in claim 1, characterized in that: The specific steps for generating resource asset decision data are as follows: The resource asset assessment data is matched with the pre-stored resource management rules to determine the resource management rules for each resource object in the resource asset assessment data; The resource management rules identify the resource management methods, resource utilization restrictions, and resource protection measures for resource objects, and then process these into decision-making data to generate resource asset decision-making data.

9. A natural resource asset assessment system based on multi-source data, based on the natural resource asset assessment method based on multi-source data as described in any one of claims 1 to 8, characterized in that: include, The semantic processing module is used to collect multi-source natural resource data, perform spatial coordinate unification and resource semantic mapping processing on the multi-source natural resource data, and form resource semantic mapping data. The object construction module is used to extract spatially continuous resource region data with the same resource semantic labels from the resource semantic mapping data, merge and mark the resource region data, output a resource region set, and obtain resource attribute information and resource status information from the resource semantic mapping data and attach them to the resource region set to form a resource object set. The resource association module is used to identify the spatial adjacency relationship, attribute matching relationship and state evolution relationship between resource objects in the resource object set, perform directional relationship mapping on each resource object, output resource association structure data, and organize the resource association structure data into association hierarchy through graph hierarchical layout algorithm to generate hierarchical association structure data. The asset valuation module is used to deconstruct the association path and determine the strength of the association effect in the hierarchical association structure data, output the resource association valuation sequence, and perform collaborative value assessment and association arrangement with the resource attribute information and resource status information in the resource object set, outputting resource asset valuation data. The resource decision-making module is used to match resource asset assessment data with pre-stored resource management rules, identify the resource management methods, resource utilization restrictions and resource protection measures corresponding to resource objects, and integrate them to generate resource asset decision-making data.

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