A method for quality control and evaluation of cave temple cave micro-environment monitoring data

Abstract

The application discloses a kind of grottoes micro-environment monitoring data quality control and evaluation method, specifically related to grottoes micro-environment monitoring data field, including obtaining grottoes micro-environment monitoring data, the space-time reconstruction of grottoes micro-environment monitoring data, establish the data expression basis with consistent attribute structure, provide execution entrance for subsequent processing;From the cross relationship of time rhythm and numerical change, identify potential abnormal fragments and remove abnormal points, ensure the effectiveness of grottoes micro-environment monitoring data value range;Identify the time stamp order confusion problem and restore the correct order structure combined with the trend law, ensure the consistency of time sequence.Through the construction of multi-stage closed loop processing chain with "rhythm change identification, numerical mutation removal, trend order recovery, structure quality fusion" as the core, realize the structure of monitoring data, abnormal identification, time sequence correction and credibility output, so as to systematically improve the effectiveness, consistency and application value of monitoring data.

Description

Technical Field

[0001] This invention relates to the field of microenvironment monitoring data technology in grottoes and caves, and more specifically, to a method for quality control and evaluation of microenvironment monitoring data in grottoes and caves. Background Technology

[0002] Currently, my country has a large number of grotto temple sites, and cave microenvironment monitoring systems have been widely deployed to achieve long-term data collection of key indicators such as temperature, relative humidity, and carbon dioxide inside the caves. However, most existing technologies are limited to data collection and display, lacking a systematic data quality management mechanism. This leads to frequent problems such as outliers, missing items, and mutation points in the monitoring data, which in turn makes it impossible to guarantee the validity and consistency of the data.

[0003] Meanwhile, since the monitoring data of the microenvironment of grottoes is generally presented in time series form, time errors and sequence disorder are prone to occur during the export process, which further aggravates the data structure chaos and affects the usability of analysis. Therefore, it is urgent to construct a systematic quality control and evaluation method for the monitoring data of the microenvironment of grottoes to achieve anomaly identification, time series correction and overall quality assessment, so as to fundamentally improve the credibility of the data and its subsequent application value. Summary of the Invention

[0004] To overcome the aforementioned deficiencies in the prior art, embodiments of the present invention provide a method for quality control and evaluation of microenvironment monitoring data in grottoes and caves. By constructing a multi-stage closed-loop processing chain centered on "rhythm change identification, numerical mutation elimination, trend sequence recovery, and structural quality fusion," the method achieves structural unification, anomaly identification, temporal correction, and reliability output of monitoring data. This systematically improves the effectiveness, consistency, and application value of the monitoring data, thereby solving the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a method for quality control and evaluation of microenvironment monitoring data in grottoes and caves, comprising:

[0006] Acquire cave microenvironment monitoring data, reconstruct the spatiotemporal nature of the cave microenvironment monitoring data, establish a data expression foundation with a consistent attribute structure, and provide an execution entry point for subsequent processing;

[0007] By identifying potential abnormal segments and eliminating abnormal points from the intersection of time rhythm and numerical changes, the validity of the value range of cave microenvironment monitoring data can be ensured.

[0008] Identify timestamp disorder issues and restore the correct sorting structure by combining trend patterns to ensure time sequence consistency;

[0009] Based on the evaluation dimensions of completeness, consistency, and rhythm, a data credibility level expression is formed, which is used to monitor the quantification of data value.

[0010] Under the premise of ensuring that the integrity rate of the cave microenvironment monitoring data fields meets the structural consistency judgment, the sorting and correction structure is subjected to format specification and field mapping operations to obtain the integrated data structure and output it.

[0011] In a preferred embodiment, cave microenvironment monitoring data is acquired, including temperature data, relative humidity data, and carbon dioxide concentration data. The cave microenvironment monitoring data undergoes a structure merging operation to perform a unified binding of the acquisition time field and the spatial location field, resulting in a spatiotemporal labeled dataset.

[0012] For each record in the spatiotemporal labeled dataset, the index field is extracted by aggregating by index type to obtain the index classification data column. For each type of data in the index classification data column, the sampling interval is identified by sorting the data over continuous time to obtain the initial time series structure.

[0013] In the initial time series structure, each record segment is evaluated for rhythm consistency by calculating the sampling frequency difference, resulting in a set of sampling rhythm states.

[0014] In a preferred embodiment, the sampling rhythm state set includes an interval change term and a local time difference sequence. The sampling rhythm state set is subjected to a threshold deviation identification mechanism to perform rhythm disturbance identification. If the interval fluctuation exceeds a preset interval fluctuation threshold range, an abnormal rhythm segment is output. The corresponding record in the abnormal rhythm segment is subjected to sliding difference processing in the index classification data column to extract the rate of change and obtain a rate of change sequence.

[0015] Each rate of change value in the rate of change sequence is compared with the historical change threshold template. If the deviation exceeds the upper or lower limit of the preset deviation threshold, a mutation record group is output.

[0016] In a preferred embodiment, each record in the mutation record group is combined with its sampling spatial location information and subjected to spatial continuity mapping to perform node isolation judgment. If it has no spatial continuity with neighboring records, it is judged as a spatial anomaly and removed.

[0017] If a mutation record does not meet the spatial anomaly removal criteria, it is determined whether it has exceeded the preset duration in the rhythm anomaly segment. If it does, it is marked as a fluctuation boundary point and retained.

[0018] In a preferred embodiment, the initial time series structure is subjected to time stamp increment detection and order verification. If timestamp reversal or duplicate items are detected, a time misorder index group is generated. For each record in the time misorder index group, trend matching and comparison are performed in combination with the rhythm distribution characteristics in the sampling rhythm state set to obtain a trend offset record set.

[0019] The overlap of adjacent windows between the trend offset record set and the continuous trend segments of the verified sequence is calculated. If the trend directions of the two are consistent and the slopes are similar, they are judged as repairable items and sequential merging is performed.

[0020] If the trend offset record fails to find a consistent trend direction in the continuous trend segment, a slope standardization operation is performed to construct a new rhythm template, and time series recovery is performed through perturbation sorting to obtain the sorting correction structure.

[0021] In a preferred embodiment, each type of index data in the sorting correction structure is subjected to integrity measurement after missing segment identification and sampling coverage calculation, and an integrity score column is output; the integrity score column and the mutation record group are subjected to time position intersection judgment. If the proportion of the intersection area exceeds the preset intersection area proportion threshold, it is marked as a low integrity segment.

[0022] The index data of the sorting correction structure are compared through a synchronous window to perform collaborative rate of change fitting. Those with fitting residuals less than the allowable deviation range constitute a consistent segment set. The consistent segment set is cross-judged with the sampling rhythm state set. If there is no overlap with the rhythm abnormal segment, it is marked as a high confidence data segment.

[0023] The integrity score, consistency fit, and rhythm intersection of each data segment are weighted by segment quality and aggregated to output a microenvironment data quality level vector.

[0024] In a preferred embodiment, the data quality level vector of the cave microenvironment monitoring data is combined with the field integrity rate in the sorting correction structure to perform a structural consistency judgment. If the field missing does not exceed the preset field missing threshold, it is judged to be an expandable data structure.

[0025] In the scalable data structure, each type of indicator data is generated by standard output after uniform formatting and field mapping, resulting in data integration results that can be used for anomaly tracing, trend prediction, and monitoring optimization.

[0026] In a preferred embodiment, a joint judgment is performed on the temporal rhythm disturbances and numerical abrupt changes in the cave microenvironment monitoring data. Simultaneously, the spatial location aggregation degree is used to determine whether the points are structurally isolated, resulting in a set of abnormal records to be removed. This set of abnormal records to be removed represents the set of data records identified as abnormal.

[0027]

[0028] Where x i Let τ be the index value of the i-th cave microenvironment monitoring data; D represents the set of all data records with normalized structure; t is the uniform time dimension of continuous recording; τ i =t i+1 -t i This represents the local sampling time interval for the i-th record; The acceleration within the local window represents the time interval. This represents the first rate of change of the index value; The second derivative of the index value; p is the historical steady-state tolerance factor corresponding to the i-th record; i Represents record x i Spatial coordinates of the cave monitoring point; For spatial relation with x i The set of adjacent sampling points; ||p i -p j || 2 Represents the squared Euclidean distance between the i-th and j-th spatial points; For spatial local aggregation density; θ iso This is the spatial isolation threshold.

[0029] In a preferred embodiment, trend recovery sorting is performed on the misordered time periods, and the time arrangement path of the lower limit of the trend perturbation range is selected by a perturbation function coupled with directional continuity, trend curvature and sampling interval.

[0030]

[0031] in This represents the time-ordered structure selected as having minimal trend perturbation; π represents a candidate permutation path. This represents the index value of the k-th record in the candidate path; Its first derivative, first derivative Reflects the direction of the trend; Its second derivative, the second derivative Reflects the trend curvature; Indicates the time interval between adjacent points; Π represents the angle of the trend direction, indicating the degree of deviation of the point from the trend; Π represents the set of all feasible time arrangement paths for the disordered segment; and n represents the length of the candidate sequence.

[0032] In a preferred embodiment, the three quality dimensions are integrated to generate a final grade by comprehensively evaluating the temporal continuity, trend consistency, and rhythm matching of the data.

[0033]

[0034] in This represents the final output quality level score; m is the number of dimensions of the monitoring indicators. T represents the total time of missing data for the i-th dimension indicator; i This refers to the total duration of this indicator; For the integrity factor, the square in the integrity factor represents the nonlinear penalty for the lack of continuity; θ i (t) represents the angle between the trend directions of this indicator and other indicators at time t; cos 2 (θ i (t) represents the consistent expression term. The more consistent the trend direction of the consistent expression term, the closer its value is to 1. This refers to the rhythmic perturbation acceleration, which is used to measure the non-stationarity of the sampling interval. Ω represents the sampling rhythm perturbation degree. i This indicates the effective time period for the indicator.

[0035] The technical effects and advantages of this invention are as follows:

[0036] 1. By jointly constructing identification criteria with the local changes of rhythm disturbances and the multi-order derivatives of indicator trends, and combining spatial adjacency density to judge the isolation of anomalies, the system can automatically identify and remove outliers, mutation points and structural drifts in cave microenvironment monitoring data, effectively solving the problem of data validity being difficult to guarantee due to the disordered accumulation of monitoring data in existing technologies.

[0037] 2. By constructing a perturbation sorting function based on trend direction continuity, curvature fitting degree and rhythm difference, trend recovery and sequence merging are performed on the out-of-order timestamp records in the cave microenvironment monitoring data, so that the time structure reconstruction results take into account both trend naturalness and rhythm stability, and eliminate the time sequence disorder problem caused by the chaotic export.

[0038] 3. By establishing a multi-dimensional quality calculation structure with integrity coverage, consistency trend angle and rhythm disturbance degree as the core, and using asymmetric functions for weighted fusion, a quantifiable data credibility level vector is formed, so that quality information of different dimensions has a unified output standard, which improves the adaptability and reliability of monitoring data in subsequent modeling and evaluation processes.

[0039] 4. By setting a structural consistency judgment mechanism for field completeness rate before data output, we ensure that the sorting correction structure and indicator fields remain highly consistent within the threshold tolerance range. Then, we perform standard formatting and field mapping operations, which ensures that the data integration results have the scalability and reuse efficiency to be smoothly connected to downstream functional modules (such as anomaly tracing and trend prediction).

[0040] 5. By constructing a six-stage continuous processing flow of "structure merging, rhythm identification, anomaly removal, trend recovery, quality assessment, and structure integration", a closed-loop data quality control mechanism for grotto temple scenarios is formed, which takes into account the environmental complexity and data non-repeatability in historical site monitoring, and improves the fault tolerance and engineering implementation of the overall monitoring chain. Attached Figure Description

[0041] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0042] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0043] Refer to the instruction manual appendix Figure 1 An embodiment of the present invention provides a method for quality control and evaluation of microenvironment monitoring data in grotto temples, comprising:

[0044] Acquire cave microenvironment monitoring data, reconstruct the spatiotemporal structure of the cave microenvironment monitoring data, establish a data expression foundation with consistent attribute structure, and provide a stable execution entry point for subsequent processing;

[0045] By identifying potential abnormal segments and eliminating abnormal points from the intersection of time rhythm and numerical changes, the validity of the value range of cave microenvironment monitoring data can be ensured.

[0046] Identify timestamp disorder issues and restore the correct sorting structure by combining trend patterns to ensure time sequence consistency;

[0047] Based on the evaluation dimensions of completeness, consistency, and rhythm, a data credibility level expression is formed, which is used to monitor the quantification of data value.

[0048] Under the premise of ensuring that the integrity rate of the cave microenvironment monitoring data fields meets the structural consistency judgment, the sorting and correction structure is subjected to format specification and field mapping operations to obtain the integrated data structure and output it.

[0049] The cave microenvironment monitoring data was obtained, including temperature data, relative humidity data, and carbon dioxide concentration data. The cave microenvironment monitoring data was subjected to a structure merging operation to perform a unified binding of the acquisition time field and the spatial location field, resulting in a spatiotemporal labeled dataset.

[0050] For each record in the spatiotemporal labeled dataset, the index field is extracted by aggregating by index type to obtain the index classification data column. For each type of data in the index classification data column, the sampling interval is identified by sorting the data over continuous time to obtain the initial time series structure.

[0051] In the initial time series structure, each record segment is evaluated for rhythm consistency by calculating the sampling frequency difference, resulting in a set of sampling rhythm states.

[0052] The sampling rhythm state set includes interval variation items and local time difference sequences. The sampling rhythm state set is subjected to a threshold deviation identification mechanism to perform rhythm disturbance identification. If the interval fluctuation exceeds the preset interval fluctuation threshold range, an abnormal rhythm segment is output. The corresponding record in the abnormal rhythm segment is subjected to sliding difference processing in the index classification data column to extract the rate of change and obtain the rate of change sequence.

[0053] Each rate of change value in the rate of change sequence is compared with the historical change threshold template. If the deviation exceeds the upper or lower limit of the preset deviation threshold, a mutation record group is output.

[0054] Each record in the mutation record group, combined with its sampling spatial location information, is subjected to node isolation judgment through spatial continuity mapping. If it has no spatial continuity with neighboring records, it is judged as a spatial anomaly and removed.

[0055] If a mutation record does not meet the spatial anomaly removal criteria, it is determined whether it has exceeded the preset duration in the rhythm anomaly segment. If it does, it is marked as a fluctuation boundary point and retained.

[0056] The initial time series structure is subjected to time stamp increment detection and order verification. If timestamp reversal or duplicate items are detected, a time misorder index group is generated. For each record in the time misorder index group, trend matching and comparison are performed in combination with the rhythm distribution characteristics in the sampling rhythm state set to obtain a trend offset record set.

[0057] The overlap of adjacent windows between the trend offset record set and the continuous trend segments of the verified sequence is calculated. If the trend directions of the two are consistent and the slopes are similar, they are judged as repairable items and sequential merging is performed.

[0058] If the trend offset record fails to find a consistent trend direction in the continuous trend segment, a slope standardization operation is performed to construct a new rhythm template, and time series recovery is performed through minimum perturbation sorting to obtain the sorting correction structure.

[0059] Each type of index data in the sorting and correction structure undergoes integrity measurement through missing segment identification and sampling coverage calculation, and outputs an integrity score column; the integrity score column is then compared with the mutation record group in terms of time position. If the proportion of the intersection region exceeds the preset intersection region proportion threshold, it is marked as a low integrity segment.

[0060] The index data of the sorting correction structure are compared through a synchronous window to perform collaborative rate of change fitting. Those with fitting residuals less than the allowable deviation range constitute a consistent segment set. The consistent segment set is cross-judged with the sampling rhythm state set. If there is no overlap with the rhythm abnormal segment, it is marked as a high confidence data segment.

[0061] The integrity score, consistency fit, and rhythm intersection of each data segment are weighted by segment quality and aggregated to output a microenvironment data quality level vector.

[0062] The data quality level vector of the cave microenvironment monitoring data is combined with the field integrity rate in the sorting correction structure to perform structural consistency judgment. If the field missing does not exceed the preset field missing threshold, it is judged as an expandable data structure.

[0063] In the scalable data structure, each type of indicator data is generated by standard output after uniform formatting and field mapping, resulting in data integration results that can be used for anomaly tracing, trend prediction, and monitoring optimization.

[0064] It should be noted that in the formula structure involved in this scheme, dimensionless terms can be used as proportional or structural adjustment factors. When combined with quantities with units, they only play a role in numerical scaling and do not introduce new physical dimensions. Therefore, they will not change or confuse the overall unit system. This combination of "dimensionless terms and terms with units" can be understood as a composite structural expression commonly used in mathematical physics modeling. It conforms to the principle of dimensional consistency and has a clear physical interpretation basis.

[0065] Secondly, in the formula structure of this scheme, if multiple variables with different physical units are involved, including but not limited to time, mass or energy variables, their joint appearance is to express the collaborative modeling relationship of multiple physical mechanisms. Each variable can form a unified structure through function mapping, ratio combination or normalization adjustment, with clear units and clear meaning. The overall expression conforms to the principle of dimensional consistency and the conventional formula of engineering modeling.

[0066] In this solution, constants, weights, adjustment factors, threshold parameters, proportional coefficients, etc., are all adjustable control parameters for different application environments. Their values ​​depend on the target equipment configuration, data input characteristics, and performance optimization goals. During the implementation phase, they are set to converge within a reasonable range through model verification, performance constraints, or engineering calibration. Although these parameters do not have a unique preset value, they have clear adjustment logic and calculation paths. They belong to the deterministic setting process in engineering implementation. The purpose of this setting is to ensure that the solution is both universally adaptable and reproducible and operable, without affecting its technical clarity and feasibility.

[0067] A joint assessment is performed on temporal disturbances and numerical abrupt changes in cave microenvironment monitoring data. Simultaneously, spatial location aggregation is considered to determine whether points are structurally isolated, resulting in a set of abnormal records to be removed. This set is defined as the set of data records identified as anomalous.

[0068] Where x i Let τ be the index value of the i-th cave microenvironment monitoring data; D represents the set of all data records with normalized structure; t is the uniform time dimension of continuous recording; τ i =t i+1 -t i This represents the local sampling time interval for the i-th record; This represents the acceleration (rate of change of rhythm) within the local window of the time interval; This represents the first-order rate of change of the indicator value (used to detect rapid changes); The second derivative of the index value (reflecting whether there is a jump trend); p represents the historical steady-state tolerance factor corresponding to the i-th record. The historical steady-state tolerance factor is derived from the coupling of historical rhythm and indicator stationarity in the context. i Represents record x i Spatial coordinates of the cave monitoring point, p j Similarly; For spatial relation with x i The set of adjacent sampling points; ||p i -p j || 2 Represents the squared Euclidean distance between the i-th and j-th spatial points; θ represents the local density of spatial aggregation, indicating the degree of adjacency of a point within the spatial layout. iso This is the spatial isolation threshold, which defines a spatial point as being considered isolated with low adjacency.

[0069] As the first term in the above formula This represents the local acceleration change that measures the temporal rhythm at that point.

[0070] As the second term in the above formula This indicates the sensitivity to numerical abrupt changes by combining the first-order and second-order trends of the indicators.

[0071] In the above formula, the product of the two factors constitutes an anomaly intensity estimate. If it exceeds... This indicates that the point has experienced severe disturbances; simultaneously, the density of neighboring points is aggregated using a spatial Gaussian decay method to determine whether it is "structurally isolated," avoiding the accidental deletion of continuous fluctuations; if both conditions are met, then the point x is removed. i If it is determined to be abnormal, it will be added to the removal set.

[0072] Perform trend recovery sorting on the misordered time periods, and select the time arrangement path of the lower limit of the trend perturbation range through the perturbation function coupled with the direction continuity, trend curvature and sampling interval;

[0073]

[0074] in This represents the time-ordered structure selected as having the smallest trend perturbation; π represents a candidate permutation path, where π contains π1, π2, ..., π. n Reorder the index for a time period; This represents the index value of the k-th record in the candidate path; Its first derivative, first derivative Reflects the direction of the trend; Its second derivative, the second derivative Reflects the trend curvature; Indicates the time interval between adjacent points; Π represents the angle of the trend direction, indicating the degree of deviation of the point from the trend; Π is the set of all feasible time arrangement paths for the disordered segment; n is the length of the candidate sequence.

[0075] The above equation uses trend-oriented differential continuity as the main axis (trend direction combined with curvature) to ensure that the rearranged sequence has physical trend smoothness; the first term in the above equation is used to determine the directional shift of the first-order trend change; the second term determines whether the curvature of the second-order trend change is abrupt; the third term constructs a rhythm consistency × trend direction shift term. The larger the value, the more drastic the change. The path with the lowest total disturbance cost is the trend-optimal reordering.

[0076] The final grade is generated by comprehensively evaluating the temporal continuity, trend consistency, and rhythm matching of the data and integrating the three quality dimensions.

[0077]

[0078] in This represents the final output quality level score; m is the number of monitoring indicator dimensions, which includes temperature, humidity, and CO2. T represents the total time of missing data for the i-th dimension indicator; i This refers to the total duration of this indicator; For the integrity factor, the square in the integrity factor represents the nonlinear penalty for the lack of continuity; θ i (t) represents the angle between the trend directions of this indicator and other indicators at time t; cos 2 (θ i (t) represents the consistent expression term. The more consistent the trend direction of the consistent expression term, the closer its value is to 1. This refers to the rhythmic perturbation acceleration, which is used to measure the non-stationarity of the sampling interval. Ω represents the sampling rhythm perturbation degree. i This indicates the valid time period for the indicator;

[0079] exist The first term in the formula represents the inference of completeness using the missing test time, and the squaring process strengthens the penalty; in The term in the formula represents the square integral of the cosine of the angle between the trend directions, and the square root of the integral represents the overall consistency of the trend direction; The last term in the formula represents the overall integration of the second derivative with respect to the rhythm disturbance, reflecting the stability of the time distribution; the "1 + disturbance" in the denominator controls the upper limit. The overall structure of the formula incorporates three core quality logics: complete data collection coverage, consistent multi-dimensional trends, and stable rhythm distribution.

[0080] It should be noted that the "Method for Quality Control and Evaluation of Monitoring Data of Grotto Temple Microenvironment" proposed in this invention stems from the common deficiencies in monitoring data processing in grotto temple sites in my country, particularly in the areas of data anomaly identification, time series disorder correction, and reliability assessment of monitoring results, where a systematic and structured technical approach is lacking. To address this problem, the inventors do not simply rely on existing data cleaning strategies or general quality scoring frameworks. Instead, starting from the unique structure of "grotto temple microenvironment monitoring data," they have designed a quality control method with rhythm, abrupt changes, trends, and spatial location as the core of composite judgment. This ensures that the solution can adapt to the multi-source, heterogeneous, continuous, dense, and irreversible monitoring scenarios in the protection of historical sites.

[0081] The first step of this method starts with the acquired cave microenvironment monitoring data. The time and spatial location fields of the data collection are first merged to construct a spatiotemporal labeled dataset with a consistent attribute structure. This is because only when both the time and spatial dimensions are consistent can a basic logical entry point be provided for subsequent data analysis. Then, by aggregating data according to index type, data such as temperature, humidity, and carbon dioxide concentration are classified by dimension to generate index classification data columns. Continuous time sorting and identification are then performed on these data columns to extract the sampling interval and time jump situation, thereby forming an initial time series structure with a temporal progression relationship.

[0082] To identify anomalous data, the second step of the scheme employs a coupling approach of rhythmic perturbation and abrupt change trends. This involves constructing combined features of second-order time difference fluctuations and first- and second-order changes in index values ​​to achieve a deeper assessment of anomalous states, rather than relying on traditional threshold-based judgments. Furthermore, by combining the spatial location information of each data point and calculating its adjacency density in the spatial topology, the scheme determines whether the point is in a state of "structural isolation." This is a highly practical mechanism for monitoring the microenvironment of grottoes, as environmental changes exhibit significant spatial continuity. Once an anomalous point loses its adjacency support in space, its authenticity becomes questionable.

[0083] In the third step, to address the frequent temporal misordering issues during the export or writing of cave data, the solution designs a trend matching and perturbation sorting logic with trend direction continuity as the core. By comparing the directional direction and curvature of each record with the trends before and after it, it is determined whether a trend deviation has occurred. Then, based on trend consistency and sampling rhythm stability, a perturbation sorting model is constructed, prioritizing the retention of path combinations with continuous trends and stable rhythms, ultimately achieving the restoration of the temporal structure of misordered segments.

[0084] Next, the plan moves to the fourth step: establishing an assessment mechanism for the overall quality level of cave monitoring data. This quality assessment considers not only completeness indicators such as the missing data rate, but also introduces trend consistency (through changes in the angle between the trends of multiple indicators) and rhythm fluctuation consistency (through the integral of the fluctuation of the sampling rhythm) as collaborative dimensions. These three dimensions are nested and expressed using an asymmetric mapping method during the fusion process to enhance the ability to distinguish between high-fluctuation, high-disorder, and high-missing data sections. The resulting quality level vector not only reflects the performance of each data segment in a multi-dimensional structure, but can also be used as input for subsequent processes such as anomaly tracing, trend modeling, and early warning assessment.

[0085] In the final step, this method confirms whether the monitoring data meets the scalability conditions for integrated output by performing a consistency judgment on structural integrity. Only when the missing rate of all indicator fields is lower than the set threshold, the sorting and correction structure is stable, and the quality level is traceable, will the data be formatted and mapped according to standards, and finally output as an integrated result body, serving scenarios such as the protection of grotto temple cultural relics, monitoring of microenvironment evolution, and auxiliary decision support.

[0086] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for quality control and evaluation of microenvironment monitoring data in grotto temples, characterized in that, include: Acquire cave microenvironment monitoring data, reconstruct the spatiotemporal nature of the cave microenvironment monitoring data, establish a data expression foundation with a consistent attribute structure, and provide an execution entry point for subsequent processing; By identifying potential abnormal segments and eliminating abnormal points from the intersection of time rhythm and numerical changes, the validity of the value range of cave microenvironment monitoring data can be ensured. Identify timestamp disorder issues and restore the correct sorting structure by combining trend patterns to ensure time sequence consistency; Based on the evaluation dimensions of completeness, consistency, and rhythm, a data credibility level expression is formed, which is used to monitor the quantification of data value. Under the premise of ensuring that the integrity rate of the cave microenvironment monitoring data fields meets the structural consistency judgment, the sorting and correction structure is subjected to format specification and field mapping operation to obtain the integrated data structure and output it. A joint assessment is performed on temporal disturbances and numerical abrupt changes in cave microenvironment monitoring data. Simultaneously, spatial location aggregation is considered to determine whether points are structurally isolated, resulting in a set of abnormal records to be removed. This set is defined as the set of data records identified as anomalous. ; in For the first The index values ​​of the cave microenvironment monitoring data; This represents the set of all data records with normalized structure. A unified time dimension for continuous recording; Indicates the first The local sampling time interval of each record; The acceleration within the local window represents the time interval. This represents the first rate of change of the index value; The second derivative of the index value; For the first The historical steady-state tolerance factor corresponding to each record; Representing records Spatial coordinates of the cave monitoring point; For spatial and The set of adjacent sampling points; Indicates the first With the The square of the Euclidean distance between points in space; This refers to the localized aggregation density in space. The spatial isolation threshold; Perform trend recovery sorting on the misordered time periods, and select the time arrangement path of the lower limit of the trend perturbation range through the perturbation function coupled with the direction continuity, trend curvature and sampling interval; in This indicates the time-ordered structure selected as having minimal trend perturbation. For a given candidate permutation path; The first candidate path The indicator value of each record; Its first derivative, first derivative Reflects the direction of the trend; Its second derivative, the second derivative Reflects the trend curvature; Indicates the time interval between adjacent points; The angle between the trend direction and the trend direction indicates the degree to which the point deviates from the trend. The set of all feasible time permutation paths for the out-of-order segment; The length of the candidate sequence; The final grade is generated by comprehensively evaluating the temporal continuity, trend consistency, and rhythm matching of the data and integrating the three quality dimensions. in This represents the final output quality rating score; The number of dimensions for monitoring indicators; For the first The total duration of missing data for dimensional indicators; This refers to the total duration of this indicator; For integrity factor, the square in integrity factor represents the nonlinear penalty for missing information on continuity; For this indicator in The angle between the trend direction of the moment and other indicators; For consistent expression terms, the more consistent the trend direction of the consistent expression term, the closer the value is to 1; This refers to the rhythmic perturbation acceleration, which is used to measure the non-stationarity of the sampling interval. This represents the sampling rhythm perturbation degree; This indicates the effective time period for the indicator.

2. The method for quality control and evaluation of microenvironment monitoring data in grottoes and caves according to claim 1, characterized in that: The cave microenvironment monitoring data was obtained, including temperature data, relative humidity data, and carbon dioxide concentration data. The cave microenvironment monitoring data was subjected to a structure merging operation to perform a unified binding of the acquisition time field and the spatial location field, resulting in a spatiotemporal labeled dataset. For each record in the spatiotemporal labeled dataset, the index field is extracted by aggregating by index type to obtain the index classification data column. For each type of data in the index classification data column, the sampling interval is identified by sorting the data over continuous time to obtain the initial time series structure. In the initial time series structure, each record segment is evaluated for rhythm consistency by calculating the sampling frequency difference, resulting in a set of sampling rhythm states.

3. The method for quality control and evaluation of microenvironment monitoring data in grottoes and caves according to claim 2, characterized in that: The sampling rhythm state set includes interval variation items and local time difference sequences. The sampling rhythm state set is subjected to a threshold deviation identification mechanism to perform rhythm disturbance identification. If the interval fluctuation exceeds the preset interval fluctuation threshold range, an abnormal rhythm segment is output. The corresponding record in the abnormal rhythm segment is subjected to sliding difference processing in the index classification data column to extract the rate of change and obtain the rate of change sequence. Each rate of change value in the rate of change sequence is compared with the historical change threshold template. If the deviation exceeds the upper or lower limit of the preset deviation threshold, a mutation record group is output.

4. The method for quality control and evaluation of microenvironment monitoring data in grotto temples according to claim 3, characterized in that: Each record in the mutation record group, combined with its sampling spatial location information, is subjected to node isolation judgment through spatial continuity mapping. If it has no spatial continuity with neighboring records, it is judged as a spatial anomaly and removed. If a mutation record does not meet the spatial anomaly removal criteria, it is determined whether it has exceeded the preset duration in the rhythm anomaly segment. If it does, it is marked as a fluctuation boundary point and retained.

5. The method for quality control and evaluation of microenvironment monitoring data in grottoes and caves according to claim 4, characterized in that: The initial time series structure is subjected to time stamp increment detection and order verification. If timestamp reversal or duplicate items are detected, a time misorder index group is generated. For each record in the time misorder index group, trend matching and comparison are performed in combination with the rhythm distribution characteristics in the sampling rhythm state set to obtain a trend offset record set. The overlap of adjacent windows between the trend offset record set and the continuous trend segments of the verified sequence is calculated. If the trend directions of the two are consistent and the slopes are similar, they are judged as repairable items and sequential merging is performed. If the trend offset record fails to find a consistent trend direction in the continuous trend segment, a slope standardization operation is performed to construct a new rhythm template, and time series recovery is performed through perturbation sorting to obtain the sorting correction structure.

6. The method for quality control and evaluation of microenvironment monitoring data in grottoes and caves according to claim 5, characterized in that: Each type of index data in the sorting and correction structure undergoes integrity measurement through missing segment identification and sampling coverage calculation, and outputs an integrity score column; the integrity score column is then compared with the mutation record group in terms of time position. If the proportion of the intersection region exceeds the preset intersection region proportion threshold, it is marked as a low integrity segment. The index data of the sorting correction structure are compared through a synchronous window to perform collaborative rate of change fitting. Those with fitting residuals less than the allowable deviation range constitute a consistent segment set. The consistent segment set is cross-judged with the sampling rhythm state set. If there is no overlap with the rhythm abnormal segment, it is marked as a high confidence data segment. The integrity score, consistency fit, and rhythm intersection of each data segment are weighted and aggregated to output a microenvironment data quality level vector.

7. The method for quality control and evaluation of microenvironment monitoring data in grottoes and caves according to claim 6, characterized in that: The data quality level vector of the cave microenvironment monitoring data is combined with the field integrity rate in the sorting correction structure to perform structural consistency judgment. If the field missing does not exceed the preset field missing threshold, it is judged as an expandable data structure. In the scalable data structure, each type of indicator data is generated by standard output after uniform formatting and field mapping, resulting in data integration results that can be used for anomaly tracing, trend prediction, and monitoring optimization.

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