A method for channel surveying and mapping achievement warehousing auditing and permission hierarchical management

Abstract

The application discloses a kind of channel surveying and mapping achievement warehousing auditing and permission hierarchical management method, specifically related to channel spatial data management field, for solving the problem of post-positioning of permission control under spatial index exploration stage;Through in the channel surveying and mapping spatial geometric data flow access after, during spatial topology check, the business sensitivity parameter and spatial distribution boundary of spatial geometric element are extracted, a multidimensional security feature vector is formed, then the business sensitivity parameter is mapped to security control mask and solidified to the internal node feature load area of corresponding internal node;In the internal node clustering stage, the difference degree of permission mask and the spatial containment expansion ratio are calculated by combining security control mask and spatial distribution boundary, and after being determined by support vector data description model, upward permission compatible merging or security boundary box is generated;In the process of spatial inquiry instruction exploration, according to the logical bit mapping result of request party attribute label and security control mask, traversal blocking control is implemented.

Description

Technical Field

[0001] This invention relates to the field of waterway spatial data management, and more specifically, to a method for the review and hierarchical management of waterway surveying and mapping results for database entry. Background Technology

[0002] With the digitization of waterway surveying results, data such as multibeam bathymetry point clouds and navigational obstruction vectors are no longer just single surveying results files, but are gradually entering a spatial data management platform that allows for retrieval, access, and cross-departmental reuse. The unique characteristic of this type of data lies in its distinct spatial geometric attributes, requiring participation in spatial topology verification and spatial index construction based on point, line, surface, or point cloud sets. Simultaneously, it carries business attributes such as classification levels, industry layer categories, and timeliness constraints. For the same waterway area, military waterway layers, civilian navigational obstruction layers, bathymetry point clouds from different surveys, and results data with different validity periods may coexist, spatially adjacent or even overlapping, but with different access permissions. Therefore, the review of waterway surveying results for retrieval is not simply a matter of data format verification or geometric boundary entry, but requires addressing permission hierarchy issues simultaneously with the formation of the spatial retrieval structure.

[0003] Existing spatial database access control typically revolves around users, spatial objects, authorized regions, and spatial predicates. For example, patent document CN101976249A, entitled "An Access Control Method for a Spatial Database," discloses a processing flow including user authentication, query parse tree generation, retrieval of authorization policies based on the query parse tree, rewriting of the query parse tree based on the authorization policies, and optimization and execution of the rewritten query. This document also sets the authorization policy as a six-tuple containing subject, object, permission, authorized region, policy type, and propagation options to express the access control relationships in the spatial database. The focus of this type of solution is on authorization policy retrieval and query rewriting at the query semantic level; that is, after a user initiates a spatial query, access control conditions are incorporated into the query execution process.

[0004] However, there is a deeper problem in the retrieval of waterway mapping results: the access path for spatial data often first passes through the spatial index tree. When permission judgment mainly relies on query rewriting, external policy retrieval, or application-layer filtering, the spatial index itself still mainly organizes data according to spatial distribution boundaries, and permission information does not become an endogenous constraint in the internal node tree building and exploration process. Thus, in sea areas with mixed military and civilian use or sea areas with varying levels of classification, spatially similar data may be aggregated into the same index branch, while mutually exclusive data needs to be stripped during retrieval. If permission filtering is only performed after the retrieval cursor has already explored down to the leaf node or triggered the reading of underlying data, it will increase the burden of invalid reads and subsequent removal, and also cause high-precision raw spatial data to be exposed prematurely in the processing chain. Therefore, a processing method is needed that embeds permission control information into the hierarchical spatial index structure during the review and approval of waterway mapping results, and implements blocking based on the internal node permission information during the retrieval exploration stage.

[0005] To address the aforementioned problems, a technical solution is provided. Summary of the Invention

[0006] To overcome the aforementioned deficiencies in the prior art, embodiments of the present invention provide a method for the review and hierarchical management of waterway surveying results. This method involves extracting business sensitivity parameters and spatial distribution boundaries of spatial geometric elements during spatial topology verification after the waterway surveying spatial geometric data stream is accessed, forming a multi-dimensional security feature vector. The business sensitivity parameters are then mapped to security control masks and solidified into the internal node feature load area of ​​the corresponding internal nodes. During the upward clustering stage of internal nodes, the dissimilarity of the permission mask and the spatial coverage expansion ratio are calculated by combining the security control mask and the spatial distribution boundary. After judgment by the support vector data description model, upward permission compatibility merging or generation of security boundary boxes is performed. During the downward exploration of spatial access commands, traversal blocking control is implemented based on the logical bit mapping results between the requester's attribute tags and the security control mask, thereby solving the problems mentioned in the background art.

[0007] To achieve the above objectives, the present invention provides the following technical solution: A method for reviewing and managing the access permissions of waterway surveying results for database entry includes the following steps: S1: In response to the access of the spatial geometry data stream of waterway surveying, extract the business sensitivity parameters and spatial distribution boundaries of spatial geometric elements during the spatial topology verification, and generate a multi-dimensional security feature vector; S2: Based on the multi-dimensional security feature vector, the business sensitivity parameter is mapped to the security control mask, and when constructing the hierarchical spatial index structure, it is solidified into the internal node feature load area of ​​the corresponding internal node; S3: During the upward clustering stage of internal nodes, the dissimilarity of the permission mask and the spatial containment expansion ratio are calculated based on the security control mask and the spatial distribution boundary. After the support vector data description model determines the upward permission compatibility merging or generates a security bounding box. S4: Parse the spatial retrieval command carrying the requester's attribute label. When exploring the hierarchical spatial index structure and encountering a security boundary box, obtain the logical bit mapping result between the requester's attribute label and the security control mask attached to the security boundary box, and implement traversal blocking control.

[0008] Furthermore, in step S1, the data processing server establishes an element processing sequence according to the access order of the waterway surveying spatial geometry data stream; maintains a one-to-one correspondence between the coordinate sequence, business attribute record, business sensitivity parameter, and spatial distribution boundary of each spatial geometry element through a correspondence index; and encapsulates the business sensitivity parameter and spatial distribution boundary of the same spatial geometry element into a multi-dimensional security feature vector.

[0009] Furthermore, in step S2, the data processing server calls the mask mapping rule table to convert the confidentiality level, industry layer category, and timeliness constraint scalar in the business sensitivity parameter into the high-segment encoding, middle-segment encoding, and low-segment timestamp check bit of the security control mask, respectively; and writes the security control mask into the internal node feature load area within the same internal node storage record as the internal node's spatial boundary description information.

[0010] Furthermore, in step S3, the data processing server uses the internal node merging records in the historical steady-state channel model to form a historical node merging evaluation vector, and trains the support vector data description model to obtain the hypersphere center and the hypersphere radius as a preset safety baseline; the spatial containment expansion ratio and the dissimilarity of the permission mask are input into the support vector data description model, and the envelope surface free judgment value is output; when the envelope surface free judgment value exceeds the preset safety baseline, upward permission compatible merging is performed; when it does not exceed the preset safety baseline, the dissimilarity of the permission mask is further determined to see if it is mutually exclusive.

[0011] Furthermore, in step S2, the data processing server converts the timeliness constraint scalar into a time validity bucket according to a preset time granularity, and generates a low-segment timestamp verification bit based on the time validity bucket; in step S4, the data processing server generates a low-segment authorization validity bit of the requester's authorization bit string according to the same time validity bucket rule for the time access constraint in the requester's attribute tag.

[0012] Furthermore, in step S3, when the data processing server calculates the spatial containment expansion ratio, it forms a relative difference ratio based on the difference between the minimum bounding volume of the merged bounding box and the sum of the volumes of its child nodes; when the relative difference ratio is negative, the negative value is retained as the input value of the support vector data description model; if there are multiple candidate internal nodes at the current clustering level, the multiple candidate internal nodes are divided into pairs of internal nodes to be merged, and envelope evaluation is performed on each pair of internal nodes to be merged.

[0013] Furthermore, in step S3, when the dissimilarity of the permission masks is mutually exclusive, the data processing server allocates the internal nodes to be merged to different split branches according to the permission distribution boundary, and calculates the minimum bounding box for the spatial distribution boundary within each split branch to generate a security bounding box; when the dissimilarity of the permission masks is not mutually exclusive, the data processing server performs a bitwise OR operation on the security control masks of the child nodes participating in the proximity clustering to form the security control mask of the parent node.

[0014] Furthermore, in step S4, the spatial access instruction carries the requester attribute label and the target access spatial range; the requester attribute label includes security level authorization, layer access range and time access constraints; the data processing server calls the same mask mapping rule table as in step S2 to convert the requester attribute label into a requester authorization bit string with the same bit width, the same bit segment order and the same bit segment meaning as the security control mask.

[0015] Furthermore, in step S4, when the logical bit mapping result is no permission or partially exceeded, the data processing server discards the retrieval pointer of the currently encountered security boundary box pointing to the subordinate node and generates the record content of the blocking branch; the record content of the blocking branch is limited to the security boundary box identifier, the blocking status and the logical bit mapping result; when the logical bit mapping result is authorized, the data processing server reads the waterway mapping results corresponding to the leaf node and writes them into the spatial retrieval result set.

[0016] Furthermore, in step S4, when the data processing server outputs the spatial retrieval result set, it only writes the waterway mapping results that have been authorized and whose spatial distribution boundary intersects with the target retrieval spatial range; for the safety boundary box marked as a blocking branch, the data processing server retains the blocking status in the record content of the blocking branch and does not write the spatial geometric coordinates of the leaf nodes under the safety boundary box.

[0017] The technical effects and advantages of the present invention regarding the method for reviewing and managing the access permissions of waterway surveying results in the database are as follows: This invention maps business sensitivity parameters to security control masks and embeds these masks into the internal node feature load area of ​​a hierarchical spatial index structure. This ensures that access control information and spatial distribution boundaries are read at the same internal node level. Compared to processing methods that primarily rely on query rewriting, external authorization strategies, or application-layer filtering, unauthorized access paths can be identified during the retrieval cursor's exploration, reducing access to leaf node data that should not be included in the access chain.

[0018] This invention introduces both permission mask dissimilarity and spatial coverage expansion ratio during the upward clustering stage of internal nodes, and performs branching processing on spatially adjacent but mutually exclusive internal nodes. For situations where military waterway layers, civilian navigational obstruction layers, etc., are interspersed in the same sea area, the data processing server can perform upward permission compatibility merging or generate safe bounding boxes based on the judgment results of the support vector data description model, so that the index construction process no longer depends solely on spatial distribution boundaries.

[0019] This invention generates logical bit mapping results based on the requester's attribute tags and the security control mask attached to the security boundary box during the spatial access phase, and discards the current probe pointer when there is no authorization or partial overreach. Therefore, the spatial access result set only includes the waterway mapping results that have been authorized and whose spatial distribution boundary intersects with the target access spatial range; for security boundary boxes marked as blocking branches, the spatial geometric coordinates of their subordinate leaf nodes are not written, ensuring that the access output range remains consistent with the authorization boundary. Attached Figure Description

[0020] Figure 1 This is a flowchart of a method for reviewing and managing the access levels of waterway surveying results in the database, according to the present invention. Figure 2 This is a schematic diagram of the structure in which the security control mask of the present invention is embedded into the feature load area of ​​the internal node; Figure 3 This is a flowchart of the support vector envelope evaluation process during the upward clustering stage of internal nodes in this invention. Detailed Implementation

[0021] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0022] Please see Figure 1 - Figure 3 This invention provides a method for reviewing and managing the access permissions of waterway surveying and mapping results for database entry, including: This invention uses waterway surveying spatial geometry data stream as processing input. The data processing server first locks spatial geometric elements item by item during spatial topology verification and extracts business sensitivity parameters from corresponding business attribute records. Simultaneously, it forms spatial distribution boundaries based on coordinate sequences, thereby generating a multi-dimensional security feature vector. Subsequently, based on the multi-dimensional security feature vector, the business sensitivity parameters are mapped to security control masks. When constructing a hierarchical spatial index structure, the security control masks are written into the internal node feature load area of ​​the corresponding internal nodes, enabling internal nodes to simultaneously carry spatial distribution boundaries and access control information. During the upward clustering phase of internal nodes, the data processing server reads the security control masks and spatial distribution boundaries of the internal nodes to be merged, calculates the dissimilarity of the access mask and the spatial containment expansion ratio, and determines the current clustering path using a support vector data description model, forming an upward access-compatible merging result or a security bounding box. When a spatial retrieval command enters the downward exploration phase of the hierarchical spatial index structure, the data processing server parses the requester's attribute tags and reads the attached security control mask when the retrieval cursor encounters a security bounding box. Then, based on the logical bit mapping result, it decides whether to retain the downward exploration pointer or implement traversal blocking control.

[0023] In one implementation, a method for reviewing and managing the access permissions of waterway surveying results is executed by a data processing server. The processing object is the waterway surveying spatial geometric data stream, which includes multibeam bathymetry point clouds, navigational obstruction vectors, and business attribute records corresponding to spatial geometric elements. During the review and approval phase of waterway surveying results entering the database, the data processing server simultaneously constructs a hierarchical spatial index structure and writes the access control information of spatial geometric elements into the internal nodes of the index. This allows subsequent spatial retrieval commands to complete access permission determination during the index traversal and exploration phase.

[0024] S1: In response to the access of the spatial geometric data stream for waterway surveying, extract the business sensitivity parameters and spatial distribution boundaries of spatial geometric elements during the spatial topology verification period, and generate a multi-dimensional security feature vector.

[0025] The specific implementation method of step S1 is as follows: The data processing server responds to the access of the waterway mapping spatial geometry data stream, reading the spatial geometric elements in the data stream item by item during spatial topology verification. Spatial geometric elements refer to spatial objects in waterway mapping results that can be expressed as points, lines, surfaces, or point cloud sets; for multibeam bathymetry point clouds, spatial geometric elements consist of bathymetry sampling points within the same survey area or the same survey line segment; for navigational obstruction vectors, spatial geometric elements consist of the obstruction's boundary points, center positioning points, or vector contours.

[0026] The data processing server establishes a feature processing sequence according to the access order of the spatial geometric data stream and generates a corresponding relationship index for each spatial geometric feature. The corresponding relationship index is used to maintain a one-to-one correspondence between the coordinate sequence, business attribute records, business sensitivity parameters, and spatial distribution boundaries of the same spatial geometric feature. The data processing server reads the geometric description information of each spatial geometric feature and uniformly converts point clouds, linear features, and area features into coordinate sequences that can be traversed by the spatial topology verification process. Before the coordinate sequences enter the spatial distribution boundary calculation, the data processing server transforms the coordinate sequences in the same waterway mapping spatial geometric data stream to the same plane coordinate datum, so that the lateral and longitudinal coordinates participate in the calculation under the same coordinate datum.

[0027] After the data processing server locates the current spatial geometric element using the spatial topology verification cursor, it reads the corresponding business attribute record for that element. From this record, it extracts the confidentiality level, industry layer category, and timeliness constraint scalar, combining these three elements to form the business sensitivity parameter for that spatial geometric element. The business sensitivity parameter represents the access control requirements for the spatial geometric element in terms of confidentiality level, business layer, and effective usage time. The confidentiality level is determined based on the confidentiality management rules adopted for the review and approval of waterway surveying results entering the database; the industry layer category is determined based on the business purpose of the layer to which the spatial geometric element belongs; and the timeliness constraint scalar is determined based on the time constraint information in the corresponding business attribute record of the spatial geometric element.

[0028] In one implementation, the timeliness constraint scalar is determined based on the time of result formation and the effective use deadline. The data processing server converts both into the same time unit, calculates the effective interval, and uses this effective interval as the timeliness constraint scalar. In this implementation, the formation relationship of the timeliness constraint scalar is as follows: , in, For the first The timeliness constraint scalar of each spatial geometric element is used to generate the low-order segment timestamp verification bit in the security control mask in step S2. For the first The effective usage deadline for each spatial geometric element is derived from the business attribute record corresponding to that spatial geometric element. For the first The time of creation of each spatial geometric element is derived from the business attribute record corresponding to that spatial geometric element; Each spatial geometric feature is the currently processed object located in the feature processing sequence according to the access order.

[0029] After completing the coordinate sequence traversal of the current spatial geometric feature, the data processing server calculates the minimum bounding rectangle based on the coordinate sequence and uses this minimum bounding rectangle as the spatial distribution boundary of the spatial geometric feature. For the th The spatial geometric elements and their spatial distribution boundaries are formed as follows: , in, For the first The spatial distribution boundary of each spatial geometric element is used in step S2 to bind the spatial boundary description information of the internal nodes, and in step S3 to calculate the spatial containment expansion ratio. For the first The minimum horizontal coordinate in a spatial geometric element coordinate sequence; For the first The minimum value of the longitudinal coordinate in a spatial geometric element coordinate sequence; For the first The maximum horizontal coordinate value in a spatial geometric element coordinate sequence; For the first The maximum vertical coordinate value in the coordinate sequence of spatial geometric elements. For multibeam bathymetry point clouds, the data processing server uses the planar coordinates of the bathymetry sampling points to participate in the spatial distribution boundary calculation. The bathymetry value is retained as attribute data of the waterway mapping results corresponding to the leaf nodes and is not written into the spatial distribution boundary of the internal nodes.

[0030] The data processing server encapsulates the correspondence index, spatial distribution boundary, and business sensitivity parameters of the same spatial geometric feature into a multidimensional security feature vector according to a fixed field order. This multidimensional security feature vector is a structured data carrier used for subsequent index construction; it contains at least the spatial distribution boundary and business sensitivity parameters, and maintains the binding relationship between these two and the same spatial geometric feature through the correspondence index. Multiple multidimensional security feature vectors corresponding to spatial geometric features are combined into a multidimensional security feature vector set according to the feature processing sequence. This multidimensional security feature vector set serves as the input source for step S2, which performs security control mask mapping and solidifies the internal node feature load area.

[0031] For example, during the data entry process of a batch of waterway mapping results, the data processing server first accesses the multibeam bathymetry point cloud of a certain sea area, and then accesses the navigational obstruction vectors of the same sea area. The data processing server writes both the bathymetry sampling point clusters and the navigational obstruction boundaries as spatial geometric elements into the element processing sequence, and reads their respective business attribute records. For the bathymetry point cloud in the military waterway layer, the data processing server extracts its classification level, industry layer category, and timeliness constraint scalars, and forms a spatial distribution boundary based on the coordinates of the bathymetry sampling points; for the civilian navigational obstruction vectors, the data processing server extracts their corresponding business sensitivity parameters, and forms a spatial distribution boundary based on the vector boundary points. Both are encapsulated into multidimensional security feature vectors and proceed to step S2.

[0032] S2: Based on the multidimensional security feature vector, the business sensitivity parameter is mapped to the security control mask, and when constructing the hierarchical spatial index structure, it is solidified to the internal node feature load area of ​​the corresponding internal node.

[0033] The specific implementation method of step S2 is as follows: The data processing server uses the set of multidimensional security feature vectors output in step S1 as the starting point for processing. Before constructing the underlying hierarchical spatial index structure, it reads the multidimensional security feature vectors item by item according to the feature processing sequence, and obtains the business sensitivity parameter and spatial distribution boundary from each multidimensional security feature vector. The data processing server verifies whether the business sensitivity parameter and spatial distribution boundary within the same multidimensional security feature vector correspond to the same spatial geometric feature through the correspondence index. When they correspond to the same spatial geometric feature, the data processing server writes the multidimensional security feature vector into the mapping input sequence. The mapping input sequence is a processing sequence formed by the multidimensional security feature vectors verified through the correspondence according to the feature processing sequence, and is used to provide stable input to the mask mapping rule table.

[0034] The data processing server reads the business sensitivity parameters from each multi-dimensional security feature vector one by one according to the order of the mapped input sequence, and calls the mask mapping rule table to convert the business sensitivity parameters into security control masks. The mask mapping rule table is a pre-configured encoding correspondence in the data processing server, including the correspondence between the confidentiality level and the high-order segment encoding, the correspondence between the industry layer category and the middle-order segment encoding, and the conversion relationship between the timeliness constraint scalar and the low-order segment timestamp check bit. The mask mapping rule table is configured in a way that the segments do not overlap. The high-order segment is only used to receive the confidentiality level encoding, the middle-order segment is only used to receive the industry layer category encoding, and the low-order segment is only used to receive the timestamp check bit corresponding to the timeliness constraint scalar. In one embodiment, the security control mask is a 64-bit binary string, where bits 63 to 56 are the high-order segments, bits 55 to 32 are the middle-order segments, and bits 31 to 0 are the low-order segments. The high-order segments use a single valid bit to represent the confidentiality level, the middle-order segments use at least one valid bit to represent the industry layer category, and the low-order segments use valid bits corresponding to the time-sensitive buckets to represent timeliness constraints. Bits not marked as valid in the mask mapping rule table are reserved bits, which are not compared in the logical bit mapping result determination. For the same business sensitivity parameter, the data processing server outputs a unique security control mask under the same mask mapping rule table; for different confidentiality levels or different industry layer categories, the mask mapping rule table assigns different combinations of valid bits to make them distinguishable in subsequent XOR statistics and bitwise AND determinations.

[0035] The bit field organization of the security control mask is as follows: , in, For the first The security control mask corresponding to each spatial geometric element is derived from the first... The business sensitivity parameter in the multidimensional security feature vector is used to write the corresponding internal node feature load area; For the first The high-bit segment encoding obtained by converting the confidentiality level of each spatial geometric element through a mask mapping rule table is used to express the confidentiality access requirements. For the first The median segment code obtained by converting the industry layer category of each spatial geometric feature through the mask mapping rule table is used to express the layer access requirements. For the first The low-order timestamp check bits, obtained by using a deterministic hash function or deterministic coding rule from the timeliness constraint scalar of each spatial geometric element, are used to express the time access constraint; symbol This indicates that binary data is concatenated in the order of high-order segment, middle-order segment, and low-order segment.

[0036] During the low-segment generation process, the data processing server converts the timeliness constraint scalar into time-valid buckets according to a preset time granularity. A time-valid bucket refers to a time constraint coding unit formed after being divided according to the same time granularity. The preset time granularity is given by the waterway surveying results entry review configuration file and remains consistent during the generation of the security control mask and the parsing of spatial access instructions for the same batch of waterway surveying spatial geometric data streams. In one embodiment, the preset time granularity is 1 day. The data processing server generates low-segment timestamp verification bits based on the bucket number corresponding to the natural day of the result formation time or the effective use deadline. The data processing server prioritizes generating low-segment timestamp verification bits according to the one-to-one correspondence between the time-valid bucket number and the low-segment valid bits. When the number of time-valid buckets exceeds the number of available low-segment valid bits, the data processing server calls a preset deterministic hash function to compress the time-valid bucket number to the range of available low-segment valid bits. The same low-segment generation rule is used for the same batch of waterway surveying spatial geometric data streams. If the same timeliness constraint covers multiple time-valid buckets, the data processing server performs a bitwise OR operation on the low-order timestamp check bits corresponding to the multiple time-valid buckets to obtain the low-order segment code corresponding to the spatial geometric element. This process enables the requester's time access constraint in step S4 to form comparable low-order segment authorization valid bits according to the same time-valid bucket rules.

[0037] After generating the security control mask, the data processing server generates candidate internal nodes based on the spatial distribution boundaries within the same multidimensional security feature vector. These candidate internal nodes are non-leaf node preparatory objects that, while not yet having completed parent-child hierarchical clustering during the hierarchical spatial index structure construction, already possess spatial boundary description information and standard pointer fields. The data processing server will then... The spatial distribution boundary of each spatial geometric element is written into the spatial boundary description information of the corresponding internal node candidate object, and the first... The security control mask corresponding to each spatial geometric element serves as the permission input for the candidate object of the same internal node, and the two are bound together through the correspondence index.

[0038] The data processing server accesses the internal node feature payload area outside the standard pointer field of the internal node candidate object. The internal node feature payload area is a custom attribute mounting space set within the internal node storage record, used to store the security control mask. The internal node feature payload area and the spatial boundary description information of the internal node establish a fixed offset relationship or an equivalent intra-page positioning relationship within the same internal node storage record, enabling the retrieval cursor to read the security control mask within the same internal node storage record when reading the internal node spatial boundary description information.

[0039] The data processing server writes the security control mask into the internal node feature load area of ​​the corresponding internal node candidate object, and confirms the internal node candidate object that has completed the writing as an internal node. After the writing is completed, the data processing server generates a mask persistence marker. The mask persistence marker is used to record the status that the security control mask has been written into the feature load area of ​​the corresponding internal node, so that the internal node can participate in the permission mask dissimilarity calculation in step S3, and can also participate in the logical bit mapping result determination in step S4.

[0040] For example, after a multibeam bathymetry point cloud of a military waterway layer enters step S2, the data processing server converts its classification level into a high-segment code, converts the industry layer category corresponding to the military waterway layer into a mid-segment code, and converts its timeliness constraint scalar into a low-segment timestamp check bit, then concatenates them to form a security control mask. The data processing server then writes the spatial distribution boundary of the bathymetry point cloud into the spatial boundary description information of the corresponding internal node, and writes the security control mask into the internal node feature load area of ​​that internal node. For civilian navigational obstruction vectors in the same sea area, the data processing server generates another security control mask according to the same rules and writes it into the corresponding internal node feature load area.

[0041] S3: During the upward clustering stage of internal nodes, the dissimilarity of the permission mask and the spatial containment expansion ratio are calculated based on the security control mask and the spatial distribution boundary. After the support vector data description model determines the upward permission compatibility merging or generates a security bounding box. The specific implementation method of step S3 is as follows: The data processing server uses the internal nodes, internal node feature load areas, security control masks, spatial distribution boundaries, and mask fixation markers output in step S2 as the starting point. During the stage of performing upward clustering of internal nodes to form a hierarchical spatial index structure, it implements anomaly defense processing based on the support vector envelope evaluation mechanism. This anomaly defense processing is used to determine whether, when spatial proximity and permission exclusion relationships coexist, the current internal node to be merged should undergo upward permission-compatible merging, conventional spatial proximity clustering, or forced splitting based on the permission distribution boundary.

[0042] The data processing server extracts pairs of internal nodes that satisfy the spatial proximity condition from the current clustering level, denoted as the nth pair. The internal node and the first The data processing server is only on the first internal node. The internal node and the first When each internal node has a masked fixed mark, read the internal node feature load area of ​​both and obtain the security control mask respectively. and Simultaneously, read the spatial boundary description information of both, and obtain the spatial distribution boundaries respectively. and If there are multiple candidate internal nodes at the current clustering level, the data processing server will divide the multiple candidate internal nodes into pairs of internal nodes to be merged according to their spatial proximity, and perform subsequent envelope evaluation on each pair of internal nodes to be merged.

[0043] The data processing server first checks the security control mask. and Execute the underlying logic XOR instruction, count the number of binary 1s in the XOR result, and divide by the total bit width of the security control mask to obtain the dissimilarity of the permission mask: , in, For the first The internal node and the first The dissimilarity of the permission masks between internal nodes is used to characterize the degree of difference between their security control masks. For the first The security control mask of an internal node is derived from the internal node characteristic load area of ​​that internal node. For the first The security control mask of an internal node is derived from the internal node characteristic load area of ​​that internal node. This represents the underlying logic XOR operation; This indicates the number of 1s in the binary result; To ensure security control of the total bit width of the mask, it is derived from the preset bit width of the fixed-length binary bit string in step S2.

[0044] After obtaining the dissimilarity of the permission mask, the data processing server calculates the space coverage expansion ratio. The data processing server then calculates the expansion ratio based on the... The internal node and the first The spatial distribution boundary of each internal node generates a merged bounding box that can simultaneously enclose both child nodes, and the ratio of the relative difference between the minimum bounding volume of the merged bounding box and the sum of the volumes of the two child nodes is calculated: , in, For the first The internal node and the first The spatial containment expansion ratio between internal nodes is used to characterize the degree of relative change of the merge bounding box relative to the spatial distribution boundary of the atomic nodes when the two are merged. For the first Spatial distribution boundary of internal nodes; For the first Spatial distribution boundary of internal nodes; To surround simultaneously and The minimum bounding volume of the merged bounding box; For the first The volume of the spatial distribution boundary of each internal node; For the first The volume of the spatial distribution boundary of each internal node. In the two-dimensional channel layer, the volume is equivalent to the area of ​​the smallest bounding rectangle. When two spatial distribution boundaries overlap and the spatial containment expansion ratio is negative, the data processing server retains this negative value as the input value of the support vector data description model. The corresponding negative value samples are also retained in the historical node merging evaluation vector, so that the training phase and the current evaluation phase use the same data distribution.

[0045] The data processing server constructs a node merging evaluation vector by placing the spatial coverage expansion ratio in the first dimension and the permission mask dissimilarity in the second dimension in a fixed order. , in, For the first The internal node and the first The node merge evaluation vector of each internal node is used as input support vector data to describe the model; This represents the spatial expansion ratio of the current pair of internal nodes to be merged. This represents the dissimilarity of the permission masks for the current pair of internal nodes to be merged.

[0046] In one embodiment, the support vector data description model is implemented using a kernelized single-class envelope model. Its computational structure is divided into an input layer, a normalization layer, a kernel mapping layer, and a distance decision layer according to the data flow. The input layer contains two input units, receiving the spatial coverage expansion ratio and the permission mask dissimilarity, respectively. The normalization layer performs minimum-to-maximum normalization on the two input units, ensuring they fall within the 0-1 range. The normalization layer reads the minimum and maximum values ​​of the spatial coverage expansion ratio and the permission mask dissimilarity from the training set, and generates a normalized input value by subtracting the corresponding minimum value from the current input value and then dividing by the difference between the corresponding maximum and minimum values. When the corresponding maximum and minimum values ​​are equal, the data processing server sets the normalized input value for that dimension to 0. The kernel mapping layer uses a radial basis function to map the merged evaluation vectors of two-dimensional nodes to a high-dimensional feature space, with a kernel function width parameter of 4.0. The distance decision layer calculates the distance from the current mapped point to the center of the hypersphere and outputs the envelope surface free decision value. The kernel mapping layer of this support vector data description model serves to represent nonlinear boundaries. The training dataset consists of internal node merging records from the historical steady-state channel model. In this embodiment, the data processing server selects 8000 manually reviewed historical internal node merging records as training samples. Each record contains two security control masks, two spatial distribution boundaries, and the manual review result before the internal node merging. The data processing server calculates only two training features from each record: spatial coverage expansion ratio and permission mask dissimilarity, and assembles them into a historical node merging evaluation vector in a fixed order. Of these, 7200 records are used as the training set, and 800 records are used as the validation set. The training set retains only internal node merging records that have not experienced abnormal increases in index tree depth, have not experienced unauthorized access, and can complete normal spatial retrieval. The validation set retains normal internal node merging records and abnormal internal node merging records from the manual review results. Abnormal internal node merging records include those that have experienced abnormal increases in index tree depth, unauthorized access, or have failed to complete normal spatial retrieval. The validation set is only used for fixing the kernel function width parameter and relaxation penalty coefficient, and does not participate in the training and solving of the hypersphere center and hypersphere radius. During training, the data processing server uses support vector data to describe the objective function, ensuring that the training samples are enveloped within a minimum hypersphere in the kernel mapping space. The loss function consists of a hypersphere radius term and a relaxation error term, with a relaxation penalty coefficient of 0.05. The iteration stopping condition is when the change in the objective function between two consecutive iterations is less than 0.0001 or the number of iterations reaches 500. After training, the data processing server saves the support vector set, the hypersphere center, the hypersphere radius, and the normalization parameters. The hypersphere radius serves as a preset safety baseline, and the hypersphere center is used for subsequent calculation of the envelope detachment determination value. The model pre-training parameters are obtained offline from the historical training dataset. The normalization parameter is determined by the minimum and maximum values ​​of the spatial coverage expansion ratio and the permission mask dissimilarity in the training set; the radial basis kernel function width parameter is initially set to 4.0 and is fixed in the validation set to minimize the number of falsely identified anomalous merged records; the relaxation penalty coefficient is initially set to 0.05 to allow a small number of historical merged records to be located outside the hypersphere. After the model training is completed, the data processing server writes the normalization parameters, kernel function width parameter, support vector set, hypersphere center, and preset safety baseline into the model parameter file, which is directly called when performing internal node upward clustering in step S3.

[0047] The node merging evaluation vector formed by the internal nodes to be merged. The data processing server calls the model parameter file and... After normalization according to the normalization parameters, the input is given to the kernel mapping layer, and the distance from the mapping point to the center of the hypersphere is calculated. The distance is taken as the free envelope value. The calculation relationship for the free envelope value is as follows: , in, For the first The internal node and the first The envelope surface free determination value corresponding to each internal node is used for comparison with the preset safety baseline; To support vector data description models, the evaluation vectors for node merging are used. The mapping points obtained after performing high-dimensional feature mapping; The hypersphere center obtained by training the model using support vector data description; This indicates distance calculation, which in this embodiment is Euclidean distance; The hypersphere radius obtained from training the support vector data description model is used as a preset safety baseline.

[0048] When the following conditions are met: When the condition is met, the data processing server sets the envelope evaluation result to the above-baseline state; when the condition is not met, the data processing server sets the envelope evaluation result to the in-baseline state.

[0049] When the envelope evaluation result is above baseline, the data processing server triggers an upward permission-compatible merge, creating a parent node to receive the clustering results of the currently merged internal nodes. The data processing server reads the security control masks of each of the currently merged internal nodes and performs a bitwise OR operation to form the security control mask of the parent node. , in, This is the security control mask for the parent node, used to write to the parent node's internal node characteristic payload area; For the first Security control mask for each internal node; For the first Security control mask for each internal node; This indicates a bitwise OR operation, used to form a union of permissions that covers the authorization requirements of all child nodes. The data processing server, based on the... The internal node and the first The spatial distribution boundary of each internal node generates the spatial distribution boundary of the parent node. The security control mask of the parent node is written into the internal node feature load area of ​​the parent node, and a mask-fixed marker is generated for the parent node. The parent node then proceeds to the next clustering level in the hierarchical spatial index structure.

[0050] When the envelope evaluation result is within the baseline state, the data processing server continues to read the high-order and middle-order segments of the security control masks of the internal nodes to be merged, and determines whether the dissimilarity of the permission masks is mutually exclusive based on the mask mapping rule table used in step S2. Mutually exclusive means that the security control masks of the internal nodes to be merged correspond to different access domains at the confidentiality level or industry layer category, and there are no common accessible valid bits in the mask mapping rule table. Specifically, when the high-order or middle-order segments of the two security control masks do not have common accessible valid bits in the mask mapping rule table, the data processing server determines that the dissimilarity of the permission masks is mutually exclusive. During the mutual exclusion determination, the data processing server performs a bitwise AND operation on the valid bits of the high-order and middle-order segments of the two security control masks respectively; when the result of the bitwise AND operation on the high-order segment or the middle-order segment is all zeros, it is determined that the corresponding segments do not have common accessible valid bits.

[0051] When the dissimilarity of permission masks is mutually exclusive, the data processing server blocks proximity clustering based on conventional spatial distribution boundaries and performs forced node splitting based on permission distribution boundaries. Permission distribution boundaries refer to the node affiliation boundaries defined by the confidentiality level bit segment and industry layer bit segment in the security control mask. The data processing server first assigns the internal nodes to be merged to different split branches according to the permission distribution boundaries. Then, within each split branch, it reads the spatial distribution boundary of the corresponding internal node and calculates the minimum bounding box for the spatial distribution boundaries within the same split branch. The resulting minimum bounding box serves as the spatial range of the security boundary box for that split branch. The data processing server performs a bitwise OR operation on the security control masks of each internal node within that split branch. The result serves as the security control mask attached to that security boundary box and is written into the feature load area of ​​the corresponding internal node. After the security boundary box is written into the hierarchical spatial index structure, it serves as the permission interception object during the spatial traversal in step S4.

[0052] When the envelope evaluation result is within the baseline state and the dissimilarity of the permission masks does not show a mutually exclusive situation, the data processing server performs proximity clustering according to the conventional spatial distribution boundary, and performs a bitwise OR operation on the security control masks of each child node participating in this proximity clustering to form the security control mask of the corresponding parent node. The data processing server writes the security control mask of the parent node into the internal node feature load area of ​​the parent node, and generates a mask solidification mark for the parent node, so that the parent node can be directly read in the subsequent clustering stage and spatial traversal exploration stage.

[0053] For example, in a mixed military-civilian sea area, nodes within the depth point cloud of a military waterway and nodes within the vector of a civilian navigational obstruction highly overlap on the spatial distribution boundary. The data processing server reads the security control mask from the feature load area of ​​both nodes, calculates the dissimilarity of the permission mask, and simultaneously calculates the spatial containment expansion ratio based on the spatial distribution boundary of both nodes. The resulting node merge evaluation vector is then input into the support vector data description model. If the envelope surface detachment judgment value exceeds the preset security baseline, the data processing server performs an upward permission compatibility merge, forming a parent node with union permissions. If the envelope surface detachment judgment value does not exceed the preset security baseline, but the security level bit segments or layer bit segments of the two nodes are mutually exclusive, the data processing server generates a security bounding box according to the permission distribution boundary, causing subsequent retrieval cursors to first perform permission determination at the security bounding box level.

[0054] S4: Parse the spatial retrieval command carrying the requester's attribute label. When exploring the hierarchical spatial index structure and encountering a security boundary box, obtain the logical bit mapping result between the requester's attribute label and the security control mask attached to the security boundary box, and implement traversal blocking control.

[0055] The specific implementation method of step S4 is as follows: The data processing server uses the spatial access command sent by the external business terminal as the starting point for processing. The spatial access command is a data retrieval command that carries the requester's attribute tags and the target spatial range for access. The requester's attribute tags are used to characterize the requester's security level authorization, layer access range, and time access constraints, while the target spatial range for access is used to define the spatial boundary of this waterway surveying results access.

[0056] After receiving the spatial access instruction, the data processing server parses the requester's attribute tags and calls the same mask mapping rule table as in step S2 to convert the requester's attribute tags into a requester authorization bit string. The requester authorization bit string is binary authorization data with the same bit width, bit order, and bit meaning as the security control mask, used to perform a bitwise AND operation with the security control mask attached to the security boundary frame. The requester's security level authorization is written to the authorization valid bit corresponding to the high-order bits of the security control mask, and the requester's layer access range is written to the authorization valid bit corresponding to the middle-order bits of the security control mask. The requester's time access constraints generate time access verification bits according to the same time valid bucket rule, deterministic hash function, or deterministic encoding rule used in step S2 for the low-order bits. If the requester's time access constraints cover multiple time valid buckets, the data processing server performs a bitwise OR operation on the low-order time access verification bits corresponding to the multiple time valid buckets to form the low-order authorization valid bits of the requester's authorization bit string.

[0057] The data processing server reads the target retrieval space range and determines its intersection with the spatial distribution boundary of the root node of the hierarchical spatial index structure. When an intersection exists, the data processing server generates a retrieval cursor and points it to the root node of the hierarchical spatial index structure. The retrieval cursor is a retrieval pointer that moves from top to bottom along the hierarchical spatial index structure, used to sequentially read the spatial distribution boundaries of internal nodes, the feature load areas of internal nodes, and the security control masks attached to the security bounding boxes.

[0058] The data processing server drives the retrieval cursor to probe downwards from the root node of the hierarchical spatial index structure. Each time the retrieval cursor reaches an internal node, the data processing server first reads the spatial distribution boundary of that internal node and determines its intersection with the target retrieval space. If there is no spatial intersection between the spatial distribution boundary of the current internal node and the target retrieval space, the data processing server discards the downward probe branch corresponding to that internal node and moves the retrieval cursor to the next candidate internal node at the same level; if there is a spatial intersection, the data processing server continues to read the internal node feature load area of ​​the current internal node to obtain the embedded security control mask.

[0059] When the retrieval cursor encounters the security bounding box generated in step S3, the data processing server identifies the security bounding box as the currently encountered object and reads the security control mask attached to the security bounding box. The security control mask attached to the security bounding box is derived from the result of performing a bitwise OR operation on the security control masks of each internal node within the split branch in step S3, and has already been written into the feature load area of ​​the corresponding internal node.

[0060] The data processing server performs a bitwise AND operation between the requester's authorization bit string and the security control mask attached to the currently encountered security boundary, generating a logical bit mapping result: , in, To retrieve the cursor encountered the first The logical bit mapping result generated when a safety bounding box is defined is used to determine whether the retrieval cursor is allowed to continue exploring downwards. This is the requester's authorization bit string, which is derived from the requester's attribute tags after being converted by the mask mapping rule table; For the first The security control mask attached to each security bounding box is derived from the internal node feature load area corresponding to that security bounding box. This indicates a bitwise AND operation.

[0061] Data processing servers with security control masks The valid authorization bit in the comparison is used as the benchmark to map the logical bits. Perform a bit-by-bit comparison. Unused bits in the security control mask that are not set to valid bits are not included in the logical bit mapping result determination. When exist All valid authorization bits are with When consistent, the logical bit mapping result is "authorized"; when... exist When all valid authorization bits are zero, the logical bit mapping result is no permission; when exist When a non-zero result exists on the valid authorization bits but does not cover all valid authorization bits, the logical bit mapping result is partial privilege escalation. Both lack of authorization and partial privilege escalation trigger traversal blocking control.

[0062] When the logical bit mapping result indicates permission is granted, the data processing server retains the downlink pointer of the current security bounding box and allows the retrieval cursor to continue entering the internal nodes or leaf nodes under that security bounding box. When the logical bit mapping result indicates no permission or partial unauthorized access, the data processing server implements traversal blocking control at the current internal node level. The execution path of traversal blocking control is as follows: the data processing server discards the retrieval pointer of the current security bounding box to its subordinate nodes, stops disk I / O read operations on the leaf nodes under that security bounding box, and marks the branch containing that security bounding box as a blocked branch. A blocked branch is a hierarchical spatial index structure branch where the retrieval cursor abandons downlinking because the logical bit mapping result does not meet the authorization requirements. The recorded content of the blocked branch is limited to the security bounding box identifier, blocking status, and logical bit mapping result; the spatial geometric coordinates of the leaf nodes under that security bounding box are not written.

[0063] When the logical bit mapping result indicates authorization and the retrieval cursor reaches a leaf node, the data processing server reads the channel mapping results corresponding to the leaf node and writes the read results into the spatial retrieval result set. The spatial retrieval result set is an output set consisting of channel mapping results that have been authorized and whose spatial distribution boundaries intersect with the target retrieval spatial range. For security boundary boxes marked as blocking branches, the data processing server only records their blocking status and does not load their subordinate high-precision raw spatial data.

[0064] For example, in an emergency search and rescue scenario after a typhoon, a maritime data retrieval terminal requests access to the vector data of navigational obstructions within a civilian waterway. The data processing server parses this spatial retrieval command, generates a requester authorization bit string, and drives the retrieval cursor into a hierarchical spatial index structure. When the retrieval cursor encounters a civilian navigational obstruction safety boundary, a bitwise AND operation is performed between the requester authorization bit string and the safety control mask attached to the safety boundary. The logical bit mapping result covers all valid authorization bits, and the retrieval cursor continues to probe down and read the corresponding navigational obstruction vector. When the retrieval cursor moves to the adjacent military waterway depth point cloud safety boundary, the bitwise AND result does not cover the valid authorization bits of the security level segment or layer segment. The data processing server discards the probe pointer at the internal node level of the safety boundary and does not read the multibeam depth point cloud belonging to that safety boundary. The final spatial retrieval result set only includes the authorized waterway mapping results.

[0065] Through steps S1 to S4, the data processing server completes the following steps after the access of the waterway mapping spatial geometry data stream: security feature extraction, security control mask generation, internal node permission solidification, anomaly defense processing based on the support vector envelope evaluation mechanism, and traversal blocking control during the spatial retrieval stage. Multidimensional security feature vectors, internal node feature load areas, security control masks, security bounding boxes, and logical bit mapping results are continuously transmitted within the same processing chain, enabling the review and hierarchical management of waterway mapping results for database entry within the hierarchical spatial index structure.

[0066] The above embodiments are merely preferred embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for reviewing and managing the access permissions of waterway surveying and mapping results for database entry, characterized in that, Including the following steps: S1: In response to the access of the spatial geometry data stream for waterway surveying, extract the business sensitivity parameters and spatial distribution boundaries of spatial geometric elements during spatial topology verification, and generate a multi-dimensional security feature vector. S2: Based on the multi-dimensional security feature vector, the business sensitivity parameter is mapped to the security control mask, and when constructing the hierarchical spatial index structure, it is solidified into the internal node feature load area of ​​the corresponding internal node; S3: In the internal node upward clustering stage, based on the security control mask and spatial distribution boundary, the dissimilarity of the permission mask and the spatial containment expansion ratio are calculated. After being judged by the support vector data description model, upward permission compatibility merging or generation of security bounding boxes are performed. In step S3, the data processing server forms a historical node merging evaluation vector with the internal node merging records in the historical steady-state channel model, and trains the support vector data description model to obtain the hypersphere center and the hypersphere radius as the preset security baseline. The spatial containment expansion ratio and the dissimilarity of the permission mask are input into the support vector data description model, and the envelope surface free judgment value is output. When the envelope surface free judgment value exceeds the preset safety baseline, the upward permission compatibility merging is performed; when it does not exceed the preset safety baseline, the permission mask dissimilarity is further judged to see if it is mutually exclusive. S4: Parse the spatial retrieval command carrying the requester's attribute label. When exploring the hierarchical spatial index structure and encountering a security boundary box, obtain the logical bit mapping result between the requester's attribute label and the security control mask attached to the security boundary box, and implement traversal blocking control.

2. The method for reviewing and managing the access permissions of waterway surveying results in the database according to claim 1, characterized in that, In step S1, the data processing server establishes an element processing sequence according to the access order of the waterway surveying spatial geometry data stream; maintains a one-to-one correspondence between the coordinate sequence, business attribute record, business sensitivity parameter and spatial distribution boundary of each spatial geometry element through the correspondence index; and encapsulates the business sensitivity parameter and spatial distribution boundary of the same spatial geometry element into a multi-dimensional security feature vector.

3. The method for reviewing and managing the access permissions for waterway surveying results as described in claim 2, characterized in that, In step S2, the data processing server calls the mask mapping rule table to convert the confidentiality level, industry layer category, and timeliness constraint scalar in the business sensitivity parameter into the high-segment encoding, middle-segment encoding, and low-segment timestamp check bit of the security control mask, respectively; and writes the security control mask into the internal node feature load area within the same internal node storage record as the internal node's spatial boundary description information.

4. The method for reviewing and managing the access permissions of waterway surveying results in the database according to claim 3, characterized in that, In step S2, the data processing server converts the timeliness constraint scalar into a time validity bucket according to a preset time granularity, and generates a low-segment timestamp verification bit based on the time validity bucket; in step S4, the data processing server generates a low-segment authorization validity bit of the requester's authorization bit string according to the same time validity bucket rule for the time access constraint in the requester's attribute tag.

5. The method for reviewing and managing the access permissions for waterway surveying results in the database according to claim 3, characterized in that, In step S3, when the data processing server calculates the spatial containment expansion ratio, it forms a relative difference ratio based on the difference between the minimum bounding volume of the merged bounding box and the sum of the volumes of its child nodes. When the relative difference ratio is negative, the negative value is retained as the input value of the support vector data description model. If there are multiple candidate internal nodes at the current clustering level, the multiple candidate internal nodes are divided into pairs of internal nodes to be merged, and envelope evaluation is performed on each pair of internal nodes to be merged.

6. The method for reviewing and managing the access permissions of waterway surveying results in the database according to claim 3, characterized in that, In step S3, when the dissimilarity of the permission mask is mutually exclusive, the data processing server allocates the internal nodes to be merged to different split branches according to the permission distribution boundary, and calculates the minimum bounding box for the spatial distribution boundary within each split branch to generate a safe bounding box. When the dissimilarity of the permission masks does not exhibit a mutually exclusive situation, the data processing server performs a bitwise OR operation on the security control masks of the child nodes participating in the nearest clustering to form the security control mask of the parent node.

7. The method for reviewing and managing the access permissions for waterway surveying results in the database according to claim 3, characterized in that, In step S4, the space retrieval command carries the requester's attribute tag and the target retrieval space range; The requester's attribute tags include security level authorization, layer access range, and time access constraints; The data processing server calls the same mask mapping rule table as in step S2 to convert the requester attribute tags into requester authorization bit strings that have the same bit width, bit order, and bit meaning as the security control mask.

8. The method for reviewing and managing the access permissions for waterway surveying results in the database according to claim 7, characterized in that, In step S4, when the logical bit mapping result is no permission or partial overreach, the data processing server discards the retrieval pointer of the currently encountered security boundary box pointing to the subordinate node and generates the record content of the blocking branch; the record content of the blocking branch is limited to the security boundary box identifier, blocking status and logical bit mapping result; when the logical bit mapping result is permission, the data processing server reads the waterway mapping results corresponding to the leaf node and writes them into the spatial retrieval result set.

9. The method for reviewing and managing the access permissions of waterway surveying results in the database according to claim 8, characterized in that, In step S4, when the data processing server outputs the spatial retrieval result set, it only writes the waterway mapping results that have been authorized and whose spatial distribution boundary intersects with the target retrieval spatial range; for the safety boundary box marked as a blocking branch, the data processing server retains the blocking status in the record content of the blocking branch and does not write the spatial geometric coordinates of the leaf nodes under the safety boundary box.

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