Global disaster risk dynamic assessment method oriented to vulnerability of disaster-affected body

Abstract

The present application relates to the technical field of engineering technology research and development, in particular to a global disaster risk dynamic assessment method for vulnerability of hazard-affected bodies, comprising the following steps: obtaining structure attribute, state, disaster loss, danger, exposure and source data, generating a hazard-affected body state sequence, correcting dynamic vulnerability representation value, generating a spatial vulnerability correction amount according to spatial correlation, calculating risk score and determining risk level in combination with a reliability correction coefficient, and updating the hazard-affected body state sequence or dynamic vulnerability representation value based on evaluation write-back data. In the present application, the hazard-affected body state change, historical disaster loss, spatial correlation, data reliability and evaluation write-back are included in the same calculation link, so that the vulnerability is converted from a static index to an evaluation basis that is dynamically corrected with time and space, the transmission ability of local anomalies to global risk calculation is improved, the influence of quality difference of multi-source data on the stability of risk level is reduced, and a traceable feedback correction closed loop is formed.

Description

Technical Field

[0001] This invention relates to the field of engineering technology research and development, and in particular to a dynamic assessment method for comprehensive disaster risk based on the vulnerability of disaster-bearing bodies. Background Technology

[0002] In the field of engineering technology research and development, data collection, transmission, calculation, storage, retrieval, and display are typically carried out around engineering objects, natural environments, spatial locations, and operational status. On-site monitoring instruments, remote sensing platforms, geographic information databases, business terminals, and servers transmit topography, meteorological and hydrological data, engineering facilities, population assets, and historical disaster records via network links and program instructions. Among these, the traditional dynamic disaster risk assessment method for the entire region, focusing on the vulnerability of disaster-bearing bodies, involves receiving disaster hazard data, exposure data, and structural attribute data of disaster-bearing bodies such as buildings, roads, pipelines, public facilities, and residential areas within a regional grid or administrative unit. Risk levels are then generated through location matching, time-series updates, indicator assignment, threshold comparison, and weight calculation. The server then stores the calculation records, zoning results, and layer information in a database or sends them to the terminal for display.

[0003] Existing technologies use grids or administrative units as assessment units. The vulnerability of disaster-bearing bodies mainly relies on structural ledgers and fixed weights in the calculation. Risk level updates are biased towards changes in disaster hazard and exposure. The operational status of disaster-bearing bodies and historical disaster damage are difficult to be included in the calculation process in a timely manner. The reliability differences of multi-source data are weakened. Local anomalies are easily ignored in independent unit calculations. On-site verification information remains at the level of display and feedback. Subsequent assessments lack continuous correction, resulting in a lag in the response of overall risk results to the actual vulnerability changes of disaster-bearing bodies. Abnormal fluctuations lack traceable judgment criteria. Summary of the Invention

[0004] The purpose of this invention is to provide a dynamic assessment method for the overall disaster risk based on the vulnerability of disaster-bearing bodies. This method enables the structural attributes, state changes, historical disaster damage, disaster hazard, exposure degree, and data source reliability of disaster-bearing bodies to be processed in the same assessment chain. Through state sequence, dynamic vulnerability correction, spatial correlation correction, reliability correction, and assessment write-back update, dynamic assessment of overall disaster risk and determination of risk level can be achieved.

[0005] To achieve the above objectives, this invention provides a dynamic assessment method for comprehensive disaster risk based on the vulnerability of disaster-bearing bodies, comprising the following steps: The system acquires structural attribute data, status records, historical disaster damage data, disaster hazard data, exposure data, and source information. This data can be obtained by collecting data from disaster-bearing body ledgers, remote sensing identification records, sensor monitoring records, disaster investigation records, and regional disaster observation records. The collected data items are then merging fields based on the disaster-bearing body identifier, collection time, and spatial coordinates. Simultaneously, the data item source, collection interface, collection batch, and update time are written into a source index table to generate a data package to be associated.

[0006] By associating the structural attribute data, the status records, and the historical disaster damage data with the disaster-bearing body identifier, location, and timestamp, a disaster-bearing body status sequence is generated. Further, the disaster-bearing body identifier, spatial coordinates, administrative division code, and timestamp are obtained from the structural attribute data, the status records, and the historical disaster damage data. A first association is established for data items with consistent disaster-bearing body identifiers; candidate associations are established for data items with missing disaster-bearing body identifiers but whose spatial coordinates are within a preset distance threshold. The data items corresponding to the first association and candidate associations are then sorted according to their timestamps to generate the disaster-bearing body status sequence.

[0007] Based on the damage level and anomaly markers of the disaster-bearing body's state sequence, the vulnerability characterization value is corrected to generate a dynamic vulnerability characterization value. Further, the structural type, damage level, maintenance status, and anomaly markers corresponding to each state node in the disaster-bearing body's state sequence are obtained. A baseline vulnerability characterization value is read from the vulnerability baseline table based on the structural type. The damage correction range is determined based on the damage level and maintenance status. The anomaly confidence status is determined based on the anomaly markers. The baseline vulnerability characterization value is then corrected based on the damage correction range and the anomaly confidence status to generate a dynamic vulnerability characterization value.

[0008] Based on the spatial relationships determined by regional grids or infrastructure links, a spatial vulnerability correction amount is generated for the spatial relationships of disaster-bearing bodies with changing states. The dynamic vulnerability characterization value is then adjusted based on this spatial vulnerability correction amount. Further, the disaster-bearing body identifier, spatial coordinates, regional grid number, link node number, and state change direction of the disaster-bearing body with changing states are obtained. Associated disaster-bearing bodies are determined based on regional grid adjacency relationships or infrastructure link connectivity relationships. The association weight and location distance between the associated disaster-bearing body and the disaster-bearing body with changing states are read. A spatial vulnerability correction amount is generated based on the association weight, location distance, and state change direction. This spatial vulnerability correction amount is then superimposed on the dynamic vulnerability characterization value to generate the adjusted vulnerability characterization value.

[0009] As a further constraint on the process of determining spatial association, the adjacent grid numbers of the grid in the area where the disaster-bearing body with the state change is located, as well as the upstream, downstream and peer nodes of the infrastructure link, are obtained. Disaster-bearing bodies located in adjacent grids and with the same disaster-bearing body type are identified as grid-associated disaster-bearing bodies, and disaster-bearing bodies located within the service range of the upstream, downstream or peer nodes are identified as link-associated disaster-bearing bodies, thus generating spatial association.

[0010] A credibility correction coefficient is generated based on the source information, time interval, and location deviation. A risk score is calculated by combining the disaster hazard data, exposure data, dynamic vulnerability characterization value, and credibility correction coefficient. The risk level is then determined based on the risk score. Further, the credible source marker, collection time, disaster-bearing body spatial coordinates, and data provider identifier are obtained from the source information. The time-effect decay state is determined based on the time interval between the collection time and the current assessment time. The spatial deviation state is determined based on the location deviation between the disaster-bearing body spatial coordinates and the disaster hazard data sampling location. The credible source marker, time-effect decay state, and spatial deviation state are then combined to generate a credibility correction coefficient. A single disaster-bearing body risk score is calculated by combining the disaster hazard data, exposure data, dynamic vulnerability characterization value, and credibility correction coefficient. The risk scores of single disaster-bearing bodies within the same regional grid or the same infrastructure link are combined according to the importance weight of the disaster-bearing body to generate a regional link risk score. The regional link risk score is then compared with a preset risk level range to generate a risk level.

[0011] As a further limitation on the process of generating the credibility correction coefficient, the credible source marker and the source marker to be verified are read from the source credibility record. The data item corresponding to the credible source marker is determined as the regular participating data item, and the data item corresponding to the source marker to be verified is determined as the deweighted participating data item. When the time interval exceeds the preset timeliness threshold or the position deviation exceeds the preset spatial threshold, the participation weight of the corresponding data item is reduced and adjusted. The participation weight of the regular participating data item, the participation weight of the deweighted participating data item, the timeliness decay state corresponding to the time interval, and the spatial deviation state corresponding to the position deviation are merged to generate the credibility correction coefficient.

[0012] The system acquires assessment write-back data generated from terminal verification results or disaster records. Based on the comparison results between the assessment write-back data and the assessment records for the risk level, it updates the disaster-bearing body status sequence or the dynamic vulnerability characterization value. Further, it acquires the disaster-bearing body identifier, verification location, verification time, measured damage level, and handling status from the terminal verification results or disaster records. It matches the assessment records corresponding to the risk level according to the disaster-bearing body identifier, verification location, and verification time, and generates an assessment record comparison result based on the difference between the measured damage level and the predicted damage status in the assessment record. When the assessment record comparison result indicates a missing or delayed status, it writes the measured damage level and handling status into the disaster-bearing body status sequence. When the assessment record comparison result indicates a vulnerability deviation, it corrects the dynamic vulnerability characterization value based on the measured damage level and generates an assessment write-back update record.

[0013] As a further limitation on the process of generating the comparison results of assessment records, the risk level, predicted damage status, assessment time window, and spatial coordinates of the disaster-bearing body in the assessment records are read. Assessment records whose review time falls within the assessment time window and whose positional deviation between the review location and the spatial coordinates of the disaster-bearing body is less than a preset write-back distance threshold are identified as comparable assessment records. The measured damage level is compared with the predicted damage status in the comparable assessment records. Status missing markers, status lag markers, or vulnerability deviation markers are determined according to the direction of the level difference, the magnitude of the level difference, and the treatment status, thereby generating the assessment record comparison results.

[0014] The beneficial effects of the technical solution provided by the embodiments of the present invention include at least the following: establishing a state sequence through disaster-bearing body identification, location and timestamp; incorporating structural attributes, operational status and historical disaster damage into the same vulnerability update link; dynamic vulnerability characterization values ​​changing with damage level and anomaly markers and undergoing spatial correlation correction; the credibility correction coefficient converting source information, time interval and location deviation into risk calculation weights; and updating the state sequence or dynamic vulnerability characterization values ​​in reverse after comparing the evaluation write-back data with the risk level record. Risk assessment is transformed from static assignment to traceable closed-loop calculation, and the overall risk results can synchronously reflect changes in the disaster-bearing body status, data quality differences, sources of abnormal fluctuations and differences in on-site verification. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying 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.

[0016] Figure 1 This is a schematic diagram of the main process for dynamic assessment of the vulnerability of disaster-bearing bodies according to the present invention; Figure 2 This is a schematic diagram illustrating the generation of a disaster-bearing body state sequence through multi-source data aggregation according to the present invention. Figure 3 This is a schematic diagram illustrating the correction of dynamic vulnerability characterization values ​​in this invention; Figure 4 This is a schematic diagram illustrating the spatial correlation and vulnerability correction of the present invention; Figure 5 This is a schematic diagram illustrating the credibility correction and risk level generation of the present invention; Figure 6 This is a schematic diagram illustrating the evaluation of write-back updates in this invention. Detailed Implementation

[0017] The following description, in conjunction with the technical solutions, scope of protection text, and accompanying drawings, illustrates the possible implementation methods of this invention. The following content explains the execution process of the dynamic disaster risk assessment method for the vulnerability of disaster-bearing bodies. The disaster-bearing body can be a building, structure, facility node, object in an infrastructure link, or other object determined within the scope of the original disclosure materials that are included in the disaster risk assessment. Structural attribute data, status records, historical disaster damage data, disaster hazard data, exposure data, and source information are respectively used as input data items entering the assessment process. The disaster-bearing body status sequence, dynamic vulnerability characterization value, spatial vulnerability correction amount, credibility correction coefficient, risk score, risk level, and assessment write-back update record are used as intermediate data or output data generated at different processing stages of this method. The thresholds, benchmark tables, weights, level intervals, and source credibility records in the following implementation process are based on the configurations supported by the technical solutions described in the application documents, and no specific numerical limits are imposed unless specific values ​​are given.

[0018] Please see Figures 1 to 6 This embodiment provides a method for dynamic assessment of overall disaster risk based on the vulnerability of disaster-bearing bodies. This embodiment starts with multi-source data related to disaster-bearing bodies entering the assessment process as input. A state sequence is established using disaster-bearing body identifiers, locations, and timestamps. Vulnerability and risk scores are then processed based on damage levels, anomaly markers, spatial correlations, and reliability correction relationships. Finally, assessment write-back data is generated based on terminal verification results or disaster records, and the disaster-bearing body state sequence or dynamic vulnerability characterization values ​​are updated. This method includes six main steps, S1 to S6.

[0019] S1, acquires structural attribute data, status records, historical disaster damage data, disaster risk data, exposure data, and source information.

[0020] The input sources for S1 include disaster-bearing body ledgers, remote sensing identification records, sensor monitoring records, disaster investigation records, and regional disaster observation records. The disaster-bearing body ledger provides structural attribute data such as disaster-bearing body identification, structure type, spatial coordinates, and administrative division code; remote sensing identification records and sensor monitoring records provide disaster-bearing body status records; disaster investigation records provide historical disaster damage data and verification content for subsequent assessment write-back data; and regional disaster observation records provide disaster hazard data. Exposure data characterizes the exposure status of disaster-bearing bodies or their surrounding grid and infrastructure links in risk assessment. Source information records the data item source, acquisition interface, acquisition batch, update time, data provider identifier, trusted source marker, or source marker pending verification.

[0021] S111 involves collecting disaster-bearing body ledgers, remote sensing identification records, sensor monitoring records, disaster investigation records, and regional disaster observation records. The collected data items are then processed by merging fields according to the disaster-bearing body identifier, collection time, and spatial coordinates. This field merging process applies to multi-source data items from the same disaster-bearing body or suspected same disaster-bearing body. The processed data results in a data package to be associated, containing structural attribute data, status records, historical disaster damage data, disaster hazard data, exposure data, and source information.

[0022] S112, Write the data packets to be associated into the source index table. The source index table records at least the source of the data item, the collection interface, the collection batch, and the update time, and maintains association with the corresponding data item. For data items with a trusted source marker in the source information, retain their normal participation status; for data items with a source marker pending verification in the source information, retain their pending verification participation status, so that they can be called when generating the subsequent credibility correction coefficient.

[0023] S113: Availability identification is performed on the data items entering S1. Data items with complete disaster-bearing body identifier, collection time, spatial coordinates, and data source fields proceed to subsequent association processing; data items with missing disaster-bearing body identifiers but available spatial coordinates and timestamps are retained as candidate association data items; data items with missing collection time, unidentifiable spatial coordinates, data formats that do not conform to the field structure of the data package to be associated, or whose source indexes do not correspond, are not directly included in the risk scoring calculation. This process results in three states: data items that can participate in association, candidate association data items, and data items that do not directly participate. Subsequent steps only call data items that can participate in association and data items that meet the candidate association conditions.

[0024] The data packets to be associated output by S1 are received by S2, and the source index table is received by S5. The data items that can be associated and the candidate data items together constitute the input range for generating the disaster-bearing body status sequence. The execution result of S1 can be verified by the correspondence between the disaster-bearing body identifier, collection time, spatial coordinates in the data packets to be associated, and the data item source, collection interface, collection batch, and update time in the source index table.

[0025] S2 generates a disaster-bearing body status sequence by associating structural attribute data, status records, and historical disaster damage data with the disaster-bearing body identifier, location, and timestamp.

[0026] S2 takes the data packet to be associated generated by S1 as input and processes structural attribute data, status records, and disaster-bearing body identifiers, spatial coordinates, administrative division codes, and timestamps from historical disaster damage data. The purpose of S2 is to group scattered data items into the time series of the same disaster-bearing body, so that subsequent damage level identification, anomaly marking processing, and vulnerability correction have a traceable data foundation.

[0027] S211, establish a first association relationship for data items with consistent disaster-bearing body identifiers. The starting data item of the first association relationship is the disaster-bearing body identifier in the structural attribute data, and the ending data item is the same disaster-bearing body identifier in the status record or historical disaster damage data. After establishing the first association relationship, the corresponding structural attribute data, status record, and historical disaster damage data are grouped into the same disaster-bearing body's status sequence candidate set.

[0028] S212, establish candidate associations for data items lacking disaster-bearing body identifiers. The trigger condition for a candidate association is that the data item lacks a disaster-bearing body identifier, but its spatial coordinates are within a preset distance threshold. The candidate association processing uses spatial coordinates and administrative division codes as the location basis, searches for corresponding disaster-bearing bodies within the preset distance threshold range, and marks the matched data items as candidate association data items. If the spatial coordinates exceed the preset distance threshold, or if multiple disaster-bearing bodies meet the location conditions and cannot be further distinguished based on administrative division codes and timestamps, then the data item is not directly incorporated into the disaster-bearing body status sequence, but is retained as a pending review status.

[0029] S213, sort the data items corresponding to the first association and candidate associations according to their timestamps. The sorting process uses the disaster-bearing entity identifier or candidate association object as the group boundary and the timestamp as the basis for the order of state nodes. Data items with consistent timestamps maintain a traceable order according to the update time or collection batch in the source index table; data items with missing timestamps are not included in the state sequence as determined state nodes.

[0030] S214 generates a disaster-bearing body status sequence. Each status node in the disaster-bearing body status sequence corresponds to at least one structural attribute data, status record, or historical disaster damage data source, and retains data items needed for subsequent processing, such as disaster-bearing body identifier, spatial coordinates, administrative division code, timestamp, damage level, maintenance status, and anomaly markers. After the disaster-bearing body status sequence is formed, S3 reads the structural type, damage level, maintenance status, and anomaly markers, and S6 updates them during the assessment write-back.

[0031] The disaster-bearing entity status sequence output by S2 can be verified through the consistency of the disaster-bearing entity identifier of the status nodes, the candidate association location conditions, the timestamp sorting results, and the correspondence with the source index. For candidate association data items, their candidate attributes are retained in the subsequent credibility correction process, and the candidate status is not rewritten as a trusted source status.

[0032] S3, based on the damage level and anomaly markers of the disaster-bearing body state sequence, corrects the vulnerability characterization value and generates dynamic vulnerability characterization value.

[0033] S3 takes the disaster-bearing body state sequence generated by S2 as input and processes the structural type, damage level, maintenance status, and anomaly markers corresponding to each state node. S3 reads the baseline vulnerability characterization value from the vulnerability benchmark table through the structural type, determines the damage correction range through the damage level and maintenance status, and determines the anomaly confidence status through the anomaly marker, thereby forming a dynamic vulnerability characterization value.

[0034] S311: Read the available status nodes within the current assessment time window from the disaster-bearing body's status sequence. If multiple status nodes exist for the same disaster-bearing body, determine the current node and historical nodes according to the timestamp order, and retain historical damage status for damage change identification. If the current node lacks a damage level but has historical disaster damage data, the node can be marked as pending status supplementation, without directly forming a definite damage correction range.

[0035] S312, retrieve the baseline vulnerability characterization value from the vulnerability benchmark table based on the structure type. The structure type is derived from the structural attribute data, and the vulnerability benchmark table is used to map the structure type to the baseline vulnerability characterization value. If the structure type cannot be matched with a corresponding item in the vulnerability benchmark table, the affected body will not enter the current round of deterministic vulnerability correction, and a benchmark matching failure status will be output to avoid vulnerability characterization values ​​without a source from entering the risk score.

[0036] S313. Determine the damage correction magnitude based on the damage level and maintenance status. The damage level is derived from the status records in the disaster-bearing body's status sequence or historical disaster damage data. The maintenance status indicates whether the damage status has been addressed. When the damage level indicates an increase in damage severity, the damage correction magnitude is adjusted according to the direction of increased vulnerability; when the maintenance status indicates that the damage has been addressed, the damage correction magnitude is adjusted according to the corresponding maintenance status. If there is a timestamp inconsistency between the damage level and maintenance status, the damage level and maintenance status that can correspond within the same status node are considered valid combinations; combinations that cannot correspond are retained as objects for handling abnormal and reliable states.

[0037] S314. Determine the credible state of an anomaly based on anomaly markers. Anomaly markers are derived from records in the disaster-bearing body state sequence or source index table for states such as data anomalies, sources pending verification, candidate associations, timestamp misalignment, and spatial coordinate deviation. The credible state of an anomaly is used to limit the way that state node participates in vulnerability correction; it does not directly equate anomaly markers with damage states, nor does it generate risk levels solely based on anomaly markers.

[0038] S315 modifies the baseline vulnerability characterization value based on the damage correction magnitude and the abnormal confidence state, generating a dynamic vulnerability characterization value. The inputs to the dynamic vulnerability characterization value are the baseline vulnerability characterization value, the damage correction magnitude, and the abnormal confidence state; the output is the current vulnerability characterization result of the disaster-bearing body, which can be invoked by S4 and S5. For data items whose abnormal confidence states have not been resolved, the dynamic vulnerability characterization value retains the corresponding abnormal participation state for weight reduction or decrement processing when generating the confidence correction coefficient in S5.

[0039] The dynamic vulnerability characterization value output by S3 can be verified through the correspondence between structural type and vulnerability benchmark table, the damage correction range formed by damage level and maintenance status, the anomalous credibility status corresponding to anomaly markers, and the disaster-bearing body identifier. This output is received by S4 for further spatial correlation correction, and also received by S5 for participation in risk scoring calculation.

[0040] S4 determines spatial relationships based on regional grids or infrastructure links, generates spatial vulnerability correction quantities for the spatial relationships of disaster-bearing bodies with changing states, and adjusts dynamic vulnerability characterization values ​​based on spatial vulnerability correction quantities.

[0041] S4 takes the dynamic vulnerability characterization value generated by S3 and the disaster-bearing body state sequence in S2 as inputs. The trigger condition is the presence of a disaster-bearing body with a changed state in the disaster-bearing body state sequence. A disaster-bearing body with a changed state refers to a disaster-bearing body whose damage level, maintenance status, or other state change direction has changed, as displayed by the state node. S4 determines the associated disaster-bearing bodies through regional grid adjacency relationships or infrastructure link connectivity relationships, and generates spatial vulnerability correction amounts based on association weights, location distances, and state change direction.

[0042] S411: Obtain the disaster-bearing body identifier, spatial coordinates, regional grid number, link node number, and state change direction of the disaster-bearing body undergoing state change. The state change direction is determined by the state differences between adjacent state nodes in the disaster-bearing body's state sequence, and its function is to limit the adjustment direction of the spatial vulnerability correction amount on the vulnerability characterization of the associated disaster-bearing body.

[0043] S412, determine grid-associated disaster-bearing bodies based on regional grid adjacency relationships. This process first reads the adjacent grid numbers of the regional grid where the state-changed disaster-bearing body is located, and then identifies disaster-bearing bodies located within adjacent grids and of the same disaster-bearing body type as grid-associated disaster-bearing bodies. The data connection objects between grid-associated disaster-bearing bodies and state-changed disaster-bearing bodies include regional grid number, adjacent grid number, disaster-bearing body type, and spatial coordinates.

[0044] S413, determine the link-associated disaster-bearing entities based on infrastructure link connectivity. This process reads the upstream, downstream, and peer nodes of the infrastructure link where the status-changed disaster-bearing entity is located, and identifies disaster-bearing entities within the service range of the upstream, downstream, or peer nodes as link-associated disaster-bearing entities. The data connection objects between link-associated disaster-bearing entities and status-changed disaster-bearing entities include the link node number, upstream node, downstream node, peer node, and corresponding service range.

[0045] S414, Generate spatial association relationships. Spatial association relationships include grid association relationships between state-changed disaster-bearing bodies and grid-associated disaster-bearing bodies, and link association relationships between state-changed disaster-bearing bodies and link-associated disaster-bearing bodies. For the same associated disaster-bearing body that satisfies both grid association and link association, its association source is retained, so that subsequent association weights and location distances can correspond to a clear spatial association type.

[0046] S415: Read the association weight and location distance between the associated disaster-bearing body and the disaster-bearing body undergoing state change, and generate a spatial vulnerability correction amount based on the association weight, location distance, and direction of state change. The association weight is used to characterize the degree of influence between the associated disaster-bearing body and the disaster-bearing body undergoing state change in the spatial relationship; the location distance is used to characterize the spatial proximity between the two; and the direction of state change is used to limit the adjustment direction of the spatial vulnerability correction amount. If the association weight is missing, the location distance is unavailable, or the direction of state change is unclear, then the associated disaster-bearing body will not be included in the generation of the spatial vulnerability correction amount in this round.

[0047] S416 adds the spatial vulnerability correction to the dynamic vulnerability characterization value to generate the adjusted vulnerability characterization value. The inputs to this overlay process are the dynamic vulnerability characterization value output from S3 and the spatial vulnerability correction from S415, and the output is the adjusted vulnerability characterization value. The adjusted vulnerability characterization value is used as a vulnerability data item in S5 for risk scoring calculation.

[0048] The spatial correlations and adjusted vulnerability characterization values ​​output by S4 can be verified through regional grid adjacency relationships, infrastructure link connectivity relationships, correlation weights, location distances, and state change directions. The determined paths of grid-associated disaster-bearing bodies and link-associated disaster-bearing bodies are retained separately to avoid mixing different spatial correlation sources.

[0049] S5 generates a confidence correction coefficient based on source information, time interval, and location deviation. It then calculates a risk score by combining disaster risk data, exposure data, dynamic vulnerability characterization values, and the confidence correction coefficient, and determines the risk level based on the risk score.

[0050] S5 takes the source information, hazard hazard data, and exposure data from S1, the dynamic vulnerability characterization value from S3, and the adjusted vulnerability characterization value from S4 as input. If S4 has already generated an adjusted vulnerability characterization value, the risk score calculation uses this value; if a disaster-bearing body does not trigger spatial correlation correction, the risk score calculation uses the dynamic vulnerability characterization value generated by S3. S5 generates a reliability correction coefficient using trusted source markers, collection time, disaster-bearing body spatial coordinates, data provider identifier, time interval, and location deviation, then calculates the risk score for a single disaster-bearing body and the regional link risk score, and compares them with a preset risk level range to generate a risk level.

[0051] S511: Obtain the trusted source marker, collection time, spatial coordinates of the disaster-bearing body, and data provider identifier from the source information. The trusted source marker and the source marker to be verified are read from the trusted source record and matched with the source index table in S1. The data item corresponding to the trusted source marker is determined as a regular participating data item, and the data item corresponding to the source marker to be verified is determined as a de-weighted participating data item.

[0052] S512 determines the timeliness decay status based on the time interval between the data acquisition time and the current evaluation time. The time interval is determined by the chronological relationship between the data acquisition time and the current evaluation time. When the time interval exceeds a preset timeliness threshold, the participation weight of the corresponding data item is reduced; data items that do not exceed the preset timeliness threshold maintain their corresponding timeliness participation status. Data items with missing acquisition times or unrecognizable formats do not directly form a determined timeliness decay status, but enter a pending review participation status.

[0053] S513 determines the spatial deviation state based on the positional deviation between the spatial coordinates of the disaster-bearing body and the sampling location of the disaster hazard data. The positional deviation is determined by the spatial correspondence between the spatial coordinates of the disaster-bearing body and the sampling location of the disaster hazard data. When the positional deviation exceeds a preset spatial threshold, the participation weight of the corresponding data item is reduced; data items that do not exceed the preset spatial threshold maintain their corresponding spatial participation state. Data items with missing disaster hazard data sampling locations or unavailable spatial coordinates of the disaster-bearing body do not directly form a determined spatial deviation state.

[0054] S514 merges the participation weights of regular participating data items, the participation weights of de-weighted participating data items, the time-related decay status corresponding to the time interval, and the spatial deviation status corresponding to the location deviation to generate a credibility correction coefficient. The inputs to the credibility correction coefficient include the source credibility status, the time-related decay status, and the spatial deviation status; the output is the corrected data item used in the risk scoring calculation. If the same data item simultaneously has a source mark pending verification, exceeds a preset time threshold, and exceeds a preset spatial threshold, the source of each status is retained, allowing the credibility correction coefficient to be traced back to the three correction dimensions of source, time, and location.

[0055] S515 calculates the risk score for a single disaster-bearing entity by combining disaster hazard data, exposure data, dynamic vulnerability characterization values, and reliability correction coefficients. The risk score uses disaster hazard data as the input for disaster intensity or hazard level, exposure data as the input for the exposure status of the disaster-bearing entity, dynamic vulnerability characterization values ​​or adjusted vulnerability characterization values ​​as the vulnerability input, and the reliability correction coefficient as the input for the data's reliable participation status. This process outputs the risk score for the single disaster-bearing entity and associates it with the disaster-bearing entity identifier, regional grid number, or infrastructure link number for storage.

[0056] S516 merges the risk scores of individual disaster-bearing entities within the same regional grid or infrastructure link according to their importance weights to generate a regional link risk score. The importance weights of disaster-bearing entities characterize the degree of participation of different disaster-bearing entities in the risk merging of regional grids or infrastructure links. If multiple disaster-bearing entities exist within the same regional grid, the regional grid number is used as the merging boundary; if multiple disaster-bearing entities exist within the same infrastructure link, the infrastructure link number or link node number is used as the merging boundary. Data items lacking disaster-bearing entity importance weights do not change the merging boundaries of other available data items and are retained in a weight-pending-review state.

[0057] S517 compares the regional link risk score with the preset risk level range to generate a risk level.

[0058] Multiple risk levels are pre-set, arranged from low to high risk, along with corresponding risk score ranges for each level. Except for the risk score range corresponding to the highest risk level, each risk score range includes its lower limit but excludes its upper limit; the risk score range corresponding to the highest risk level includes both its lower and upper limits.

[0059] When the regional link risk score is greater than or equal to the lower limit of the risk score interval corresponding to a risk level, and less than the upper limit of the risk score interval, the risk level corresponding to the regional link risk score is determined as that risk level; when the regional link risk score is greater than or equal to the lower limit of the risk score interval corresponding to the highest risk level, and less than or equal to the upper limit of the risk score interval corresponding to the highest risk level, the risk level corresponding to the regional link risk score is determined as the highest risk level.

[0060] When the risk score of a regional link is less than the lower limit of the risk score range corresponding to the lowest risk level, or greater than the upper limit of the risk score range corresponding to the highest risk level, a risk score out-of-bounds flag is generated, and no definite risk level is output.

[0061] The risk level output by S5 can be verified through the correspondence between the confidence correction coefficient, the risk score of a single disaster-bearing body, the risk score of a regional link, and the preset risk level range. The risk level is stored together with assessment records such as the assessment time window, the spatial coordinates of the disaster-bearing body, and the predicted damage status for comparison during the S6 assessment write-back.

[0062] S6: Obtain the assessment write-back data formed by the terminal review results or disaster records, and update the disaster-bearing body status sequence or dynamic vulnerability characterization value based on the comparison results between the assessment write-back data and the risk level assessment records.

[0063] S6 takes the terminal verification results or disaster records as input to generate assessment write-back data. The assessment write-back data includes the disaster-bearing body identification, verification location, verification time, measured damage level, and treatment status. S6 compares the assessment write-back data with the risk level corresponding assessment records generated by S5, and updates the disaster-bearing body status sequence or dynamic vulnerability characterization value based on the comparison results.

[0064] S611: Obtain the disaster-bearing body identifier, verification location, verification time, measured damage level, and handling status from the terminal verification results or disaster records. The disaster-bearing body identifier is used to match the assessment record; the verification location is used to determine the positional deviation with the spatial coordinates of the disaster-bearing body; the verification time is used to correspond with the assessment time window; the measured damage level is used to compare the level difference with the predicted damage status; and the handling status is used to determine the status missing marker, status lag marker, or vulnerability deviation marker.

[0065] S612 reads the risk level, predicted damage state, assessment time window, and spatial coordinates of the affected body from the assessment record. The assessment record originates from the risk level and its corresponding record output by S5. If the review time falls within the assessment time window, and the positional deviation between the review location and the spatial coordinates of the affected body is less than a preset write-back distance threshold, the assessment record is identified as a comparable assessment record. If the review time is outside the assessment time window or the positional deviation is not less than the preset write-back distance threshold, the corresponding assessment record is not considered a comparable assessment record for this round.

[0066] S613 compares the measured damage level with the predicted damage state in comparable assessment records. The inputs to the level difference comparison are the measured damage level and the predicted damage state, and the outputs are the direction and magnitude of the level difference. The direction of the level difference characterizes the direction of deviation of the measured damage level from the predicted damage state, and the magnitude of the level difference characterizes the degree of difference between the two. If no comparable assessment record exists, an incomparable state is generated, and the dynamic vulnerability characterization value is not updated.

[0067] S614, determine the status missing marker, status lag marker, or vulnerability deviation marker according to the direction of the level difference, the magnitude of the level difference, and the handling status. When the assessment record comparison result indicates a status missing or status lag, it means that the disaster-bearing body status sequence has not reflected the measured damage level or handling status in a timely manner; when the assessment record comparison result indicates a vulnerability deviation, it means that the dynamic vulnerability characterization value needs to be corrected based on the measured damage level.

[0068] S615, when the assessment record comparison results indicate a missing or outdated status, the measured damage level and treatment status are written into the disaster-bearing body status sequence. The writing process uses the disaster-bearing body identifier, review location, and review time as matching conditions to create new status nodes or update corresponding status nodes in the disaster-bearing body status sequence, while retaining the source of the assessment write-back data. The written disaster-bearing body status sequence serves as the input for S3 in subsequent assessment cycles.

[0069] S616: When the assessment record comparison results indicate a vulnerability deviation, the dynamic vulnerability characterization value is corrected based on the measured damage level, and an assessment write-back update record is generated. The assessment write-back update record includes the disaster-affected entity identifier, review time, measured damage level, treatment status, assessment record comparison results, and the corrected dynamic vulnerability characterization value. This record serves as a traceable input for vulnerability correction and risk score calculation in subsequent assessment cycles.

[0070] The updated results of the disaster-bearing body status sequence, the corrected results of the dynamic vulnerability characterization values, and the assessment write-back update records output by S6 can be verified through the correspondence between the disaster-bearing body identifier, the review location, the review time, the assessment time window, the spatial coordinates of the disaster-bearing body, the measured damage level, and the predicted damage status. Through S6, the terminal review results or disaster records do not directly replace the risk level; instead, they first form an assessment record comparison result, and then enter the corresponding update path based on different markers indicating missing status, delayed status, or vulnerability deviation.

[0071] During the execution of S1 to S6 above, the data flow relationship is as follows: structural attribute data, status records, historical disaster damage data, disaster hazard data, exposure data, and source information first enter the data packet to be associated; the structural attribute data, status records, and historical disaster damage data in the data packet to be associated are associated through the disaster-bearing body identifier, location, and timestamp to form a disaster-bearing body status sequence; the damage level, maintenance status, and anomaly markers in the disaster-bearing body status sequence participate in the generation of dynamic vulnerability characterization values; the regional grid number, link node number, and status change direction of the disaster-bearing body with status changes participate in the generation of spatial association relationships and form a spatial vulnerability correction amount; source information, time interval, and location deviation form a reliability correction coefficient; disaster hazard data, exposure data, dynamic vulnerability characterization values, and reliability correction coefficients form a risk score and risk level; the terminal verification result or disaster record forms assessment write-back data, and the assessment write-back data and the assessment record corresponding to the risk level form an assessment record comparison result, and the disaster-bearing body status sequence or dynamic vulnerability characterization value is updated in reverse.

[0072] For abnormal states such as missing data, format errors, misaligned timestamps, unidentifiable spatial coordinates, missing disaster-bearing body identifiers, mismatched source indexes, failed matching of baseline vulnerability characterization values, missing association weights, unavailable location distances, unclear direction of state change, missing credible source markers, risk scores failing to fall within the preset risk level range, and review records failing to match the assessment time window, this method does not directly generate deterministic output based on the abnormal data. Instead, it forms intermediate states such as candidate association, pending review, failed matching, uncomparable, or not directly involved. These intermediate states serve as the basis for subsequent review, reduced weighting, or write-back updates, without altering the correspondence between the disaster-bearing body state sequence, dynamic vulnerability characterization values, and risk levels already formed by available data.

[0073] The specific structures, processes, field states, processing order, judgment conditions, parameter sources, and data flow relationships described above are used to illustrate the possible implementations of the present invention and should not be construed as limiting the present invention to the specific implementation processes listed. Without departing from the technical solutions described in this invention and the scope supported by the original disclosure, any equivalent substitutions, equivalent modifications, equivalent combinations, order adjustments, equivalent changes in field names, equivalent succession of the executing entity, or equivalent changes in the carrier form that can be conceived by those skilled in the art should fall within the scope of protection of this patent; however, they should not be extended to unclaimed topics, nor should the substantive correspondence of the technical objects be altered through name changes.

Claims

1. A dynamic disaster risk assessment method for the entire region based on the vulnerability of disaster-bearing bodies, characterized in that, The method includes: Acquire structural attribute data, status records, and historical disaster damage data, including disaster-bearing body identifiers, spatial coordinates, and timestamps, as well as disaster risk data, exposure data, and source information. The structural attribute data also includes administrative division codes. By associating the structural attribute data, the status record, and the historical disaster damage data with the disaster-bearing body identifier, spatial coordinates, and timestamp, a disaster-bearing body status sequence is generated. A baseline vulnerability characterization value is obtained based on the structural attribute data, and the baseline vulnerability characterization value is corrected based on the damage level and anomaly markers contained in the disaster-bearing body state sequence to generate a dynamic vulnerability characterization value. Based on the regional grid or infrastructure links, spatial relationships are determined, spatial vulnerability correction amounts are generated for the spatial relationships of the disaster-bearing bodies whose states have changed, and the dynamic vulnerability characterization values ​​are adjusted based on the spatial vulnerability correction amounts. Based on the source information, the time interval between the collection time of each data item and the current assessment time is determined, and the spatial position deviation is determined based on the spatial coordinates of the disaster-bearing body and the sampling location of the disaster hazard data; a confidence correction coefficient is generated based on the source information, the time interval, and the spatial position deviation; a risk score is calculated by combining the disaster hazard data, the exposure data, the dynamic vulnerability characterization value, and the confidence correction coefficient; and the risk level is determined based on the risk score. The system acquires assessment write-back data generated from terminal verification results or disaster records. Based on the comparison results between the assessment write-back data and the assessment records of the risk level, it updates the disaster-bearing body status sequence or the dynamic vulnerability characterization value.

2. The method for dynamic assessment of global disaster risk based on the vulnerability of disaster-bearing bodies according to claim 1, characterized in that, The steps for obtaining the structural attribute data, the status record, the historical disaster damage data, the disaster hazard data, the exposure data, and the source information include: Collect disaster-bearing body ledgers, remote sensing identification records, sensor monitoring records, disaster investigation records, and regional disaster observation records; extract structural attribute data, including disaster-bearing body identifier, structure type, spatial coordinates, administrative division code, and disaster-bearing body importance weight, as well as exposure data from the disaster-bearing body ledgers; extract status records from the remote sensing identification records and sensor monitoring records; extract historical disaster damage data from the disaster investigation records; and extract disaster hazard data from the regional disaster observation records. Write the data source, collection interface, collection batch, and update time of each data item as source information into a source index table; merge the extracted data items according to the disaster-bearing body identifier, collection time, and spatial coordinates to generate a data package to be associated.

3. The method for dynamic assessment of global disaster risk based on the vulnerability of disaster-bearing bodies according to claim 1, characterized in that, The steps for generating the disaster-bearing body state sequence include: Obtain the disaster-bearing body identifier, spatial coordinates, and administrative division code from the structural attribute data, as well as the disaster-bearing body identifier, spatial coordinates, and timestamp from the status record and the historical disaster damage data; establish a first association relationship for data items with consistent disaster-bearing body identifiers, establish candidate association relationships for data items with missing disaster-bearing body identifiers, identical administrative division codes, and spatial coordinates within a preset distance threshold, and sort the data items corresponding to the first association relationship and the candidate association relationship according to the timestamp order to generate a disaster-bearing body status sequence.

4. The method for dynamic assessment of global disaster risk based on the vulnerability of disaster-bearing bodies according to claim 1, characterized in that, The steps for generating the dynamic vulnerability characterization value include: Obtain the structural type, damage level, maintenance status, and anomaly marker corresponding to each state node in the disaster-bearing body state sequence. Read the baseline vulnerability characterization value from the vulnerability benchmark table according to the structural type. Determine the damage correction range according to the damage level and maintenance status. Determine the anomaly confidence status according to the anomaly marker. Correct the baseline vulnerability characterization value based on the damage correction range and the anomaly confidence status to generate a dynamic vulnerability characterization value.

5. The method for dynamic assessment of global disaster risk based on the vulnerability of disaster-bearing bodies according to claim 1, characterized in that, The steps for adjusting the dynamic vulnerability characterization value include: The system acquires the disaster-bearing body identifier, spatial coordinates, regional grid number, link node number, and state change direction of the disaster-bearing body undergoing state change. Based on the regional grid adjacency relationship or infrastructure link connectivity relationship, it determines the associated disaster-bearing bodies. It reads the association weight and location distance between the associated disaster-bearing bodies and the disaster-bearing bodies undergoing state change, and generates a spatial vulnerability correction amount based on the association weight, location distance, and state change direction. The spatial vulnerability correction amount is superimposed on the dynamic vulnerability characterization value to generate the adjusted vulnerability characterization value.

6. The method for dynamic assessment of global disaster risk based on the vulnerability of disaster-bearing bodies according to claim 5, characterized in that, The process of determining associated disaster-bearing bodies based on regional grid adjacency relationships or infrastructure link connectivity relationships specifically includes: Obtain the adjacent grid numbers of the grid in the area where the disaster-bearing body with the status change is located, as well as the upstream, downstream, and peer nodes of the infrastructure link. Determine the disaster-bearing bodies that are located in adjacent grids and have the same disaster-bearing body type as grid-related disaster-bearing bodies, and determine the disaster-bearing bodies that are located within the service range of the upstream, downstream, or peer nodes as link-related disaster-bearing bodies, and generate spatial association relationships.

7. The method for dynamic assessment of global disaster risk based on the vulnerability of disaster-bearing bodies according to claim 1, characterized in that, The steps for determining the risk level include: The trusted source marker, collection time, spatial coordinates of the disaster-bearing body, and data provider identifier are obtained from the source information. The time-effect decay state is determined based on the time interval between the collection time and the current assessment time. The spatial deviation state is determined based on the positional deviation between the spatial coordinates of the disaster-bearing body and the sampling location of the disaster hazard data. The trusted source marker, time-effect decay state, and spatial deviation state are combined to generate a credibility correction coefficient. The reliable source marker, collection time, and data provider identifier are obtained from the source information. The spatial coordinates of the disaster-bearing body and the importance weight of the disaster-bearing body corresponding to the disaster-bearing body identifier are obtained from the structural attribute data. The time-effect decay state is determined based on the time interval between the collection time and the current assessment time. The spatial deviation state is determined based on the positional deviation between the spatial coordinates of the disaster-bearing body and the sampling position of the disaster hazard data. The reliable source marker, the time-effect decay state, and the spatial deviation state are combined to generate a reliability correction coefficient. The risk score of a single disaster-bearing body is calculated by combining the disaster hazard data, the exposure data, the dynamic vulnerability characterization value and the confidence correction coefficient. The risk scores of single disaster-bearing bodies within the same regional grid or the same infrastructure link are weighted and merged according to the importance weight of each disaster-bearing body corresponding to its identifier to generate a regional link risk score. The regional link risk score is then compared with a preset risk level range to generate a risk level. The risk level, the predicted damage state corresponding to the risk level, the assessment time window, and the spatial coordinates of the disaster-bearing body are associated and stored to form an assessment record.

8. The method for dynamic assessment of global disaster risk based on the vulnerability of disaster-bearing bodies according to claim 7, characterized in that, The process of generating the credibility correction coefficient specifically includes: Read trusted source markers and source markers to be verified from trusted source records. Determine the data items corresponding to trusted source markers as regular participating data items and the data items corresponding to source markers to be verified as deweighted participating data items. When the time interval exceeds the preset timeliness threshold or the location deviation exceeds the preset spatial threshold, adjust the participation weight of the corresponding data items by decreasing the weight. The confidence correction coefficient is generated by merging the participation weights of regular data items, the participation weights of de-weighted data items, the time decay state corresponding to the time interval, and the spatial deviation state corresponding to the position deviation.

9. The method for dynamic assessment of global disaster risk based on the vulnerability of disaster-bearing bodies according to claim 1, characterized in that, The steps for updating the disaster-bearing body state sequence or the dynamic vulnerability characterization value include: Obtain the disaster-bearing body identifier, verification location, verification time, measured damage level, and handling status from the terminal verification results or disaster records, and obtain the assessment record corresponding to the risk level. The assessment record includes the predicted damage status, assessment time window, and spatial coordinates of the disaster-bearing body. Match the assessment record according to the disaster-bearing body identifier, the verification location, and the verification time, and generate an assessment record comparison result based on the difference between the measured damage level and the predicted damage status in the matched assessment record. When the assessment record comparison result indicates a missing or outdated status, the measured damage level and the treatment status are written into the disaster-bearing body status sequence; when the assessment record comparison result indicates a vulnerability deviation, the dynamic vulnerability characterization value is corrected based on the measured damage level, and an assessment write-back update record is generated.

10. The method for dynamic assessment of global disaster risk based on the vulnerability of disaster-bearing bodies according to claim 9, characterized in that, The process of generating the comparison results of the assessment records based on the difference between the measured damage level and the predicted damage state in the assessment records specifically includes: Read the risk level, predicted damage status, assessment time window, and spatial coordinates of the disaster-bearing body from the assessment record, wherein the assessment time window is the assessment time range corresponding to the formation of the risk level; and determine the assessment record that the review time falls within the assessment time window and the positional deviation between the review position and the spatial coordinates of the disaster-bearing body is less than a preset write-back distance threshold as a comparable assessment record. The measured damage level is compared with the predicted damage state in the comparable assessment record. The status missing mark, status lag mark, or vulnerability deviation mark are determined according to the direction of the level difference, the magnitude of the level difference, and the treatment status, and the assessment record comparison result is generated.

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