Quantitative-qualitative adaptive natural disaster prevention and control capability evaluation method based on data quality score and application

Abstract

The present application relates to a kind of quantitative-qualitative self-adaptive natural disaster prevention and control capability evaluation method and application based on data quality score, qualitative index system and quantitative index system are respectively constructed;After obtaining the to-be-evaluated data corresponding to the evaluation object, the comprehensive data quality score is calculated from four dimensions of data completeness, data timeliness, data accuracy and data consistency;According to the comparison result of the comprehensive data quality score and the first threshold and the second threshold, it is determined to adopt quantitative evaluation mode, qualitative evaluation mode or quantitative-qualitative fusion evaluation mode.The present application realizes the adaptive matching of natural disaster prevention and control capability evaluation method and data basic condition by data quality driven evaluation mode selection, improves the scientificity, applicability and reliability of evaluation result.

Description

Technical Field

[0001] This invention belongs to the field of natural disaster risk assessment and emergency management technology, and in particular relates to the application of a quantitative-qualitative adaptive natural disaster prevention and control capability assessment method based on data quality scoring. Background Technology

[0002] Natural disaster prevention and mitigation capacity assessment is a crucial foundation for improving regional disaster risk governance, optimizing the allocation of disaster prevention, mitigation, and relief resources, and supporting emergency management decision-making. Existing natural disaster prevention and mitigation capacity assessment methods mainly fall into two categories: quantitative assessment methods and qualitative assessment methods.

[0003] Quantitative assessment methods typically rely on statistical data, monitoring data, business ledger data, or historical data. They construct an indicator system and employ mathematical models for comprehensive evaluation, offering advantages such as strong objectivity and repeatable results. However, they place high demands on the completeness, timeliness, accuracy, and consistency of the underlying data. When data is missing, outdated, inconsistent across multiple sources, or difficult to verify, quantitative assessment results are prone to bias.

[0004] Qualitative assessment methods typically rely on expert experience, system verification, document review, on-site inspection, or questionnaire surveys to score or classify the assessment objects. They have the advantage of being able to be implemented even when data is insufficient, but their results are somewhat subjective, and the consistency and stability of the evaluation results are easily affected by differences in expert experience.

[0005] While existing technologies combine quantitative and qualitative methods, the following problems still exist: 1. The lack of a unified and standardized quantitative evaluation model for data quality makes it impossible to objectively measure and evaluate the quality of the basic data, and it is impossible to scientifically match the corresponding evaluation method according to the basic data condition. Only quantitative or qualitative evaluation can be selected manually, and the selection of evaluation mode is subjective. 2. The hierarchical logic of the indicator system is unclear, and there is a problem of listing indicators in a fixed number, resulting in insufficient universality and scalability; 3. The quantitative evaluation results and the qualitative evaluation results lack a unified hierarchical structure, which leads to insufficient comparability between the two and difficulty in integration. 4. Existing methods typically employ a fixed evaluation model and lack a mechanism to automatically select the evaluation model based on the data quality status.

[0006] Therefore, there is an urgent need for a natural disaster prevention and control capability assessment method that can first construct qualitative and quantitative indicator systems for the same regional assessment object, then adaptively select the assessment mode based on data quality scores, and, when necessary, integrate quantitative and qualitative results. Summary of the Invention

[0007] To address the problems existing in the prior art, this invention provides a quantitative-qualitative adaptive natural disaster prevention and control capability assessment method based on data quality scoring, which improves the scientificity, applicability, and reliability of the assessment results.

[0008] The technical solution of this invention is as follows: A quantitative-qualitative adaptive natural disaster prevention and control capability assessment method based on data quality scoring, comprising the following steps: S1. A multi-level structure consisting of a dimension layer, a capability layer, and an element layer is used to construct qualitative and quantitative indicator systems, respectively. S2. After obtaining the data to be evaluated for the evaluation object, calculate the comprehensive data quality score Dq from four dimensions: data completeness, data timeliness, data accuracy, and data consistency. S3. Based on the comparison results between the comprehensive data quality score Dq and the first threshold T1 and the second threshold T2, determine whether to adopt a quantitative evaluation mode, a qualitative evaluation mode, or a quantitative-qualitative fusion evaluation mode. S4. When using the quantitative evaluation mode, call the quantitative indicator system and calculate the quantitative evaluation result Rq; when using the qualitative evaluation mode, call the qualitative indicator system and calculate the qualitative evaluation result Rd; when using the fusion evaluation mode, calculate Rq and Rd respectively, and then calculate the final evaluation result R according to the preset fusion formula.

[0009] Furthermore, step S1, which constructs a multi-level evaluation index system, includes: S11. Construct a quantitative indicator system for assessing natural disaster prevention and control capabilities. The dimension layer is used to characterize the quantitative evaluation dimensions of natural disaster prevention and control capabilities. The capability layer is used to characterize the capability categories under each quantitative evaluation dimension. The element layer is used to characterize the quantitative evaluation elements under each capability category whose values ​​are directly obtained through statistical data, monitoring data, business ledger data, standardized formulas, or binary codes. S12. Construct a qualitative indicator system for assessing natural disaster prevention and control capabilities. The dimension layer is used to characterize the macro-evaluation dimensions of natural disaster prevention and control capabilities; the capability layer is used to characterize the capability categories under each dimension; and the element layer is used to characterize the specific evaluation elements under each capability category that form the scoring results through expert evaluation, system verification, on-site inspection, document review, or questionnaire evaluation.

[0010] Furthermore, in step S11, the dimensional layers of the quantitative indicator system include: monitoring and early warning capabilities, disaster prevention capabilities, rescue and relief capabilities, disaster emergency management capabilities, disaster risk management capabilities, and economic and social security capabilities; under the monitoring and early warning capability dimension, there are capability layers for disaster monitoring capabilities and early warning and forecasting capabilities; under the disaster prevention capability dimension, there are capability layers for engineering defense capabilities and important facility defense capabilities; under the rescue and relief capability dimension, there are capability layers for comprehensive fire rescue capabilities, professional rescue capabilities, and medical rescue capabilities; under the disaster emergency management capability dimension, there are capability layers for emergency management capabilities and emergency support capabilities; under the disaster risk management capability dimension, there are capability layers for risk prevention capabilities and risk diversification capabilities; and under the economic and social security capability dimension, there are capability layers for disaster prevention economic support capabilities and post-disaster social security capabilities. The evaluation elements selected for the element layer cover categories such as monitoring station density, early warning information coverage, flood control standards of dike projects, proportion of high-standard dikes, seismic fortification compliance rate of houses, proportion of comprehensive fire and rescue personnel, proportion of medical and health technicians, number of emergency plans, density of emergency shelters, investment in disaster prevention and mitigation funds, disaster insurance payout rate, and per capita GDP.

[0011] Furthermore, the quantitative evaluation elements in the quantitative indicator system adopt standardized quantitative calculation methods, including density-based calculation, ratio-based calculation, per capita-based calculation, count-based calculation, and Boolean judgment-based calculation. The formula for calculating density is:

[0012] The formula for calculating proportions is:

[0013] The formula for calculating per capita is:

[0014] The formula for counting is:

[0015] The Boolean decision class calculation formula is:

[0016] in: To quantify the values ​​of evaluation elements, For the number of facilities, sites, institutions or mechanisms, These are spatial scale parameters for regional area, coastline length, and forest area. and These are the number of conditions met and the total number, respectively. For a certain total amount of resources or total economic output, This represents the total population of the region.

[0017] Furthermore, in step S12, the dimensional layers of the qualitative indicator system include: risk prevention and disaster reduction capabilities, monitoring and early warning capabilities, and emergency response and rescue capabilities; under the risk prevention and disaster reduction capabilities dimension, capability layers are set up for prevention capabilities, emergency preparedness capabilities, and disaster reduction capabilities; under the monitoring and early warning capabilities dimension, capability layers are set up for monitoring capabilities and early warning capabilities; and under the emergency response and rescue capabilities dimension, capability layers are set up for emergency response capabilities, life protection capabilities, basic needs and service capabilities for disaster-stricken people, property and environmental protection capabilities, danger elimination capabilities, and recovery and reconstruction capabilities. The selection of evaluation elements at the element layer covers categories such as risk identification, risk assessment, risk prevention, government supervision, technological support, material support, personnel support, monitoring layout, timeliness of monitoring information, accuracy of monitoring information, release of early warning information, accuracy of early warning information, emergency command and decision-making, emergency support and coordination, emergency material support, medical rescue, environmental pollution prevention and control, disease prevention and control in disaster areas, reconstruction compensation, and recovery and reconstruction time. Although the evaluation elements in the qualitative indicator system contain quantitative information or objective data, the system does not directly use raw data for quantitative model calculation. Instead, qualitative element scores are formed through scoring rules, data verification, judgment of system completeness, evaluation of allocation rationality, and comprehensive expert judgment. The qualitative and quantitative indicator systems establish semantic correspondence or mapping relationships at the dimension and capability levels to ensure that the quantitative and qualitative evaluation results are comparable and integrable under the fusion evaluation mode; wherein, the mapping relationship is a one-to-one correspondence, or a one-to-many or many-to-one correspondence.

[0018] Further, in step S2, for the data in the element layer, the data completeness Dc, data timeliness Dt, data accuracy Da, and data consistency Con are calculated respectively, and the comprehensive data quality score Dq is calculated, including the following steps: S21. Data completeness Dc Data completeness measures the ratio between the actual data items collected and the data items that should have been collected, reflecting the degree of missing basic data. Its calculation formula is as follows:

[0019] in: This represents the number of valid data items actually collected. This represents the total number of data items required for the feature layer. S22. Data Timeliness Dt Data timeliness measures the frequency and freshness of data updates, and is calculated using the following formula:

[0020] in: This is the time difference between the current assessment time and the last data update time. For the data baseline update cycle, This is the time decay coefficient, used to represent the degree of impact of data aging on the reliability of the assessment; S23. Data Accuracy (Da) Data accuracy measures the degree of conformity between collected data and authoritative reference values. It is calculated using sampling data error verification methods, and its expression is:

[0021] in: This refers to the number of qualified data items in the sampled data whose error is within the allowable range. This represents the total number of data items used in the sampling verification. S24. Data Consistency Con Data consistency measures the degree of coordination between data from different sources. When the same indicator has multiple data sources, consistency is assessed by calculating the degree of deviation between the data from different sources. The formula is as follows:

[0022] in: The value of this indicator provided by the j-th data source. For the number of data sources, This is the average of data from all sources. A higher consistency score is achieved when the deviation between data from different sources is small; a lower consistency score is achieved when the data differences are large.

[0023] S25. Overall Data Quality Score Dq After obtaining the scores for each dimension, the overall data quality score is calculated using a weighted summation method, and its expression is:

[0024] in: Score the data completeness. To score the timeliness of the data, Data accuracy score Score the data consistency. These are the weighting coefficients for each dimension. .

[0025] Furthermore, for real-time monitoring indicators =10~15; for static attribute type indicators =5~8; By default: .

[0026] Furthermore, in step S3, the evaluation mode is selected based on the comprehensive data quality score Dq.

[0027] when Enter quantitative assessment mode; when Enter qualitative assessment mode; when Entering a quantitative-qualitative integrated evaluation mode; in: To quantitatively assess the trigger threshold, To qualitatively assess the trigger threshold, .

[0028] Furthermore, in step S3, S31. When the overall data quality score meets the requirements At that time, a quantitative evaluation method was adopted, and a quantitative indicator system was invoked to obtain quantitative evaluation results. : Let the original value of the k-th quantitative element in the j-th capability layer under the h-th dimension be... After quantitative calculation and standardization, the score of the kth quantitative element is obtained. The standardization process transforms raw data of different dimensions and scales into a unified scoring range of 0 to 100 points. Let the weights of each quantitative element within the capability layer be . And satisfy:

[0029] in, Let be the total number of quantitative elements contained in the j-th capability layer under the h-th dimension layer. Weights for each quantitative element The quantitative score of the j-th capability layer under the h-th dimension layer is:

[0030] in, Let be the total number of quantitative elements contained in the j-th capability layer under the h-th dimension layer. Assign weights to each quantitative element. Score for the kth quantitative element Let the weights of each capability layer under the h-th dimension be... And satisfy:

[0031] in, Let h be the total number of capability layers contained in the h-th dimension layer. The weights of each capability layer under the h-th dimension are... The quantitative score for the h-th dimension is:

[0032] in, Let h be the total number of capability layers contained in the h-th dimension layer. Let h be the weights of each capability layer under the h-th dimension. The quantitative score of the j-th capability layer under the h-th dimension layer. Let the weights of each dimension be... And satisfy:

[0033] Where H represents the number of dimensional layers in the quantitative indicator system. Weights for each dimension layer The final quantitative assessment result is as follows:

[0034] Where H represents the number of dimension layers in the quantitative indicator system. For the weights of each dimension layer, The quantitative score for the h-th dimension layer; S32. When the overall data quality score meets the requirements At that time, a qualitative evaluation method was adopted, and a qualitative indicator system was invoked to obtain the qualitative evaluation results. : Let the k-th quantitative element in the j-th capability layer under the h-th dimension be rated by p experts, and the t-th expert's rating be... Then the average score of this qualitative element is:

[0035] in, The total number of experts participating in the scoring of a single qualitative element. Rate by experts Let the weights of each qualitative element within the capability layer be . And satisfy:

[0036] in, Let be the total number of qualitative elements contained in the j-th capability layer under the h-th dimension layer. Weights of each qualitative element within the capability layer Then the qualitative score of the j-th capability layer under the h-th dimension layer is:

[0037] in, Let be the total number of qualitative elements contained in the j-th capability layer under the h-th dimension layer. The weights of each qualitative element within the capability layer, The average score of this qualitative element Let the weights of each capability layer under the h-th dimension be... And satisfy:

[0038] in, Let h be the total number of capability layers contained in the h-th dimension layer. The weights of each capability layer under the h-th dimension are... The qualitative score for the h-th dimension is:

[0039] in, Let h be the total number of capability layers contained in the h-th dimension layer. Let h be the weights of each capability layer under the h-th dimension. The qualitative score of the j-th capability layer under the h-th dimension layer. Let the weights of each dimension be... And satisfy:

[0040] in, This represents the total number of dimension layers in the qualitative indicator system. Weights for each dimension layer The final qualitative assessment result is:

[0041] Where: H' represents the number of dimension layers in the qualitative indicator system. For the weights of each dimension layer, The qualitative score for the h-th dimension. S33. When the overall data quality score meets the requirements This indicates that the data quality of the evaluated object is in the middle range, meaning that some evaluation elements have the conditions for quantitative calculation, but there are still situations where supplementary evaluation is needed through expert judgment, system verification, or on-site inspection. Therefore, a quantitative-qualitative integrated evaluation model is adopted. In the aforementioned integrated evaluation mode, the system calculates quantitative evaluation results based on a quantitative indicator system. And based on the qualitative indicator system, the qualitative evaluation results are calculated. Then, the quantitative evaluation results are processed according to a preset fusion formula. and the qualitative assessment results Weighted fusion is performed to obtain the final evaluation result R; The fusion formula is as follows:

[0042] in: The weighting coefficients for the quantitative evaluation results. Let be the weighting coefficients of the qualitative evaluation results, and satisfy: ; The weighting coefficient and Pre-set according to the needs of the assessment task.

[0043] A computer-readable storage medium includes a stored computer program, wherein, when the computer program is executed, it controls the device on which the computer-readable storage medium is located to perform the above-described method.

[0044] The beneficial effects of this invention are: 1. This invention constructs a four-dimensional data quality quantification model integrating data completeness, timeliness, accuracy, and consistency, achieving standardized and quantitative evaluation of the quality of basic assessment data. It addresses the core pain points of existing technologies, such as the lack of unified data quality evaluation standards and the inability to scientifically match evaluation methods. It provides a precise, quantifiable, and reproducible decision-making basis for selecting evaluation models, avoiding subjectivity and blind spots in model selection.

[0045] 2. This invention designs a dual-indicator system with a three-level structure of dimension layer, capability layer, and element layer. This ensures that the hierarchical logic of the indicator system is clear, universal, and scalable. Furthermore, through the semantic mapping relationship between the two indicator systems, it enables the comparability of quantitative and qualitative assessment results within the same dimension. The quantitative indicator system can achieve a refined and objective quantification of prevention and control capabilities, while the qualitative indicator system is adapted to the assessment needs of scenarios with insufficient data.

[0046] 3. This invention designs a dual-threshold, three-mode adaptive evaluation mechanism based on data quality scoring, achieving dynamic adaptive matching between the evaluation method and the data foundation. When the data quality is excellent, a quantitative evaluation mode with strong objectivity and high accuracy is adopted; when the data quality is poor, a qualitative evaluation mode with strong applicability and low data dependence is adopted; when the data quality is moderate, a quantitative-qualitative integrated evaluation mode is adopted, taking into account both the objectivity and comprehensiveness of the evaluation results. This mechanism can adapt to the evaluation needs of different regions and different data foundation conditions, significantly improving the applicability, flexibility, and robustness of the evaluation method.

[0047] 4. This invention achieves standardization and normalization of the assessment process through standardized quantitative calculation methods, a hierarchically weighted assessment result aggregation model, and a flexibly configurable weight system and threshold parameters. The assessment results are repeatable, traceable, and comparable. At the same time, it can be flexibly adjusted according to the needs of different assessment scenarios and different types of disasters. It can provide a standardized tool for assessing natural disaster prevention and control capabilities at the county level, and can also be extended to different administrative levels such as provinces and cities, as well as to assess prevention and control capabilities for different types of disasters such as floods, earthquakes, and geological disasters. It has extremely strong promotion and application value.

[0048] 5. This invention, through a data quality-driven adaptive evaluation model, effectively avoids the negative impact of data deficiencies, distortions, and outdated information on evaluation results. It avoids the bias caused by forcibly using quantitative evaluation when data is insufficient, and also avoids the problem of excessive subjectivity caused by over-reliance on qualitative evaluation when data is sufficient. It fundamentally improves the scientificity, reliability, and credibility of natural disaster prevention and control capacity evaluation results, and can provide accurate and reliable decision support for the optimal allocation of regional disaster prevention, mitigation, and relief resources, the formulation of emergency management policies, and the improvement of disaster risk prevention and control capabilities. Attached Figure Description

[0049] Figure 1 This is an overall flowchart of the quantitative-qualitative adaptive evaluation method based on data quality scoring of the present invention; Figure 2 This is a schematic diagram of the four-dimensional evaluation system of the data quality scoring model of the present invention; Figure 3 This is a schematic diagram of the dual-threshold, three-evaluation mode selection mechanism of the present invention; Detailed Implementation To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0050] like Figure 1-3 As shown, the quantitative-qualitative adaptive natural disaster prevention and control capability assessment method based on data quality scoring of the present invention includes the following steps: I. Constructing a multi-level evaluation indicator system (1) Quantitative indicator system This invention constructs a quantitative indicator system for assessing natural disaster prevention and control capabilities. The quantitative indicator system also adopts a three-level structure: dimension layer, capability layer, and element layer.

[0051] Among them, the dimension layer is used to characterize the quantitative evaluation dimensions of natural disaster prevention and control capabilities; the capability layer is used to characterize the capability categories under each quantitative evaluation dimension; and the element layer is used to characterize the quantitative evaluation elements under each capability category whose values ​​can be directly obtained through statistical data, monitoring data, business ledger data, standardized formulas, or binary codes.

[0052] Preferably, the dimensional layers of the quantitative indicator system include: monitoring and early warning capabilities; disaster prevention capabilities; rescue and relief capabilities; disaster emergency management capabilities; disaster risk management capabilities; and economic and social security capabilities.

[0053] Specifically: the monitoring and early warning capability dimension can be divided into capability layers such as disaster monitoring capability and early warning and forecasting capability; the disaster prevention capability dimension can be divided into capability layers such as engineering defense capability and important facility defense capability; the rescue and relief capability dimension can be divided into capability layers such as comprehensive fire rescue capability, professional rescue capability and medical rescue capability; the disaster emergency management capability dimension can be divided into capability layers such as emergency management capability and emergency support capability; the disaster risk management capability dimension can be divided into capability layers such as risk prevention capability and risk diversification capability; and the economic and social security capability dimension can be divided into capability layers such as disaster prevention economic support capability and post-disaster social security capability.

[0054] The evaluation elements at the element layer can preferably cover categories such as monitoring station density, early warning information coverage, flood control standards of dike projects, proportion of high-standard dikes, seismic fortification compliance rate of houses, proportion of comprehensive fire and rescue personnel, proportion of medical and health technicians, number of emergency plans, density of emergency shelters, investment in disaster prevention and mitigation funds, disaster insurance payout rate, and per capita GDP.

[0055] The quantitative evaluation elements in the quantitative indicator system preferably adopt standardized quantitative calculation methods, including density calculation, proportion calculation, per capita calculation, count calculation and Boolean judgment calculation.

[0056] The preferred formula for density calculation is:

[0057] The preferred formula for calculating proportions is:

[0058] The preferred formula for calculating per capita is:

[0059] The preferred formula for counting calculations is:

[0060] The preferred Boolean decision formula is:

[0061] in: To quantify the values ​​of evaluation elements, For the number of facilities, sites, institutions or mechanisms, These are spatial scale parameters such as regional area, coastline length, and forest area. and These are the number of conditions met and the total number, respectively. For a certain total amount of resources or total economic output, This represents the total population of the region.

[0062] (2) Construction of a qualitative indicator system This invention constructs a qualitative index system for assessing natural disaster prevention and control capabilities. The qualitative index system adopts a three-level structure: dimension layer, capability layer, and element layer.

[0063] Among them, the dimension layer is used to characterize the macro-evaluation dimensions of natural disaster prevention and control capabilities; the capability layer is used to characterize the capability categories under each dimension; and the element layer is used to characterize the specific evaluation elements under each capability category that form the scoring results through expert evaluation, system verification, on-site inspection, document review, or questionnaire evaluation.

[0064] Preferably, the dimensions of the qualitative indicator system include: risk prevention and disaster reduction capabilities; monitoring and early warning capabilities; and emergency response and rescue capabilities.

[0065] Among them: under the risk prevention and disaster reduction capability dimension, capability layers such as prevention capability, emergency preparedness capability and disaster reduction capability can be set; under the monitoring and early warning capability dimension, capability layers such as monitoring capability and early warning capability can be set; under the emergency response and rescue capability dimension, capability layers such as emergency response capability, life protection capability, basic needs and service capability for disaster-stricken people, property and environmental protection capability, danger elimination capability and recovery and reconstruction capability can be set.

[0066] The evaluation elements at the element level can preferably cover categories such as risk identification, risk assessment, risk prevention, government supervision, technological support, material support, personnel support, monitoring layout, timeliness of monitoring information, accuracy of monitoring information, release of early warning information, accuracy of early warning information, emergency command and decision-making, emergency support and coordination, emergency material support, medical rescue, environmental pollution prevention and control, disease prevention and control in disaster areas, reconstruction compensation, and recovery and reconstruction time.

[0067] It should be noted that although the evaluation elements at the element level in the qualitative indicator system may contain quantitative information or objective data, the system does not directly use raw data for quantitative model calculation. Instead, qualitative element scores are formed through scoring rules, data verification, judgment of system completeness, evaluation of allocation rationality, and comprehensive expert judgment.

[0068] (3) The correspondence between the two sets of indicator systems The qualitative and quantitative indicator systems establish semantic correspondence or mapping relationships at the dimension and capability levels to ensure that the quantitative and qualitative evaluation results are comparable and integrable under the fusion evaluation mode.

[0069] The mapping relationship can be a one-to-one correspondence, a one-to-many correspondence, or a many-to-one correspondence. For example, the "risk prevention and disaster reduction capability" in the qualitative indicator system can be mapped to the "disaster defense capability" and "disaster risk management capability" in the quantitative indicator system; the "emergency response and rescue capability" in the qualitative indicator system can be mapped to the "rescue and assistance capability" and "disaster emergency management capability" in the quantitative indicator system.

[0070] II. Establishing a Data Quality Scoring Model For the data in the element layer, calculate the data completeness Dc, data timeliness Dt, data accuracy Da, and data consistency Con, and calculate the comprehensive data quality score Dq.

[0071] (1) Data completeness (Dc) Data completeness measures the ratio between the actual data items collected and the data items that should have been collected, reflecting the degree of missing basic data. Its calculation formula is as follows:

[0072] in: This represents the number of valid data items actually collected. This represents the total number of data items required for the feature layer.

[0073] (2) Data timeliness (Dt) The formula for measuring the frequency and freshness of data updates is:

[0074] in: This is the time difference between the current assessment time and the last data update time. For the data baseline update cycle, This is the time decay coefficient, used to represent the degree of impact of data aging on the reliability of the assessment.

[0075] Generally, for real-time monitoring indicators (such as rainfall, water level, etc.), it is recommended that... =10~15; for static attribute indicators (such as topography, land use type, etc.), it is recommended =5~8.

[0076] The closer the data update time is to the current evaluation time, the higher the data timeliness score.

[0077] (3) Data accuracy (Da) Data accuracy measures the degree of conformity between collected data and authoritative reference values. It is calculated using sampling data error verification methods, and its expression is:

[0078] in: This refers to the number of qualified data items in the sampled data whose error is within the allowable range. This represents the total number of data items that were sampled for validation.

[0079] The error judgment rule is as follows:

[0080] in: i , This is the authoritative reference value or historical benchmark value for the i-th data item.

[0081] (4) Data consistency (Con) Data consistency measures the degree of harmony between data from different sources. When the same indicator has multiple data sources, consistency is assessed by calculating the degree of deviation between the data from different sources. The formula is as follows:

[0082] in: The value of this indicator provided by the j-th data source. For the number of data sources, This is the average of data from all sources.

[0083] A higher consistency score is achieved when the deviation between data from different sources is small; a lower consistency score is achieved when the data differences are large.

[0084] (5) Data Quality Score (Dq) After obtaining the scores for each dimension, the overall data quality score is calculated using a weighted summation method, and its expression is:

[0085] in: Score the data completeness. To score the timeliness of the data, Data accuracy score Score the data consistency. These are the weighting coefficients for each dimension.

[0086] satisfy:

[0087] By default:

[0088] The weight parameters can be dynamically adjusted according to the needs of the evaluation scenario.

[0089] III. Selection of Evaluation Model Based on Data Quality Scoring The evaluation mode is selected based on the comprehensive data quality score Dq.

[0090] when:

[0091] Enter quantitative assessment mode; when:

[0092] Enter qualitative assessment mode; when:

[0093] Enter the quantitative-qualitative integrated evaluation mode.

[0094] in: To quantitatively assess the trigger threshold, To qualitatively assess the trigger threshold, .

[0095] IV. Quantitative Assessment Methods When the comprehensive data quality score meets At that time, a quantitative evaluation method was adopted, and a quantitative indicator system was invoked to obtain quantitative evaluation results. .

[0096] Let the original value of the k-th quantitative element in the j-th capability layer under the h-th dimension be... After quantitative calculation and standardization, the score of the kth quantitative element is obtained. The standardization process preferably transforms the original data with different dimensions and scales into a unified scoring range, preferably 0 to 100 points.

[0097] Let the weights of each quantitative element within the capability layer be . And satisfy:

[0098] The quantitative score of the j-th capability layer under the h-th dimension layer is:

[0099] Let the weights of each capability layer under the h-th dimension be... And satisfy:

[0100] The quantitative score for the h-th dimension is:

[0101] Let the weights of each dimension be... And satisfy:

[0102] The final quantitative assessment result is as follows:

[0103] Where: H represents the number of dimension layers in the quantitative indicator system.

[0104] V. Qualitative Assessment Methods When the comprehensive data quality score meets At that time, a qualitative evaluation method was adopted, and a qualitative indicator system was invoked to obtain the qualitative evaluation results. .

[0105] Let the k-th qualitative element in the j-th capability layer under the h-th dimension be rated by p experts, and the t-th expert's rating be... Then the average score of this qualitative element is:

[0106] Let the weights of each qualitative element within the capability layer be . And satisfy:

[0107] Then the qualitative score of the j-th capability layer under the h-th dimension layer is:

[0108] Let the weights of each capability layer under the h-th dimension be... And satisfy:

[0109] The qualitative score for the h-th dimension is:

[0110] Let the weights of each dimension be... And satisfy:

[0111] The final qualitative assessment result is:

[0112] Where H' represents the number of dimension layers in the qualitative indicator system.

[0113] VI. Quantitative-Qualitative Integrated Assessment When the comprehensive data quality score meets When the data quality of the evaluated object is in the middle range, it means that some evaluation elements have the conditions for quantitative calculation, but there are still situations where supplementary evaluation is needed through expert judgment, system verification or on-site inspection. Therefore, a quantitative-qualitative integrated evaluation model is adopted.

[0114] In the aforementioned integrated evaluation mode, the system calculates quantitative evaluation results based on a quantitative indicator system. And based on the qualitative indicator system, the qualitative evaluation results are calculated. Then, the quantitative evaluation results are processed according to a preset fusion formula. and the qualitative assessment results Weighted fusion is performed to obtain the final evaluation result R.

[0115] The fusion formula is as follows:

[0116] in: The weighting coefficients for the quantitative evaluation results. Let be the weighting coefficients of the qualitative evaluation results, and satisfy:

[0117] The weighting coefficient and It can be preset according to the needs of the assessment task.

[0118] The following detailed description, using a specific embodiment, illustrates the method of this invention, taking the assessment of a county's natural disaster prevention and control capabilities as an example. This embodiment includes steps such as constructing a dual-indicator system, calculating data quality scores, selecting an assessment model, quantitative assessment, qualitative assessment, and integrated assessment.

[0119] I. Construction of the Dual Indicator System 1. Construction of a quantitative indicator system A quantitative indicator system is constructed for the same county-level assessment object. The quantitative indicator system also adopts a three-level structure of dimension layer, capability layer and element layer.

[0120] The dimensional layer includes monitoring and early warning capabilities, disaster prevention capabilities, rescue and relief capabilities, disaster emergency management capabilities, disaster risk management capabilities, and economic and social security capabilities.

[0121] Taking the monitoring and early warning capability dimension as an example, its capability layer includes disaster monitoring capability and early warning and forecasting capability. Among them, the disaster monitoring capability is set with element layer evaluation elements such as the density of meteorological stations, hydrological stations, seismic network monitoring points, geological disaster monitoring points, marine tide observation stations, forest and grassland fire prevention monitoring and early warning stations, and disaster video monitoring points. The early warning and forecasting capability is set with element layer evaluation elements such as the accuracy of 24-hour weather forecasts, the number of early warning information release channels, the coverage rate of early warning information, the highest release frequency of early warning information, and the sharing mechanism of monitoring and early warning information among departments.

[0122] Taking disaster prevention capability as an example, its capability layer includes engineering defense capability and important facility defense capability. Among them, the engineering defense capability includes the flood control standard of dike and dam projects, the proportion of high-standard dikes, the water supply capacity of drought relief projects, the drainage capacity of pumping stations, the proportion of geological disaster prevention projects, and the proportion of sea dike project length. The important facility defense capability includes the seismic fortification compliance rate of buildings, the drainage capacity of urban underground pipe networks, and the fire prevention barrier and fire prevention road network density in forest areas.

[0123] Taking rescue and relief capabilities as an example, its capability layers include comprehensive fire rescue capabilities, professional rescue capabilities, and medical rescue capabilities. Among them, comprehensive fire rescue capabilities are evaluated by factors such as the proportion of comprehensive fire rescue personnel, the density of comprehensive fire stations, and the proportion of comprehensive fire trucks. Professional rescue capabilities are evaluated by factors such as the proportion of forest firefighters, the proportion of forest firefighting equipment, the proportion of earthquake professional rescue personnel, the proportion of earthquake professional rescue equipment, the proportion of mine / tunnel professional rescue personnel, the proportion of hazardous chemical / oil and gas professional rescue personnel, the proportion of maritime professional rescue personnel, and the proportion of maritime professional rescue vehicles and vessels. Medical rescue capabilities are evaluated by factors such as the proportion of medical and health technicians, the number of beds per 10,000 people, and the proportion of ambulances.

[0124] Taking disaster emergency management capability as an example, its capability layer includes emergency management capability and emergency support capability. Among them, the emergency management capability includes evaluation elements such as the number of professional and technical personnel and managers, the number of emergency plans, the emergency command system, the departmental emergency linkage capability, the number of emergency drills per year, and the proportion of residents participating in emergency drills and training. The emergency support capability includes evaluation elements such as the per capita area of ​​emergency relief material reserve warehouses, the emergency material reserve rate, the density of emergency shelters, the shelter occupancy rate, the proportion of communication base stations, emergency communication equipment, and the amount of urban emergency backup water sources.

[0125] Taking disaster risk management capability as an example, its capability layer includes risk prevention capability and risk diversification capability. Among them, risk prevention capability includes risk assessment capability, hidden danger investigation capability, number of disaster prevention and mitigation laws and plans, land space disaster prevention and mitigation planning, disaster prevention and mitigation funding, proportion of comprehensive disaster reduction demonstration communities, number of disaster prevention and mitigation publicity and education bases, and number of disaster prevention and mitigation publicity activities held annually. Risk diversification capability includes disaster insurance payout rate and disaster insurance coverage.

[0126] Taking the economic and social security capacity dimension as an example, its capacity layer includes socio-economic security capacity; among which, socio-economic security capacity sets up evaluation elements such as per capita regional GDP, per capita local government general budget revenue, per capita total social fixed asset investment, per capita total retail sales of consumer goods, per capita urban and rural residents' RMB savings deposits, the proportion of urban residents receiving minimum living allowances, and the proportion of rural residents receiving minimum living allowances.

[0127] 2. Construction of a Qualitative Indicator System To assess the county's natural disaster prevention and control capabilities, a qualitative indicator system was constructed. This system employs a three-tiered structure: dimension layer, capability layer, and element layer.

[0128] The dimension layer includes risk prevention and disaster reduction capabilities, monitoring and early warning capabilities, and emergency response and rescue capabilities.

[0129] Taking risk prevention and disaster reduction capabilities as an example, its capability layers include prevention capabilities, emergency preparedness capabilities, and disaster reduction capabilities. Among them, prevention capabilities are set up with element-level evaluation elements such as risk identification capabilities, risk assessment capabilities, risk prevention capabilities, and government supervision capabilities; emergency preparedness capabilities are set up with element-level evaluation elements such as technological support capabilities, material support capabilities, and team support capabilities; and disaster reduction capabilities are set up with element-level evaluation elements such as infrastructure, communication systems, water supply systems, and power supply systems.

[0130] Taking the monitoring and early warning capability dimension as an example, its capability layer includes monitoring capability and early warning capability. Among them, the monitoring capability layer sets evaluation elements such as monitoring station network layout, monitoring information timeliness and monitoring information accuracy, while the early warning capability layer sets evaluation elements such as monitoring data analysis capability, monitoring data sharing capability, early warning information release capability, early warning information timeliness, early warning information accuracy and disaster reporting capability.

[0131] Taking emergency response and rescue capabilities as an example, its capability layers include emergency response capability, life-saving capability, basic needs and service capability for disaster-stricken people, property and environmental protection capability, hazard elimination capability, and recovery and reconstruction capability. Among them, emergency response capability includes evaluation elements such as emergency command and decision-making capability, emergency support and coordination capability, emergency material support capability, emergency team support capability, emergency communication support capability, and emergency transportation support capability; life-saving capability includes evaluation elements such as initial response capability, search and rescue capability, and medical rescue capability; and recovery and reconstruction capability includes evaluation elements such as reconstruction compensation capability, material supply capability, and recovery and reconstruction time.

[0132] II. Calculation of Comprehensive Data Quality Score This embodiment uses the county's "meteorological station density" data as an example to score the overall data quality. The calculation process will be explained.

[0133] 1. Data completeness calculation Assuming the current evaluation task requires a total of 120 data items, and the actual number of valid data items collected is 114, then the data completeness is calculated using the following formula:

[0134] 2. Calculation of data timeliness Assuming the current assessment time is 2 months from the last update time of the relevant data, the baseline update cycle is T = 6 months, and the timeliness decay coefficient is θ = 10, then the data timeliness is calculated using the following formula:

[0135] 3. Data accuracy calculation If the total number of data items participating in the sampling verification is 100, and the number of data items that meet the error threshold requirement is 92, then the data accuracy is calculated using the following formula:

[0136] 4. Data Consistency Calculation Suppose that this evaluation element has three data sources, with corresponding values ​​of 120, 118, and 121 respectively. First, calculate the average value. Data consistency is calculated using the following formula:

[0137]

[0138] 5. Calculation of Comprehensive Data Quality Score When W1=W2=W3=W4=0.25, the overall data quality score is:

[0139] III. Evaluation Model Selection Let the first threshold T1 = 75 and the second threshold T2 = 50. Therefore, the county has entered a quantitative assessment mode.

[0140] Example 2: Quantitative Assessment Model This embodiment conducts a quantitative assessment of the county's natural disaster prevention and control capabilities. The quantitative assessment adopts a hierarchical aggregation method of "element layer score - capability layer score - dimension layer score - comprehensive score".

[0141] 1. Calculation from the element layer to the capability layer Suppose that the first capability layer under the monitoring and early warning capability dimension contains four quantitative elements, and their standardized scores are as follows:

[0142] The corresponding weights are as follows:

[0143] The quantitative score for this capability level is:

[0144] 2. Calculation from the capability layer to the dimension layer Suppose that there are two capability layers under the monitoring and early warning capability dimension, and their quantitative scores are as follows:

[0145] The corresponding capability layer weights are as follows:

[0146] The quantitative score for this dimension is:

[0147] 3. Calculation of the dimensional layer to the comprehensive result Let the scores of the county's six quantitative dimensions be as follows:

[0148] The corresponding dimension layer weights are as follows:

[0149] The final quantitative assessment result is as follows:

[0150] Example 3: Qualitative Assessment Model This embodiment performs a qualitative assessment on the same county. The qualitative assessment also adopts a hierarchical aggregation method of "element layer score - capability layer score - dimension layer score - comprehensive score".

[0151] 1. Calculation of expert scores to qualitative element level scores Assuming an expert scoring system is used for the "risk identification capability" element, five experts will participate in the scoring, and their scores will be as follows:

[0152] The average score for this qualitative element is:

[0153] 2. Calculation from the element layer to the capability layer Suppose there are 4 qualitative factors under a certain capability level, and their average scores are as follows:

[0154] The corresponding element weights are as follows:

[0155] The qualitative score for this ability level is:

[0156] 3. Calculation from the capability layer to the dimension layer Suppose there are two capability layers under a certain dimension, and their qualitative scores are as follows:

[0157] The corresponding capability layer weights are as follows:

[0158] The qualitative score for this dimension is:

[0159] 4. Calculation of the dimensional layer to the comprehensive result Let the scores of the three qualitative dimensions of the county be as follows:

[0160] The corresponding dimension layer weights are as follows:

[0161] The final qualitative assessment result is:

[0162] Example 4: Integration Evaluation Process This example illustrates the fusion assessment process when the county's overall data quality score is in the middle range. In another round of assessment, the county's overall data quality score is:

[0163] Because it satisfies Therefore, the county has entered a quantitative-qualitative integrated assessment model.

[0164] Using the aforementioned quantitative assessment method, the quantitative assessment results for the county were obtained. Using the aforementioned qualitative assessment method, the qualitative assessment results for the county were obtained. Given that the weighting coefficient for the quantitative evaluation result is α=0.50 and the weighting coefficient for the qualitative evaluation result is β=0.50, the final fusion evaluation result is:

[0165] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A quantitative-qualitative adaptive natural disaster prevention and control capability assessment method based on data quality scoring, characterized in that, Includes the following steps: S1. A multi-level structure of dimension layer, capability layer and element layer is adopted to construct a qualitative indicator system and a quantitative indicator system respectively, and a semantic correspondence or mapping relationship is established between the dimension layer and capability layer of the qualitative indicator system and the quantitative indicator system, so that the quantitative evaluation results and the qualitative evaluation results are comparable and integrable. S2. After obtaining the data to be evaluated for the evaluation object, calculate the comprehensive data quality score Dq from four dimensions: data completeness, data timeliness, data accuracy, and data consistency. S3. Based on the comparison results between the comprehensive data quality score Dq and the first threshold T1 and the second threshold T2, determine whether to adopt a quantitative evaluation mode, a qualitative evaluation mode, or a quantitative-qualitative fusion evaluation mode. S4. When using the quantitative evaluation mode, call the quantitative indicator system and calculate the quantitative evaluation result Rq; when using the qualitative evaluation mode, call the qualitative indicator system and calculate the qualitative evaluation result Rd; when using the fusion evaluation mode, calculate Rq and Rd respectively, and then calculate the final evaluation result R according to the preset fusion formula.

2. The quantitative-qualitative adaptive natural disaster prevention and control capability assessment method based on data quality scoring according to claim 1, characterized in that, Step S1, constructing a multi-level evaluation index system, includes: S11. Construct a quantitative indicator system for assessing natural disaster prevention and control capabilities. The dimension layer is used to characterize the quantitative evaluation dimensions of natural disaster prevention and control capabilities. The capability layer is used to characterize the capability categories under each quantitative evaluation dimension. The element layer is used to characterize the quantitative evaluation elements under each capability category whose values ​​are directly obtained through statistical data, monitoring data, business ledger data, standardized formulas, or binary codes. S12. Construct a qualitative indicator system for assessing natural disaster prevention and control capabilities. The dimension layer is used to characterize the macro-evaluation dimensions of natural disaster prevention and control capabilities; the capability layer is used to characterize the capability categories under each dimension; and the element layer is used to characterize the specific evaluation elements under each capability category that form the scoring results through expert evaluation, system verification, on-site inspection, document review, or questionnaire evaluation.

3. The quantitative-qualitative adaptive natural disaster prevention and control capability assessment method based on data quality scoring according to claim 2, characterized in that, In step S11, the dimensions of the quantitative indicator system include: monitoring and early warning capabilities, disaster prevention capabilities, rescue and relief capabilities, disaster emergency management capabilities, disaster risk management capabilities, and economic and social security capabilities. The monitoring and early warning capability dimension includes a capability layer for disaster monitoring and early warning / forecasting capabilities; the disaster prevention capability dimension includes a capability layer for engineering defense capabilities and the defense capabilities of important facilities; the rescue and relief capability dimension includes a capability layer for comprehensive fire and rescue capabilities, professional rescue capabilities, and medical rescue capabilities; the disaster emergency management capability dimension includes a capability layer for emergency management capabilities and emergency support capabilities; the disaster risk management capability dimension includes a capability layer for risk prevention capabilities and risk diversification capabilities; and the economic and social security capability dimension includes a capability layer for disaster prevention economic support capabilities and post-disaster social security capabilities. The evaluation elements selected for the element layer cover categories such as monitoring station density, early warning information coverage, flood control standards of dike projects, proportion of high-standard dikes, seismic fortification compliance rate of houses, proportion of comprehensive fire and rescue personnel, proportion of medical and health technicians, number of emergency plans, density of emergency shelters, investment in disaster prevention and mitigation funds, disaster insurance payout rate, and per capita GDP.

4. The quantitative-qualitative adaptive natural disaster prevention and control capability assessment method based on data quality scoring according to claim 3, characterized in that, The quantitative evaluation elements in the quantitative indicator system adopt standardized quantitative calculation methods, including density calculation, ratio calculation, per capita calculation, count calculation and Boolean judgment calculation. The formula for calculating density is: The formula for calculating proportions is: The formula for calculating per capita is: The formula for counting is: The Boolean decision class calculation formula is: in: To quantify the values ​​of evaluation elements, For the number of facilities, sites, institutions or mechanisms, These are spatial scale parameters for regional area, coastline length, and forest area. and These are the number of conditions met and the total number, respectively. For a certain total amount of resources or total economic output, This represents the total population of the region.

5. The quantitative-qualitative adaptive natural disaster prevention and control capability assessment method based on data quality scoring according to claim 3, characterized in that, In step S12, the qualitative indicator system includes the following dimensional layers: risk prevention and disaster reduction capability, monitoring and early warning capability, and emergency response and rescue capability. Under the risk prevention and disaster reduction capability dimension, there are capability layers for prevention capability, emergency preparedness capability, and disaster reduction capability; under the monitoring and early warning capability dimension, there are capability layers for monitoring capability and early warning capability; and under the emergency response and rescue capability dimension, there are capability layers for emergency response capability, life protection capability, basic needs and service capability for disaster-stricken people, property and environmental protection capability, danger elimination capability, and recovery and reconstruction capability. The selection of evaluation elements at the element layer covers categories such as risk identification, risk assessment, risk prevention, government supervision, technological support, material support, personnel support, monitoring layout, timeliness of monitoring information, accuracy of monitoring information, release of early warning information, accuracy of early warning information, emergency command and decision-making, emergency support and coordination, emergency material support, medical rescue, environmental pollution prevention and control, disease prevention and control in disaster areas, reconstruction compensation, and recovery and reconstruction time. Although the evaluation elements in the qualitative indicator system contain quantitative information or objective data, the system does not directly use raw data for quantitative model calculation. Instead, qualitative element scores are formed through scoring rules, data verification, judgment of system completeness, evaluation of allocation rationality, and comprehensive expert judgment. The qualitative and quantitative indicator systems establish semantic correspondence or mapping relationships at the dimension and capability levels to ensure that the quantitative and qualitative evaluation results are comparable and integrable under the fusion evaluation mode; wherein, the mapping relationship is a one-to-one correspondence, or a one-to-many or many-to-one correspondence.

6. The quantitative-qualitative adaptive natural disaster prevention and control capability assessment method based on data quality scoring according to claim 5, characterized in that, In step S2, for the data in the feature layer, the data completeness Dc, data timeliness Dt, data accuracy Da, and data consistency Con are calculated respectively, and the comprehensive data quality score Dq is calculated, including the following steps: S21. Data completeness Dc Data completeness measures the ratio between the actual data items collected and the data items that should have been collected, reflecting the degree of missing basic data. Its calculation formula is as follows: in: This represents the number of valid data items actually collected. This represents the total number of data items required for the feature layer. S22. Data Timeliness Dt Data timeliness measures the frequency and freshness of data updates, and is calculated using the following formula: in: This is the time difference between the current assessment time and the last data update time. For the data baseline update cycle, This is the time decay coefficient, used to represent the degree of impact of data aging on the reliability of the assessment; S23. Data Accuracy (Da) Data accuracy measures the degree of conformity between collected data and authoritative reference values. It is calculated using sampling data error verification methods, and its expression is: in: This refers to the number of qualified data items in the sampled data whose error is within the allowable range. This represents the total number of data items used in the sampling verification. S24. Data Consistency Con Data consistency measures the degree of coordination between data from different sources. When the same indicator has multiple data sources, consistency is assessed by calculating the degree of deviation between the data from different sources. The formula is as follows: in: The value of this indicator provided by the j-th data source. For the number of data sources, This is the average of data from all sources. A higher consistency score is achieved when the deviation between data from different sources is small; a lower consistency score is achieved when the data differences are large. S25. Overall Data Quality Score Dq After obtaining the scores for each dimension, the overall data quality score is calculated using a weighted summation method, and its expression is: in: Score the data completeness. To score the timeliness of the data, Data accuracy score Score the data consistency. These are the weighting coefficients for each dimension. .

7. The quantitative-qualitative adaptive natural disaster prevention and control capability assessment method based on data quality scoring according to claim 6, characterized in that, For real-time monitoring indicators =10~15; for static attribute type indicators =5~8; By default: .

8. The quantitative-qualitative adaptive natural disaster prevention and control capability assessment method based on data quality scoring according to claim 6, characterized in that, In step S3, the evaluation mode is selected based on the comprehensive data quality score Dq. when Enter quantitative assessment mode; when Enter qualitative assessment mode; when Entering a quantitative-qualitative integrated evaluation mode; in: To quantitatively assess the trigger threshold, the default value is 75 points; The threshold for qualitative assessment is set to 50 points by default; and it must meet the following conditions: .

9. The quantitative-qualitative adaptive natural disaster prevention and control capability assessment method based on data quality scoring according to claim 8, characterized in that, In step S3, S31. When the overall data quality score meets the requirements At that time, a quantitative evaluation method was adopted, and a quantitative indicator system was invoked to obtain quantitative evaluation results. : Let the original value of the k-th quantitative element in the j-th capability layer under the h-th dimension be... After quantitative calculation and standardization, the score of the kth quantitative element is obtained. The standardization process transforms raw data of different dimensions and scales into a unified scoring range of 0 to 100 points. Let the weights of each quantitative element within the capability layer be . And satisfy: in, Let be the total number of quantitative elements contained in the j-th capability layer under the h-th dimension layer. Weights for each quantitative element The quantitative score of the j-th capability layer under the h-th dimension layer is: in, Let be the total number of quantitative elements contained in the j-th capability layer under the h-th dimension layer. Assign weights to each quantitative element. Score for the kth quantitative element Let the weights of each capability layer under the h-th dimension be... And satisfy: in, Let h be the total number of capability layers contained in the h-th dimension layer. The weights of each capability layer under the h-th dimension are... The quantitative score for the h-th dimension is: in, Let h be the total number of capability layers contained in the h-th dimension layer. Let h be the weights of each capability layer under the h-th dimension. The quantitative score of the j-th capability layer under the h-th dimension layer. Let the weights of each dimension be... And satisfy: Where H represents the number of dimensional layers in the quantitative indicator system. Weights for each dimension layer The final quantitative assessment result is as follows: Where H represents the number of dimension layers in the quantitative indicator system. For the weights of each dimension layer, The quantitative score for the h-th dimension layer; S32. When the overall data quality score meets the requirements At that time, a qualitative evaluation method was adopted, and a qualitative indicator system was invoked to obtain the qualitative evaluation results. : Let the k-th qualitative element in the j-th capability layer under the h-th dimension be rated by p experts, and the t-th expert's rating be... Then the average score of this qualitative element is: in, The total number of experts participating in the scoring of a single qualitative element. Rate by experts Let the weights of each qualitative element within the capability layer be . And satisfy: in, Let be the total number of qualitative elements contained in the j-th capability layer under the h-th dimension layer. Weights of each qualitative element within the capability layer Then the qualitative score of the j-th capability layer under the h-th dimension layer is: in, Let be the total number of qualitative elements contained in the j-th capability layer under the h-th dimension layer. The weights of each qualitative element within the capability layer, The average score of this qualitative element Let the weights of each capability layer under the h-th dimension be... And satisfy: in, Let h be the total number of capability layers contained in the h-th dimension layer. The weights of each capability layer under the h-th dimension are... The qualitative score for the h-th dimension is: in, Let h be the total number of capability layers contained in the h-th dimension layer. Let h be the weights of each capability layer under the h-th dimension. The qualitative score of the j-th capability layer under the h-th dimension layer. Let the weights of each dimension be... And satisfy: in, This represents the total number of dimension layers in the qualitative indicator system. Weights for each dimension layer The final qualitative assessment result is: Where: H' represents the number of dimension layers in the qualitative indicator system. For the weights of each dimension layer, The qualitative score for the h-th dimension. S33. When the overall data quality score meets the requirements This indicates that the data quality of the evaluated object is in the middle range, meaning that some evaluation elements have the conditions for quantitative calculation, but there are still situations where supplementary evaluation is needed through expert judgment, system verification, or on-site inspection. Therefore, a quantitative-qualitative integrated evaluation model is adopted. In the aforementioned integrated evaluation mode, the system calculates quantitative evaluation results based on a quantitative indicator system. And based on the qualitative indicator system, the qualitative evaluation results are calculated. Then, the quantitative evaluation results are processed according to a preset fusion formula. and the qualitative assessment results Weighted fusion is performed to obtain the final evaluation result R; The fusion formula is as follows: in: The weighting coefficients for the quantitative evaluation results. Let be the weighting coefficients of the qualitative evaluation results, and satisfy: ; The weighting coefficient and Pre-set according to the needs of the assessment task.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored computer program, wherein, when the computer program is executed, it controls the device on which the computer-readable storage medium is located to perform the method as described in any one of claims 1-9.

Images