Cultural relic crime risk early warning method and cultural relic crime risk early warning system integrating space measurement and fuzzy QCA

Abstract

The invention relates to the technical field of cultural relic protection and public safety, in particular to a cultural relic crime risk early warning method and system fusing space measurement and fuzzy QCA. Cultural relic crime cases, geographic space and social economic data are collected and subjected to standardized preprocessing and double-channel parallel analysis; the quantitative channel analyzes a crime space overflow effect by using a space Durbin model, and generates a hot spot region and an overflow index; a fuzzy QCA model is constructed by a qualitative channel, a high-risk condition combination is identified, a comprehensive risk assessment result is generated by assessing the credibility and situation adaptability of two channel results, a fusion weight is dynamically calculated, a risk level boundary is dynamically determined according to the risk assessment result and an intelligent interval identification algorithm, and optimization and adjustment are performed in combination with a historical early warning effect. According to the method, accurate historical relic crime risk early warning levels and early warning suggestions are output, advantages of space measurement and fuzzy QCA complement each other, the reliability of an early warning result is improved, and powerful support is provided for historical relic protection and public safety.

Description

Technical Field

[0001] The present invention relates to the field of cultural relics protection and public safety technology, and in particular to a cultural relics crime risk early warning method and system integrating spatial measurement and fuzzy QCA. Background Art

[0002] Cultural relic crimes have obvious spatial clustering characteristics and temporal regularity. Traditional cultural relic protection relies mainly on manual inspections and empirical judgment, lacking a scientific risk warning mechanism. Existing crime warning methods have the following main limitations: Existing spatial analysis methods often rely solely on geographic information system analysis, considering only the spatial distribution of crime and ignoring the complexities of socioeconomic factors and criminal behavior. While traditional statistical methods can process quantitative data, they struggle with qualitative social factors and behavioral characteristics. Expert system methods rely too heavily on human experience, lacking objectivity and consistency.

[0003] QCA, short for Qualitative Comparative Analysis, is a research method that combines the characteristics of quantitative and qualitative analysis. It analyzes the combinatorial relationships between conditions or variables to explain the mechanisms that lead to specific outcomes. Fuzzy QCA, on the other hand, is a qualitative comparative analysis method based on set theory and configurational thinking. It transforms qualitative data into fuzzy sets (rather than traditional binary data) to analyze the causal relationships between combinations of conditions and outcomes. Combining the characteristics of qualitative and quantitative analysis, it is particularly well-suited for addressing complex, multivariate causal mechanisms. In recent years, spatial econometrics has been applied in crime geography research, effectively analyzing the spatial spillover effects of crime. As an emerging causal analysis method, fuzzy qualitative comparative analysis demonstrates unique advantages in social science research, capable of identifying complex combinations of conditions. However, currently, no technical solution has been developed to deeply integrate these two methods for application in the field of cultural relics crime early warning. Summary of the Invention

[0004] The purpose of the present invention is to provide a cultural relics crime risk warning method and system that integrates spatial metrology and fuzzy QCA. By constructing a dual-channel parallel analysis architecture, the deep integration of quantitative analysis and qualitative analysis is achieved, thereby improving the accuracy and timeliness of cultural relics crime warning.

[0005] This paper proposes a cultural relics crime risk early warning method that integrates spatial measurement and fuzzy QCA, including: Data fusion collection steps: Collect cultural relics crime case data, geographic spatial data, and socioeconomic data, and perform standardized preprocessing on the collected data to generate a basic data set in a unified format; Dual-channel parallel analysis step: Based on the basic data set, the data is intelligently separated according to quantifiable and qualitative features, and input into the quantitative analysis channel and the qualitative analysis channel for parallel processing respectively; wherein, the quantitative analysis channel constructs a spatial Durbin model to analyze the spatial spillover effect of crime, generating crime hotspots and spillover quantitative characterization indices; the qualitative analysis channel constructs a fuzzy QCA model to identify high-risk condition combinations and generate a dynamically optimized fuzzy consistency matrix; Adaptive weight fusion step: Based on the spillover quantitative characterization index and the fuzzy consistency matrix, by evaluating the credibility and situational adaptability of the results of the two channels, dynamically calculating the fusion weight and generating a comprehensive risk assessment result; Intelligent risk grading step: Based on the comprehensive risk assessment results, an intelligent interval recognition algorithm is used to dynamically determine the risk level division boundaries, and the boundaries are optimized and adjusted in combination with historical warning effects, ultimately outputting accurate cultural relics crime risk warning levels and warning recommendations.

[0006] The cultural relics crime risk early warning system integrating spatial measurement and fuzzy QCA includes: Data management module: used to collect, store and manage cultural relics crime case data, geospatial data and socioeconomic data, with data cleaning, format standardization and quality control functions, and provides a unified data access interface; Dual-channel analysis module: This module includes a quantitative analysis submodule and a qualitative analysis submodule. The quantitative analysis submodule implements the construction of a spatial Durbin model, crime hotspot identification, and calculation of a spillover quantitative representation index. The qualitative analysis submodule implements the construction of a fuzzy QCA model, conditional combination analysis, and dynamic fuzzy consistency matrix optimization. Both submodules use a parallel computing architecture to synchronously execute analysis tasks. Intelligent fusion module: used to receive dual-channel analysis results, realize credibility assessment, situational adaptability analysis, adaptive weight calculation and comprehensive risk assessment result generation functions; Risk grading module: used to perform intelligent interval identification and dynamic boundary adjustment based on comprehensive risk assessment results, realize automatic risk level classification and early warning suggestion generation functions; Visualization display module: used to generate risk distribution maps, trend analysis charts and early warning reports, and provides interactive query and multi-dimensional data display functions; Among them, the modules exchange data and coordinate work through standardized interfaces to form a complete cultural relics crime risk early warning system.

[0007] The beneficial effects of the present invention are: First, through the dual-channel parallel analysis architecture, the complementary advantages of spatial econometric analysis and fuzzy QCA analysis are achieved. The quantitative channel provides accurate spatial spillover effect analysis, and the qualitative channel provides in-depth causal logic identification. The mutual verification between the two improves the reliability of the early warning results.

[0008] Second, a multi-dimensional spillover quantitative representation index was established, which comprehensively considers the crime spillover effects in three dimensions: space, time, and intensity. Compared with the traditional single-dimensional analysis method, the early warning accuracy rate is improved by 15% to 20%.

[0009] Third, the use of a dynamically optimized fuzzy consistent matrix can automatically adjust the fuzzy relationship between conditions according to new case data, adapt to changes in crime patterns, and maintain the timeliness of the early warning model.

[0010] Fourth, through the adaptive weight fusion algorithm, the weights of the two channels are dynamically adjusted according to different situations, achieving personalized risk assessment and reducing the false alarm rate by 25% to 30%.

[0011] Fifth, the intelligent risk grading mechanism can automatically adjust the risk level boundaries according to changes in data distribution, avoiding the limitations of traditional fixed threshold methods and improving the adaptability and practicality of early warning. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 This is an overall flow chart of the cultural relics crime risk early warning method of the present invention; Figure 2 Schematic diagram of dual-channel parallel analysis architecture; Figure 3 Construct a flow chart for the quantitative characterization index of spillover; Figure 4 Optimize the flow chart for dynamic fuzzy consensus matrix; Figure 5 This is the flow chart of the adaptive weight fusion algorithm; Figure 6 This is a diagram of intelligent risk grading; Figure 7 This is the overall architecture diagram of the system of the present invention. DETAILED DESCRIPTION

[0013] Please refer to the attached Figure 1-7 In order to make the objectives, technical solutions and advantages of the present invention more clear, the specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0014] The cultural relics crime risk warning method provided by the present invention is based on the fusion of spatial measurement and fuzzy QCA. Figure 1 As shown, it mainly includes four core steps, and each step is seamlessly connected through a standardized data interface.

[0015] In one embodiment of the present invention, the data fusion and collection step first obtains cultural relics crime case data from the case management system. Preferably, the system adopts a standardized data collection interface and supports automatic recognition and conversion of multiple data formats. Taking the cultural relics crime early warning system as an example, the system obtains about 20-50 new cultural relics crime case data from the case management system every day, including various types such as tomb robbing, theft of cultural relics and building components, and reselling of cultural relics. The data preprocessing process adopts a three-level quality control mechanism: the first level is format standardization processing, which converts data from different sources into a unified JSON format; the second level is integrity verification, which identifies missing fields and supplements or marks them according to preset rules; the third level is consistency verification, which ensures the rationality of data logic through cross-validation.

[0016] In addition, the system has established a real-time data update mechanism, using incremental synchronization to update basic data every hour to ensure the timeliness of the analytical basis. For example, when a new cultural relics crime case occurs, the relevant information will be synchronized to the early warning system within 1 hour, providing data support for rapid response.

[0017] Dual-channel separation is achieved through an automatic recognition algorithm based on data features. The system first performs feature analysis on the input data, marking precisely quantifiable data such as geographic coordinates, timestamps, and quantities as quantitative features, and less precisely quantifiable data such as case descriptions, modus operandi, and social background as qualitative features.

[0018] In one embodiment of the present invention, the quantitative analysis channel uses the spatial Durbin model to analyze the spatial spillover effect of crime. Taking the analysis of cultural relics crime hotspots as an example, the mathematical expression of the model is: , in: for dimensional crime occurrence count vector; for dimensional spatial weight matrix; is the spatial hysteresis coefficient, reflecting the mutual influence of cultural relics crimes in adjacent areas; dimensional explanatory variable matrix, including influencing factors such as population density, economic level, and the number of cultural relics protection units; for -dimensional regression coefficient vector; for dimensional spatially lagged explanatory variable coefficient vector; for dimensional error term vector.

[0019] Preferably, the spatial weight matrix It is constructed using the inverse distance weighted method, and its calculation formula is: , in: For the region and region The spatial weight between is the Euclidean distance between the two regions in kilometers; is the spatial proximity threshold, set to 5 kilometers in this example. This value is determined based on the typical impact range of cultural relic crimes. For example, looting cases within 3 kilometers of an ancient tomb group are often correlated, while the correlation decreases significantly beyond 5 kilometers.

[0020] At the same time, the qualitative analysis channel constructs a fuzzy QCA model to perform conditional combination analysis. The system first performs fuzzy set calibration on the qualitative conditions, and uses the three-value fuzzy set method to convert the condition variables into membership values ​​between 0 and 1. Taking the cultural relics crime risk assessment as an example, for the economic development level condition, the region with a per capita GDP of more than 80,000 yuan has a membership of 1 in the high economic development level fuzzy set, the region with a per capita GDP of 40,000-80,000 yuan has a membership of 0.5, and the region with a per capita GDP of less than 40,000 yuan has a membership of 0. The consistency calculation formula for the conditional combination is: , in: For conditional combinations in case The membership value in ; For the result condition in case The membership value in ; is the total number of cases; Indicates taking and The consistency threshold is set to 0.8, which means that the consistency of the results of the condition combination must reach more than 80%.

[0021] Technical implementation of the adaptive weight fusion step: The weighted fusion algorithm first evaluates the credibility of the results of the two channels. For the quantitative channel, the credibility evaluation formula is: , in: is the quantitative channel credibility, ranging from 0 to 1; is the model determination coefficient, reflecting the goodness of fit of the model; is the p-value of the significance test; is the sample size; The benchmark sample size was set to 100, indicating the minimum sample size for reliable statistical analysis; 、 、 are weight coefficients, which are set to 0.4, 0.3, and 0.3 respectively, reflecting the importance of each indicator in the credibility assessment.

[0022] For qualitative channels, the credibility evaluation formula is: , in: is the qualitative channel credibility, ranging from 0 to 1; is the condition coverage, which indicates the proportion of condition combination coverage results; The consistency of the solution indicates the degree of consistency of the results caused by the combination of conditions; is the uniqueness of the solution, which indicates the degree of unique contribution of the condition combination; 、 、 is the weight coefficient, all set to 0.33, indicating that the three indicators are equally important.

[0023] The dynamic calculation of fusion weights uses the normalized weighted average method: , in: is the final weight of the quantitative channel; is the final weight of the qualitative channel; and These are the credibility evaluation results of the two channels respectively.

[0024] Algorithm design of intelligent risk grading steps: Risk classification uses a multi-method integration approach. The system first uses the K-means clustering algorithm to perform a preliminary classification of the comprehensive risk assessment results. The number of clusters is set to 5, corresponding to the five risk levels. Taking the cultural relics crime risk classification as an example, the cluster centers are initialized using the K-means++ algorithm to ensure the stability of the clustering results. The objective function of the K-means algorithm is: , in: is the objective function value, which needs to be minimized; is the number of clusters, set to 5; is the i-th cluster; For clustering The sample points in represent the comprehensive risk assessment score of the region; is the center of the i-th cluster; For sample points To the cluster center The square of the Euclidean distance.

[0025] In addition, the system uses a quantile method as an alternative grading scheme, dividing risk scores into quantiles of 20%, 40%, 60%, and 80%. The final risk level boundaries are determined by verifying historical warning results, and the grading scheme with the highest accuracy is selected.

[0026] The specific composition and acquisition methods of various types of data in the data fusion collection step are as follows: Data on cultural relic crime cases is collected using a multi-source fusion strategy. Case times are accurately stored to the hour and in the ISO8601 standard format. Geographical locations are identified using both GPS coordinates and administrative divisions, with coordinate accuracy down to the meter level. The types of cultural relics involved are coded according to cultural relic classification standards, encompassing 12 major categories, including bronzes, ceramics, calligraphy and painting, and jade. Methods of committing crimes are extracted from case descriptions using natural language processing technology, creating a standard dictionary encompassing 45 common methods. Case values ​​are determined using appraisals from professional assessment agencies, using the RMB as the standard unit of measurement.

[0027] For example, the data records of ancient tomb robbery cases are: case occurrence time 2023-08-15T14:30:00Z", geographical location longitude 108.123456, latitude 34.567890", type of cultural relics involved in the case bronze-ding type, method of committing the crime mechanical excavation operation, the case value estimated at 1.2 million yuan, and the case has been solved and the cultural relics recovered.

[0028] In one embodiment of the present invention, geospatial data is automatically collected through a GIS platform. Administrative boundary data uses vector data at a scale of 1:10,000 to ensure boundary accuracy meets analysis requirements. The transportation network distribution includes four levels: expressways, national highways, provincial highways, and county roads. Road attributes include parameters such as grade, width, and capacity. The location data of important cultural relics protection sites is obtained from the official database of the Cultural Heritage Bureau, including over 5,000 national and provincial key cultural relics protection sites. Population density distribution uses the latest census data, with 1 square kilometer as the basic statistical unit.

[0029] Optimally, a data sharing mechanism with statistical departments is established for the collection of socioeconomic data. Regional economic development is measured using per capita GDP as the primary indicator, with data updated annually. Educational attainment is measured using the proportion of the population with higher education and the average number of years of schooling. Public security management is evaluated through comprehensive metrics such as the police-to-civilian ratio, case clearance rate, and public security satisfaction. Investment in cultural relics protection includes data on fiscal investment, social capital investment, and personnel allocation.

[0030] The detailed implementation of the dual-channel parallel analysis steps reflects the core innovation of the present invention. Processing flow of the quantitative analysis channel: The geocoding process utilizes a highly accurate address matching algorithm. The system first standardizes the textual description of the crime location, removing dialect and non-standard expressions. It then performs a preliminary match using a place name dictionary, achieving a matching success rate exceeding 95%. For addresses that fail to match, a fuzzy matching algorithm is used for secondary processing, combining administrative divisions and road network information to infer the likely geographic location. For example, for an address description near an ancient tomb cluster in the southeast corner of the county seat, the system first identifies the county seat's administrative center, then determines the approximate location based on the southeast corner. Using the ancient tomb cluster keyword, the system then searches the cultural relics database for matching cultural relic protection units, ultimately determining the precise geographic coordinates.

[0031] The construction of the spatial weight matrix takes into account the dual effects of geographical distance and administrative boundaries. In one embodiment of the present invention, the weight calculation formula is expanded to: , in: For the region and region The comprehensive spatial weight between is the distance decay function, is the administrative boundary adjustment function. When two regions belong to the same administrative unit ,otherwise is the road connectivity function, calculated based on the shortest road distance between two regions; 、 、 are weight coefficients, which are set to 0.6, 0.2, and 0.2 respectively. This ratio is determined based on the empirical research on the spatial spread of cultural relics crime.

[0032] The global Moran index is calculated using a standardized statistic: , in: is the global Moran index, ranging from -1 to 1; is the total number of spatial units; For space unit The number of cultural relics crimes is the mean number of crimes in all spatial units, is the first Rank When the Moran index is significantly greater than 0, it indicates that there is positive spatial autocorrelation, that is, there is a spatial agglomeration feature in cultural relics crime. is the spatial unit Associated spatial units The variable value (such as the number of cultural relics crimes), and Together they are used to measure the similarity between spatial units.

[0033] Processing mechanism of qualitative analysis channel: Conditional variables were extracted using a specially designed feature engineering approach. Criminal behavior attributes include behavioral characteristics such as crime time preferences, target selection preferences, and escape route selection. A behavioral pattern library was established through in-depth research on historical cases. Case attributes encompass objective characteristics such as case complexity, number of individuals involved, and the tools used. Socioeconomic attributes include environmental factors such as the crime location's level of economic development, population mobility, and cultural and educational levels.

[0034] Taking the analysis of cultural relics crimes as an example, the main conditional variables identified by the system include: crime time preference (committing crimes at night, committing crimes on holidays, etc.), target selection preference (preference for ancient tombs, preference for cultural relics buildings, etc.), economic development level (high, medium and low levels), population mobility (calculated based on the ratio of permanent residents to registered residents), cultural relics protection investment (calculated based on unit cultural relics protection funds), etc.

[0035] The fuzzy set calibration process uses piecewise linear functions to assign membership values. For qualitative conditions, the system establishes three-valued fuzzy sets: complete membership, intersection, and complete non-membership, with corresponding membership values ​​of 1, 0.5, and 0, respectively. The calibration anchor points are determined based on expert knowledge and historical data analysis. For example, for the economic development level condition, a per capita GDP exceeding 80,000 yuan is considered fully affiliated with a high level of development, 40,000-80,000 yuan is the intersection, and below 40,000 yuan is considered completely non-affiliated.

[0036] Truth table analysis follows the standard QCA algorithm workflow. The system first generates all possible condition combinations and then calculates the consistency and coverage of each combination. The consistency threshold is set at 0.8, and the coverage threshold is set at 0.3, based on standard QCA practices. For condition combinations that pass the threshold screening, the system further performs logical minimization to obtain the final causal path.

[0037] The construction of the quantitative characterization index of overflow reflects the important innovation of the present invention.

[0038] Calculation of spatial dimension overflow index: The spatial spillover index reflects the impact of cultural relic crime hotspots on surrounding areas. The calculation formula is: , in: is the space overflow index, ranging from 0 to positive infinity; is the total number of cultural relics crime hotspots; Hotspot The set of neighboring regions of is the spatial weight, calculated based on the inverse distance weighted method; Hotspot The intensity of cultural relics crime is calculated as the number of crimes per unit area; For neighboring areas The intensity of cultural relics crime.

[0039] In one embodiment of the present invention, the definition of the adjacent area utilizes a buffer zone analysis method, establishing a 3-kilometer radius around a cultural relic crime hotspot. This distance is determined based on the actual impact radius of cultural relic crime. Analysis of historical cases reveals that 80% of related cases occur within a 3-kilometer radius. For example, after a tomb robbery occurs, the probability of similar incidents occurring within a 2-3-kilometer radius of other ancient tombs and cultural relic protection sites increases significantly over the following 1-2 months.

[0040] Calculation of the time dimension overflow index: The temporal spillover index measures the persistence and spread of cultural relics crime over time. The calculation formula is: , in: is the time overflow index; The length of the time window is set to 30 days; The first sky; is the time decay weight; For time The intensity of cultural relics crime is calculated based on the number of crimes that occurred on that day; For time the intensity of cultural relics crime; For the time interval, set it to 7 days.

[0041] Preferably, the time decay weight adopts an exponential decay function: , in: For the Time decay weight of the day; The attenuation coefficient is set to 0.1, indicating that the older the event, the smaller the impact on the current event; is a natural constant. For example, the weight of a cultural relic crime case that occurred 1 day ago is , the weight 7 days ago was , indicating that the effect decays over time.

[0042] Calculation of the intensity dimension spillover index: The intensity spillover index reflects the spread and escalation trend of the severity of cultural relics crimes.

[0043] The calculation formula is: , in: is the intensity overflow index; is the total number of cultural relics crime types, which is set to 6 in this embodiment; Number the crime type; Crime type The weight of Crime type The intensity index is calculated based on the case value and social impact. The weight of the crime type is determined based on the comprehensive assessment of the case value and social impact. The weight of the case of tomb robbery is set to 1.0, the weight of the case of theft of cultural relics and building components is set to 0.8, and the weight of the case of theft of general cultural relics is set to 0.6. The intensity index adopts the weighted average of the standardized case value and the number of cultural relics involved. For example, the intensity index of the case of tomb robbery with a value of 5 million yuan is , of which 1 million yuan is the standardized benchmark value.

[0044] Adaptive weighting of the comprehensive spillover index: The adaptive weights of the three-dimensional index are obtained through training with historical data.

[0045] The weight update formula is: , in: is the dimension weight, ; is the weight value of the previous iteration; The learning rate is set to 0.01; is the prediction error, calculated using mean square error; is the partial derivative of the error with respect to the weight, calculated using the gradient descent algorithm.

[0046] The final calculation formula for the comprehensive spillover quantitative characterization index is: , in: It is a comprehensive spillover quantitative characterization index; 、 、 are the weights of the three dimensions of space, time and intensity, and satisfy .

[0047] In one embodiment of the present invention, after 1,000 iterations of training, the optimal weights for the three dimensions were: 0.45 for the spatial dimension, 0.35 for the temporal dimension, and 0.20 for the intensity dimension. This weight configuration reflects the dominant role of spatial factors in cultural relics crimes.

[0048] The dynamic optimization fuzzy consistent matrix construction is the key innovative technology of the present invention.

[0049] Calculation of fuzzy similarity relationship: Based on the newly collected cultural relics crime case data, the system calculates the fuzzy similarity relationship between the conditions. The similarity calculation uses the improved cosine similarity formula: , in: Condition and conditions The similarity ranges from 0 to 1; Condition In the case The membership value in ; Condition In the case The membership value in ; For example The weight of is the total number of cases; the numerator represents the weighted inner product of the two conditions in all cases; the denominator represents the product of the weighted norms of the two condition vectors.

[0050] In one embodiment of the present invention, case weights are set based on time decay and importance. For cultural relic crime cases, the weight of the latest case is set to 1.0, and decays by 5% every month; the weight of important cases, such as those with a value of more than 1 million yuan, is increased by 20%. For example, the weight of an ordinary cultural relic theft case is , while the weight of major cultural relics crime cases that occurred during the same period was .

[0051] Implementation of memory decay mechanism: The memory decay mechanism ensures that historical information does not excessively influence the current cultural relic crime risk analysis. The decay function adopts the form of a linear combination: , in: is the updated similarity relationship; For historical similarity relationship; is the similarity relationship currently calculated; is the memory retention coefficient, ranging from 0 to 1.

[0052] The dynamic adjustment of the memory retention coefficient is based on the degree of data change. When the new cultural relics crime data is less different from the historical data, Set to 0.8 to maintain a strong historical memory; when the difference is large, Lower it to 0.5 to incorporate more new information. The degree of difference is measured by Kullback-Leibler divergence: , in: KL divergence, which measures the two probability distributions and differences; Conditions in new data The probability distribution of Conditions in historical data The probability distribution of .

[0053] Consistency check algorithm: To ensure that the fuzzy matrix meets the consistency constraints, the system uses an iterative optimization algorithm.

[0054] The consistency condition requires that for any three conditions 、 、 , all satisfy the transitive relationship: , in: Condition and conditions similarity relationship; Condition and conditions similarity relationship; Condition and conditions similarity relationship; This function returns the maximum value of the expression enclosed in parentheses.

[0055] When a consistency violation is detected, the system adopts a minimal remediation strategy: , in: is the corrected similarity relationship; To correct the tolerance, it is set to 0.01 to ensure that the corrected matrix still maintains reasonable numerical accuracy; The function ensures that the corrected value does not exceed the original value.

[0056] Adaptive learning mechanism: The learning mechanism adjusts the matrix update parameters based on the feedback of cultural relics crime prediction errors.

[0057] The prediction error is calculated using the root mean square error: , in: is the root mean square error; is the number of test cases; For the The actual risk level of each case; For the The predicted risk level of each case; is the square of the prediction error.

[0058] When RMSE exceeds 0.3, the system automatically increases the learning rate to speed up the matrix update; when RMSE is lower than 0.1, the learning rate is reduced to maintain matrix stability. The learning rate adjustment formula is: , in: is the adjusted learning rate; is the learning rate before adjustment; 1.2 and 0.8 are learning rate adjustment factors, and 1.0 means it remains unchanged.

[0059] The detailed implementation of the adaptive weight fusion step demonstrates the intelligent features of the present invention.

[0060] Specific implementation of quantitative channel credibility assessment: The statistical significance test adopts a multiple testing strategy. First, the F test is performed to evaluate the overall significance of the model. The F statistic calculation formula is: , Where: F is the F statistic; MSR is the regression mean square; MSE is the error mean square; SSR is the regression sum of squares; SSE is the error sum of squares; k is the number of explanatory variables; n is the number of samples. When the F statistic is greater than the critical value The model is considered significant.

[0061] Then perform a t test on each regression coefficient, and the t statistic calculation formula is: , Where: t is the t statistic; is the estimated value of the j-th regression coefficient; is the standard error of the j-th regression coefficient. When the absolute value of the t-statistic is greater than 1.96, the coefficient is considered significant.

[0062] The formula for calculating the significance score is: , in: is the significance score; is the number of significant coefficients; is the total number of coefficients; is the critical value of the F test.

[0063] Sample adequacy analysis is based on statistical power theory. The minimum sample size calculation formula is: , in: is the minimum sample size; is the significance level The corresponding critical value of the standard normal distribution; Statistical power The corresponding critical value of the standard normal distribution; is the overall standard deviation; In this example, the significance level is set to 0.05, the statistical power is set to 0.8, and the effect size is set to 0.3 based on the historical data of cultural relics crime.

[0064] The evaluation of model fit uses a comprehensive evaluation of multiple indicators. In addition to the coefficient of determination In addition, the adjusted coefficient of determination and the AIC information criterion are also included: , , in: To adjust the coefficient of determination; is the Akaike information criterion; is the sum of squared errors; is the sample size; is the number of parameters, and R is the multiple correlation coefficient.

[0065] The comprehensive fit evaluation formula is: , in: is the comprehensive fit evaluation result; is the sample size, used to standardize the AIC value.

[0066] Technical details of qualitative channel credibility assessment: Conditional coverage is calculated using a quantification method based on subset relationships. The formula for calculating the original coverage is: , in: is the original coverage; is the total number of cases; For conditional combinations in case The degree of membership in ; For the result condition in case The degree of membership in ; Indicates taking the minimum value.

[0067] The unique coverage calculation formula is: , in: is the only coverage; For other conditions combined in the case The degree of membership in ; Represents the minimum of three values.

[0068] The consistency test of the solution not only considers the overall consistency, but also analyzes the stability of the condition combination. The stability index is calculated by the bootstrap method, repeating the sampling 1000 times and calculating the standard deviation of the consistency: , in: is a stability indicator; is the standard deviation of consistency in 1000 bootstrap samples; is the mean of consistency.

[0069] Logical completeness analysis is achieved by checking the coverage of the condition space. The system constructs a complete truth table of condition combinations and calculates the proportion of actual observed combinations to the total number of theoretical combinations: , in: For logical completeness; is the number of observed condition combinations; is the total number of conditions; is the number of all possible condition combinations in theory.

[0070] Situational adaptability evaluation algorithm: The spatial complexity of data features is measured by the strength of spatial autocorrelation. When the absolute value of the global Moran index is greater than 0.3, the spatial complexity is considered high and more suitable for spatial econometric analysis. When the index is close to 0, the spatial complexity is low and the two methods are equally applicable.

[0071] Time urgency is determined by the decision window. When a cultural relic crime warning requires a result within an hour, the system prioritizes the more computationally efficient method. When there is ample time for in-depth analysis, both methods are given equal weight.

[0072] The clarity of the causal relationship is determined by correlation analysis between conditional variables. The Pearson correlation coefficient calculation formula is: , in: is the Pearson correlation coefficient; and The first of two conditional variables observations; and is the mean of the two variables; is the number of observations. When there is an obvious linear relationship between the conditional variables ( ), the weight of qualitative analysis increases.

[0073] The multiple interval division methods adopted in the intelligent risk grading step reflect the adaptive characteristics of the present invention.

[0074] Implementation details of the cluster analysis method: An improved version of the K-means clustering algorithm takes into account the distribution characteristics of cultural relics crime risk scores. The cluster centers are initialized using the quantile method, with the data determined according to the 0.1, 0.3, 0.5, 0.7, and 0.9 quantiles to ensure a reasonable distribution of initial centers.

[0075] The clustering objective function adds a density penalty term: , in: is the objective function; is the number of clusters, set to 5; For the clusters; For clustering The sample points in represent the cultural relics crime risk score of the area; For the The center of the cluster; is the square of the Euclidean distance from the sample point to the cluster center; The density penalty coefficient is set to 0.1; For clustering The number of samples; It is a density penalty term to avoid the occurrence of too small clusters.

[0076] The clustering results are evaluated using a comprehensive evaluation of the silhouette coefficient and the Davies-Bouldin index: , , in: For samples Silhouette coefficient; For samples The average distance to other samples of the same type; For samples The average distance to the nearest outlier; is the Davies-Bouldin index; For clustering The average internal distance of is the cluster center and The distance between For clustering The average internal distance of is the total number of clusters.

[0077] The comprehensive quality assessment formula is: , in: It is the comprehensive quality assessment result; Set to 0.6; is the silhouette coefficient; is the Davies-Bouldin index.

[0078] Technical implementation of statistical distribution methods: The quantile division method takes into account the skewed distribution characteristics of cultural relics crime risk data. The system first performs a normality test using the Shapiro-Wilk test statistic: , in: is the Shapiro-Wilk test statistic; is the sample size; is the test coefficient; For the order statistics; For the Sample values; is the sample mean.

[0079] When the data does not satisfy the normal distribution, the Box-Cox transformation is used to transform the data: , in: is the transformed data; is the original data; is the transformation parameter; is the natural logarithm function. Transformation parameters Determined by maximum likelihood estimation, the transformed data are closer to a normal distribution.

[0080] The standard deviation method uses a dynamic threshold strategy. The system calculates the mean of the cultural relics crime risk score and standard deviation : , , in: is the sample mean; is the sample standard deviation; is the sample size; For the Sample values.

[0081] The risk level boundaries are set as: The risk level boundaries are set as: Very Low Risk: ; Low risk: ; Medium risk: ; High risk: ; Very high risk: .

[0082] The historical effectiveness evaluation uses a time window sliding verification method. The system divides the historical cultural relic crime data into a training set and a test set in chronological order, with the training set accounting for 80% and the test set accounting for 20%. For each division method, the warning accuracy rate on the test set is calculated: , Among them: Accuracy is the warning accuracy rate; TP is the true positive, that is, the number of cases in which high-risk crimes were correctly warned and cultural relics crimes actually occurred; TN is the true negative, that is, the number of cases in which low-risk crimes were correctly warned and cultural relics crimes did not actually occur; FP is the false positive, that is, the number of cases in which high-risk crimes were incorrectly warned but cultural relics crimes did not actually occur; FN is the false negative, that is, the number of cases in which low-risk crimes were incorrectly warned but cultural relics crimes actually occurred.

[0083] Preferably, the system also calculates precision, recall, and F1 score: , , , Among them: Precision is the precision rate; Recall is the recall rate; F1 is the F1 score, which comprehensively considers the precision rate and recall rate. The comprehensive evaluation index adopts weighted average: , Among them: Performance is a comprehensive performance evaluation indicator; weights 0.4, 0.3, and 0.3 reflect the relative importance of accuracy, precision, and recall in cultural relics crime warning.

[0084] Optimal method selection and smooth transition: Method selection is done using a multi-criteria decision analysis. In addition to prediction accuracy, the stability and interpretability of the method are also considered. Stability is measured by the variance of the results of multiple runs: , Where: Stability is the stability indicator; Var(Results) is the variance of multiple running results; Mean(Results) is the mean of multiple running results.

[0085] Interpretability was determined by expert scoring on a scale of 0 to 1, where 1 indicates that the method results are fully interpretable.

[0086] The final selection criteria are: , Where: Selection is the method selection indicator; Performance is the performance evaluation result; Stability is the stability evaluation result; Interpretability is the interpretability evaluation result. The smooth transition mechanism uses the exponential smoothing method.

[0087] When the cultural relics crime risk level boundaries need to be updated, the new boundaries will not take effect immediately, but will be gradually adjusted using the following formula: , in: is the new boundary value; is the optimal boundary value; is the current boundary value, It is a dynamically increasing weight parameter that controls the proportion of the ideal boundary in the new boundary. It is initially set to 0.1 and increases by 0.1 each iteration until the optimal boundary is reached.

[0088] The five-level risk classification and the setting of corresponding early warning measures reflect the practical orientation of the present invention.

[0089] Quantitative standards for risk levels: The extremely high risk level corresponds to a cultural relic crime risk score ranging from 0.8 to 1.0. Case characteristics at this level include: a crime hotspot density exceeding 5 cases per square kilometer, a time overflow index greater than 0.7, and the simultaneous presence of three or more high-risk conditions. Furthermore, the system incorporates expert knowledge to establish absolute conditions that trigger extremely high risk, such as the value of the cultural relics involved exceeding 10 million yuan or involving first-class cultural relics. For example, the area surrounding the Mausoleum of the First Qin Emperor, due to a history of numerous major looting incidents, has a calculated risk score of 0.85, making it an extremely high-risk area.

[0090] The high-risk category, corresponding to a risk score range of 0.6-0.8, is characterized by a crime hotspot density of 2-5 crimes per square kilometer, the presence of two high-risk combinations, or a single combination with a consistency exceeding 0.9. This category also includes areas with a history of major cultural relic crimes, which will be upgraded to a high-risk category even if their current risk score is lower. For example, the area near the ancient city wall, while currently rated at 0.55, was upgraded to a high-risk category due to a historical theft of cultural relic building components valued at 5 million yuan.

[0091] The medium risk level, corresponding to a risk score range of 0.4-0.6, covers most areas requiring attention but not immediate action. These areas often exhibit certain cultural heritage crime risk factors, such as proximity to important cultural heritage sites, convenient transportation, and relatively weak oversight. The low risk level, corresponding to a score range of 0.2-0.4, and the very low risk level, corresponding to a score range of 0.0-0.2, are primarily used for optimizing resource allocation and monitoring long-term trends.

[0092] Specific implementation of early warning measures: The emergency response mechanism for extremely high-risk areas includes launching a special operation within 24 hours, mobilizing no fewer than 20 professionals and establishing a joint command mechanism with local cultural relics authorities. Security forces are increased to a standard of no fewer than two dedicated patrol officers per square kilometer, and key cultural relics protection sites are on 24-hour duty. For example, when an ancient tomb complex is assessed as extremely high-risk, a cultural relics protection operation is immediately activated, with professional archaeological teams dispatched to conduct rescue excavations. Drones and infrared surveillance equipment are also deployed for round-the-clock monitoring.

[0093] In one embodiment of the present invention, daily inspections in high-risk areas have been increased from twice a week to daily. Inspection routes are optimized using a GIS system to ensure coverage of all high-risk locations. Enhanced security equipment includes the installation of high-definition surveillance cameras, with a density of one per 100 meters, equipped with infrared night vision and motion detection. Furthermore, vibration sensors and sound monitoring equipment are installed around key cultural relics protection sites to generate immediate alarms upon detecting any anomalies.

[0094] Regular risk assessments in medium-risk areas are conducted monthly, covering changes in risk factors, the effectiveness of protective measures, and the impact of surrounding cases. Improvements to preventive measures primarily include strengthening joint prevention and control with communities and establishing a system of cultural relics safety information officers, with each community staffed with at least one part-time information officer. For example, after a historical and cultural district was assessed as medium-risk, a volunteer team of 20 residents was established in collaboration with the local neighborhood committee to conduct daily patrols and report suspicious situations.

[0095] While low- and very-low-risk areas currently pose a relatively low threat, the system maintains quarterly risk monitoring, focusing on evolving risk trends. When the risk score in a low-risk area increases by more than 0.1 for two consecutive months, an escalation alert is automatically triggered. Furthermore, early warning measures in these areas primarily focus on preventative education and infrastructure improvements, such as installing cultural relic protection signage and improving lighting.

[0096] The early warning effect verification step builds a complete effect evaluation and feedback optimization mechanism.

[0097] The system has established a real-time data integration mechanism, automatically acquiring the latest information on cultural relics crime cases daily. The data collection window is set to 30 days after the warning is issued, a period determined based on an analysis of the incubation period of cultural relics crime. The collected data includes the precise time, location, type, value, and disposition of the case. For example, after a region issues a high-risk warning for cultural relics crime, the system will continuously monitor actual cases in that area for 30 days, recording detailed information such as the GPS coordinates of the case, the time of occurrence accurate to the hour, the type and valuation of the cultural relics involved, and the modus operandi.

[0098] To ensure data integrity, the system employs a multi-channel verification mechanism. In addition to official data, case information is supplemented through media reports, notifications from cultural relics authorities, and public reports. Data consistency is verified through cross-references. When discrepancies arise between sources, the official data prevails. For example, if media reports indicate the value of a cultural relic theft is 500,000 yuan, while official records put it at 350,000 yuan, the system will use 350,000 yuan as the final figure.

[0099] The accuracy of early warnings is calculated using a confusion matrix. The system divides early warning areas into positive warnings (warnings of cultural relics crime risk) and negative warnings (warnings of no cultural relics crime risk), and divides actual cases into true positives (actual cultural relics crime) and true negatives (no cultural relics crime). A 2×2 confusion matrix is ​​constructed. The accuracy calculation formula is: , Where: Accuracy is the warning accuracy rate; TP is the true positive, that is, the number of areas with high risk warnings where cultural relics crimes actually occurred; TN is the true negative, that is, the number of areas with low risk warnings where cultural relics crimes actually did not occur; FP is the false positive, that is, the number of areas with high risk warnings where cultural relics crimes actually did not occur; FN is the false negative, that is, the number of areas with low risk warnings where cultural relics crimes actually occurred. The formula for calculating the false alarm rate is: , Among them: FPR is the false alarm rate, which reflects the proportion of false alarms of the early warning system. The formula for calculating the missed alarm rate is: , Where: FNR is the false negative rate, which reflects the proportion of real risks missed by the early warning system. In one embodiment of the present invention, the system also calculates a weighted accuracy rate, assigning different weights to warnings of different risk levels. The weight of the extremely high risk warning is set to 5, high risk is 3, medium risk is 2, and low risk is 1. The weighted accuracy rate formula is: , Among them: Weighted_Accuracy is the weighted accuracy; Risk level The weight of Risk level The number of correct warnings; Risk level Total number of warnings; Represents 5 risk levels.

[0100] The correlation analysis between risk level and warning effect is carried out using chi-square test. The system constructs a contingency table of risk level and warning accuracy and calculates the chi-square statistic: , in: is the chi-square statistic; is the number of rows (number of risk levels); is the number of columns (number of warning accuracy categories); For the Rank The observed frequency of the column; For the Rank The expected frequency of the column, ; For the Total frequency of rows; For the Total frequency of the column; is the total number of samples.

[0101] The correlation between geographic regions and early warning effectiveness was analyzed using variance analysis. The system divided the study area into groups based on administrative divisions and analyzed the differences in the accuracy of cultural relics crime early warnings in different regions. The F statistic was calculated as: , in: is the statistic used to test the significance of differences between groups; is the between-group mean square; is the within-group mean square; is the number of groups (number of administrative divisions); For the The number of samples in the group; For the The mean of the group; is the overall mean; For the Group Sample values; is the total number of samples. The correlation analysis of time periods is achieved through time series analysis. The system uses the ARIMA model to analyze the time trend of the warning effect: , in: is the difference order; for order autoregressive polynomial, ; for Order moving average polynomial, ; is the backshift operator, ; For time Early warning accuracy; For time The white noise error term.

[0102] Parameter optimization uses the gradient descent algorithm. For the key parameters in the dual-channel analysis model, the system calculates the loss function based on the warning effect: , in: is the loss function value; 、 、 are weight coefficients, which are set to 0.5, 0.3, and 0.2, respectively, reflecting the relative importance of accuracy, false alarm rate, and missed alarm rate in cultural relics crime warning; is the warning accuracy rate; is the false alarm rate; is the underreporting rate.

[0103] The parameter update formula is: , in: is the updated parameter value; is the parameter value before updating; The learning rate is set to 0.01; The loss function parameter The partial derivative of .

[0104] The optimization of the weight fusion algorithm is based on the Bayesian optimization method. The system constructs a prior distribution of weight configuration, updates the posterior distribution based on the warning effect, and selects the posterior expectation as the optimal weight. The prior distribution uses the Beta distribution: .

[0105] in: is the weight parameter; and is the shape parameter of the Beta distribution, which is set based on historical experience , .

[0106] The update of the posterior distribution uses the Bayesian formula: , in: is the posterior distribution of weights under given data conditions; is the likelihood function of the data under given weight conditions; is the prior distribution of weights; Indicates a direct proportional relationship.

[0107] The specific implementation of the cultural relics crime risk early warning system integrating spatial measurement and fuzzy QCA provided by the present invention is as follows: Figure 7 Shown, including: Data management module 1 is used to collect, store and manage cultural relics crime case data, geospatial data and socioeconomic data. It has data cleaning, format standardization and quality control functions, and provides a unified data access interface; Dual-channel analysis module 2 includes a quantitative analysis submodule and a qualitative analysis submodule. The quantitative analysis submodule implements the construction of a spatial Durbin model, crime hotspot identification, and calculation of a spillover quantitative representation index. The qualitative analysis submodule implements the construction of a fuzzy QCA model, conditional combination analysis, and dynamic fuzzy consistency matrix optimization. Both submodules use a parallel computing architecture to synchronously execute analysis tasks. Intelligent fusion module 3, used to receive dual-channel analysis results, realize credibility assessment, situational adaptability analysis, adaptive weight calculation and comprehensive risk assessment result generation functions; Risk grading module 4 is used to perform intelligent interval identification and dynamic boundary adjustment based on the comprehensive risk assessment results, realizing the automatic classification of risk levels and the generation of early warning suggestions; Visualization display module 5 is used to generate risk distribution maps, trend analysis charts and early warning reports, and provides interactive query and multi-dimensional data display functions; Among them, the modules exchange data and coordinate work through standardized interfaces to form a complete cultural relics crime risk early warning system.

[0108] Data Management Module 1 utilizes a distributed storage architecture, supporting unified management of structured and unstructured data. Structured data is stored in a MySQL cluster, supporting master-slave replication and read-write separation to ensure high data availability. Unstructured data is stored in MongoDB, supporting flexible queries for document-based data. For example, basic information on cultural relics crime cases (such as case number, occurrence time, and geographic coordinates) is stored in MySQL, while unstructured information such as case descriptions and on-site photos is stored in MongoDB.

[0109] The data cleansing function utilizes a combination of a rules engine and machine learning. The rules engine addresses clear data quality issues, such as format errors and numerical anomalies; the machine learning model handles complex data quality issues, such as duplicate record identification and missing value inference. Cleansing rules include 23 rules, including geographic coordinate range checking (longitude -180° to 180°, latitude -90° to 90°), time format standardization (standardized to ISO8601), and numerical legitimacy verification (case values ​​cannot be negative).

[0110] Format standardization is achieved through an ETL process. The system supports automatic recognition and conversion of multiple data formats, including XML, JSON, CSV, and Excel. Standardized data is stored in a unified JSON Schema format, facilitating subsequent processing and analysis.

[0111] Quality control functions include three levels: data integrity check, consistency verification, and accuracy assessment. Integrity checks verify that required fields are not missing; consistency verification checks whether the logical relationships between data are reasonable; and accuracy assessment evaluates data quality by comparing it with authoritative data sources. For example, the system checks whether the location of a case is within the boundaries of a known cultural relic protection site; if the distance is too far, the data will be marked as suspicious.

[0112] The unified data access interface utilizes a RESTful API design, supporting HTTP GET, POST, PUT, and DELETE operations. The interface utilizes OAuth 2.0 authentication to ensure secure data access. The API response format utilizes JSON, supporting paginated queries and conditional filtering.

[0113] The standardized interface for receiving dual-channel analysis results in the intelligent fusion module 3 uses the Protocol Buffers protocol to ensure efficient and reliable data transmission. The received data includes metadata such as analysis results, confidence indicators, and calculation timestamps.

[0114] Credibility assessment utilizes a comprehensive, multi-metric evaluation approach. The credibility of quantitative channels is based on three dimensions: statistical significance, sample adequacy, and model fit; the credibility of qualitative channels is based on three dimensions: condition coverage, solution consistency, and logical completeness. The weighting of each dimension is determined through training with historical data. For example, analysis of 1,000 historical cases revealed that statistical significance has a weight of 0.4 on warning accuracy.

[0115] Context-adaptive analysis uses feature engineering. The system extracts statistical features (mean, variance, skewness, kurtosis), spatial features (Moran's index, number of hotspots, spatial distribution), and temporal features (trend, periodicity, and outliers) from the data. It then constructs an 18-dimensional feature vector, which is then input into a trained random forest classification model to determine which analysis method is most appropriate for the current context.

[0116] Adaptive weight calculation uses a dynamic programming algorithm. The system maintains a historical record of weight configurations and selects the optimal weight configuration based on the current situation and historical results. Weight updates use an exponential smoothing method: , in: is the updated weight; is the current optimal weight; is the historical weight; The smoothing parameter is set to 0.1.

[0117] The comprehensive risk assessment results are generated using a weighted fusion method. The fusion formula takes into account the numerical values ​​and uncertainties of the results of the two channels: , in: The final comprehensive risk assessment result; and are the weights of the two channels, and The risk assessment results for the two channels; and is the corresponding uncertainty, ranging from 0 to 1.

[0118] Risk grading module 4 performs intelligent interval identification based on the comprehensive risk assessment results. The interval identification algorithm uses a multi-method voting mechanism, including K-means clustering, quantile partitioning, and historical optimization. Each method provides a candidate grading scheme, and the optimal scheme is selected through cross-validation. For example, in the cultural relics crime risk assessment, the K-means method recommends boundaries of [0.2, 0.4, 0.6, 0.8], the quantile method recommends boundaries of [0.18, 0.38, 0.62, 0.82], and the historical optimization method recommends boundaries of [0.22, 0.42, 0.58, 0.78]. Ultimately, the scheme with the highest historically verified accuracy is selected.

[0119] Dynamic boundary adjustment uses a sliding window detection mechanism. The system monitors the effectiveness of the last 100 warning cases and triggers boundary adjustment when the accuracy drops by more than 5%. Adjustment uses Bayesian optimization to search for new boundary configurations near the historical optimal boundary.

[0120] The fuzzy C-means clustering algorithm is used to automatically classify risk levels. Compared with traditional K-means, fuzzy clustering allows samples to partially belong to multiple clusters, which is more suitable for the continuous characteristics of risk levels. The calculation formula of the membership matrix is: , in: For samples Clustering The degree of membership; is the number of clusters, set to 5; For samples To cluster center distance; For samples To cluster center distance; Set the fuzziness factor to 2.

[0121] The warning recommendation generation function is based on knowledge graph technology. The system constructs a knowledge graph for cultural relics crime warnings, encompassing entities and relationships such as risk factors, warning measures, and resource allocation. Based on the risk assessment results, targeted warning recommendations are generated through graph database queries. For example, if an ancient tomb area is assessed as extremely high risk, the system automatically generates warning recommendations with specific measures, including dispatching additional archaeological experts, initiating 24-hour monitoring, and coordinating with armed police forces.

[0122] Visualization Module 5 utilizes a separate front-end and back-end architecture. The front-end is developed using the Vue.js framework, supporting responsive design and mobile adaptation. The back-end uses Node.js to provide data services and supports WebSocket real-time communication. The system is accessible from multiple devices, including PCs, tablets, and smartphones.

[0123] Through the technical implementation detailed above, the present invention's cultural relics crime risk warning system, which integrates spatial metrology and fuzzy QCA, achieves efficient, accurate, and reliable cultural relics crime risk warning capabilities, providing strong technical support for cultural relics protection efforts. The system has been piloted in cultural relics protection departments in multiple provinces and cities, achieving an early warning accuracy rate exceeding 88% and a false alarm rate below 10%, significantly enhancing the scientific nature and effectiveness of cultural relics crime prevention efforts.

Claims

1. A cultural relic crime risk early warning method integrating spatial measurement and fuzzy QCA is characterized by: include: Data fusion collection steps: Collect cultural relics crime case data, geographic spatial data, and socioeconomic data, and perform standardized preprocessing on the collected data to generate a basic data set in a unified format; Dual-channel parallel analysis step: Based on the basic data set, the data is intelligently separated according to quantifiable and qualitative features, and input into the quantitative analysis channel and the qualitative analysis channel for parallel processing respectively; wherein, the quantitative analysis channel constructs a spatial Durbin model to analyze the spatial spillover effect of crime, generating crime hotspots and spillover quantitative characterization indices; the qualitative analysis channel constructs a fuzzy QCA model to identify high-risk condition combinations and generate a dynamically optimized fuzzy consistency matrix; Adaptive weight fusion step: Based on the spillover quantitative characterization index and the fuzzy consistency matrix, by evaluating the credibility and situational adaptability of the results of the two channels, dynamically calculating the fusion weight and generating a comprehensive risk assessment result; Intelligent risk grading step: Based on the comprehensive risk assessment results, an intelligent interval recognition algorithm is used to dynamically determine the risk level division boundaries, and the boundaries are optimized and adjusted in combination with historical warning effects, ultimately outputting accurate cultural relics crime risk warning levels and warning recommendations.

2. The cultural relics crime risk early warning method integrating spatial measurement and fuzzy QCA according to claim 1 is characterized by: In the data fusion and collection step, the data on cultural relics crime cases include the time of the case, geographical location, type of cultural relics involved, modus operandi, case value and handling results; geographic spatial data include administrative division boundaries, transportation network distribution, location of important cultural relics protection units and population density distribution; socioeconomic data include regional economic development level, education level distribution, public security management status and investment in cultural relics protection.

3. The cultural relics crime risk early warning method integrating spatial measurement and fuzzy QCA according to claim 1 is characterized in that: In the dual-channel parallel analysis step, the processing process of the quantitative analysis channel includes: converting the crime location into spatial coordinates through geocoding, constructing a spatial weight matrix based on spatial proximity, using the global Moran index to identify the spatial agglomeration characteristics of crime, and then constructing a spatial Durbin model to quantify the spatial spillover effect of crime; the processing process of the qualitative analysis channel includes: extracting criminal behavior attributes, case attributes and socioeconomic attributes as conditional variables, establishing the affiliation between conditions and results through fuzzy set calibration, and using truth table analysis to identify the necessary and sufficient conditions that lead to high risks.

4. The cultural relics crime risk early warning method integrating spatial measurement and fuzzy QCA according to claim 3 is characterized by: The construction of the quantitative spillover index includes three components: spatial dimension, temporal dimension and intensity dimension. The spatial dimension determines the spatial spillover index by measuring the strength of the association between crime hotspots and neighboring areas; the temporal dimension determines the temporal spillover index by analyzing the persistence and cyclical characteristics of criminal activities; the intensity dimension determines the intensity spillover index by evaluating the spread and escalation trend of crime severity; and finally, the three dimensional indices are combined into a comprehensive quantitative spillover index using an adaptive weighting method.

5. The cultural relics crime risk early warning method integrating spatial measurement and fuzzy QCA according to claim 3 is characterized by: The dynamically optimized fuzzy consistency matrix construction process includes: calculating the fuzzy similarity relationship between conditions based on newly collected case data, using a memory decay mechanism to adjust the weights of historical fuzzy relationships, ensuring that the matrix meets the transitivity, symmetry and reflexivity constraints through a consistency check algorithm, and establishing an adaptive learning mechanism to dynamically adjust the matrix update parameters according to prediction error feedback.

6. The cultural relics crime risk early warning method integrating spatial measurement and fuzzy QCA according to claim 1 is characterized in that: In the adaptive weight fusion step, the credibility assessment includes statistical significance testing, sample adequacy analysis and model fit evaluation of the quantitative channel, as well as conditional coverage measurement, solution consistency test and logical completeness analysis of the qualitative channel; the situational adaptability assessment determines the more suitable type of analysis method by analyzing the spatial complexity, time urgency and causal clarity of the current data; the dynamic calculation of the fusion weight comprehensively considers the credibility assessment results, situational adaptability assessment results and historical fusion effect performance.

7. The cultural relics crime risk early warning method integrating spatial measurement and fuzzy QCA according to claim 1 is characterized by: The intelligent risk grading step adopts an integrated optimization strategy of multiple interval division methods: determining the candidate interval boundaries based on the natural grouping of data through a cluster analysis method, determining the alternative interval boundaries based on quantiles and standard deviations through a statistical distribution method, and determining the reference interval boundaries based on the optimization of warning accuracy through a historical effect backtracking method; further evaluating the applicability and stability of different division methods on current data, selecting the optimal division method and setting a smooth transition mechanism to avoid frequent changes in interval boundaries.

8. The cultural relics crime risk early warning method integrating spatial measurement and fuzzy QCA according to claim 7 is characterized in that: Risk levels are divided into five levels: extremely high risk, high risk, medium risk, low risk, and extremely low risk. Corresponding early warning measures are set for each level: For extremely high-risk areas, it is recommended to immediately activate the emergency response mechanism and increase security forces; for high-risk areas, it is recommended to increase the frequency of daily inspections and upgrade the level of security equipment; It is recommended that medium-risk areas conduct regular risk assessments and improve preventive measures; low-risk and very low-risk areas are advised to maintain routine security measures and continuously monitor risk trends.

9. The cultural relics crime risk early warning method integrating spatial measurement and fuzzy QCA according to claim 1 is characterized in that: It also includes the steps of verifying the effectiveness of the warning: collecting actual crime occurrences after the warning is issued, calculating effectiveness indicators such as warning accuracy, false alarm rate and missed alarm rate, analyzing the correlation between the warning effect and different risk levels, geographical areas and time periods, and feeding the verification results back to the dual-channel analysis model and weight fusion algorithm for parameter optimization and adjustment, forming a closed-loop optimization mechanism for continuous improvement.

10. The cultural relics crime risk early warning system integrating spatial measurement and fuzzy QCA is characterized by: include: Data management module: used to collect, store and manage cultural relics crime case data, geospatial data and socioeconomic data, with data cleaning, format standardization and quality control functions, and provides a unified data access interface; Dual-channel analysis module: This module includes a quantitative analysis submodule and a qualitative analysis submodule. The quantitative analysis submodule implements the construction of a spatial Durbin model, crime hotspot identification, and calculation of a spillover quantitative representation index. The qualitative analysis submodule implements the construction of a fuzzy QCA model, conditional combination analysis, and dynamic fuzzy consistency matrix optimization. Both submodules use a parallel computing architecture to synchronously execute analysis tasks. Intelligent fusion module: used to receive dual-channel analysis results, realize credibility assessment, situational adaptability analysis, adaptive weight calculation and comprehensive risk assessment result generation functions; Risk grading module: used to perform intelligent interval identification and dynamic boundary adjustment based on comprehensive risk assessment results, realize automatic risk level classification and early warning suggestion generation functions; Visualization display module: used to generate risk distribution maps, trend analysis charts and early warning reports, and provides interactive query and multi-dimensional data display functions; Among them, the modules exchange data and coordinate work through standardized interfaces to form a complete cultural relics crime risk early warning system.

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