Exploration data anomaly detection and early warning system

Abstract

The invention provides a prospecting data anomaly detection and early warning system, and relates to the technical field of data processing. Comprising a data acquisition module used for acquiring various kinds of prospecting data in a preset time period to obtain original data; the data fusion module is used for carrying out data fusion on the original data to obtain a multi-dimensional data set corresponding to each moment; the data processing module is used for calculating an anomaly detection index according to the difference of the multi-dimensional data sets at adjacent moments; the first detection module is used for determining real abnormal data and fault data according to the distribution of the abnormal detection indexes; the second detection module is used for determining spatial abnormal characteristics of the real abnormal data according to a spatial analysis algorithm to obtain spatial abnormal data; and the early warning module is used for generating corresponding early warning information according to the spatial abnormal data. According to the invention, the problems of incomplete prospecting data anomaly detection and low accuracy in the prior art are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a prospecting data anomaly detection and early warning system. Background Art

[0002] Prospecting data collection and anomaly detection are important steps in the exploration of mineral resources (such as metal mines, non-metallic mines, etc.) in geology. Various prospecting data play a key role in the positioning, development and management of mineral resources. In order to accurately and efficiently explore the mineral resources in geology, the existing patent discloses a method for exploration data collection and logging data anomaly detection. Based on time series analysis, the difference between the target logging data (such as acoustic wave data, resistivity data, and natural gamma radiation data) at adjacent moments is used to calculate the initial anomaly degree, the overall deviation degree, and the actual anomaly degree, and then screen out the abnormal data, and match the target logging data through the Spearman rank correlation coefficient to distinguish between real abnormal data and fault data. However, the above patent only analyzes a single type of logging data, while prospecting data usually includes multi-source data such as geophysical data, geochemical data, and geological data. The analysis of a single data source cannot fully capture the correlation between multi-source data, resulting in inaccurate anomaly detection. In addition, the above patent is mainly based on time series analysis and cannot capture the abnormal characteristics of prospecting data in space, resulting in incomplete anomaly detection and low accuracy. Summary of the invention

[0003] In order to overcome the deficiencies of the prior art, the purpose of the present invention is to provide a prospecting data anomaly detection and early warning system, which solves the problem of incomplete prospecting data anomaly detection and low accuracy in the prior art.

[0004] To achieve the above object, the present invention provides the following solutions:

[0005] A prospecting data anomaly detection and early warning system, comprising:

[0006] The data acquisition module is used to collect various prospecting data within a preset time period to obtain raw data;

[0007] A data fusion module performs data fusion on the original data to obtain a multidimensional data set corresponding to each moment;

[0008] A data processing module, used to calculate anomaly detection indicators according to the differences of multidimensional data sets at adjacent moments, wherein the anomaly detection indicators include: initial anomaly degree, overall deviation degree and actual anomaly degree;

[0009] A first detection module, used to determine real abnormal data and fault data according to the distribution of the abnormal detection index;

[0010] A second detection module is used to determine the spatial anomaly characteristics of the real abnormal data according to a spatial analysis algorithm to obtain spatial anomaly data;

[0011] The early warning module is used to generate corresponding early warning information according to the spatial abnormal data.

[0012] Preferably, the data fusion module includes:

[0013] A data preprocessing submodule is used to perform data cleaning and normalization on the raw data to obtain raw data of the same dimension;

[0014] A trend calculation submodule is used to decompose the prospecting data of each dimension of the original data using a time series decomposition algorithm to obtain a data change trend sequence;

[0015] The correlation calculation submodule is used to calculate the Pearson correlation coefficient between the change trend sequences of each dimension data to obtain the multi-dimensional trend correlation;

[0016] A trend shift sequence calculation submodule is used to calculate the absolute value of the difference between the data sequence of each dimension of the original data and the trend sequence to obtain a trend shift sequence;

[0017] An abnormal moment detection submodule, used to detect abnormal data in the trend offset sequence using a LOF algorithm to obtain the abnormal moment of the trend;

[0018] The trend jump degree calculation submodule is used to calculate the trend jump degree according to the distribution of trend abnormal moments;

[0019] A fusion participation degree calculation module is used to calculate the fusion participation degree according to the trend jump degree and the multi-dimensional trend correlation;

[0020] A weight calculation submodule, used for calculating the fusion weight according to the fusion participation degree;

[0021] The generating submodule is used to perform data fusion on the original data according to the fusion weight to obtain a multidimensional data set corresponding to each moment.

[0022] Preferably, the data processing module includes:

[0023] A first indicator calculation submodule, used to calculate the initial abnormality degree of the multidimensional data set;

[0024] A second indicator calculation submodule, used for calculating the overall deviation degree according to the initial abnormality degree;

[0025] The third indicator calculation submodule is used to calculate the actual abnormality degree according to the overall deviation degree.

[0026] Preferably, the expression of the initial abnormality degree is:

[0027] ;

[0028] in, is the initial abnormality level, For the Dimensions in The observed value at time, For the Dimensions in The sliding standard deviation of the moment, For the Dimensions in The sliding mean at the moment, n is the total number of dimensions;

[0029] The calculation formula of the overall deviation degree is:

[0030] ;

[0031] in, is the overall deviation degree, For the Dimensions in The predicted value at the moment;

[0032] The calculation expression of the actual abnormality degree is:

[0033] ;

[0034] in, is the actual degree of abnormality, is the local consistency factor, , and are respectively the first weight coefficient, the second weight coefficient and the third weight coefficient;

[0035] The calculation expression of the local consistency factor is:

[0036] ;

[0037] in, is the size of the local time window, is the mean of the standard deviations of all dimensions in the time window, For the The standard deviation of the dimension in the time window, For the A data sequence in a time window.

[0038] Preferably, the first detection module includes:

[0039] A standardization submodule is used to standardize the anomaly detection index to obtain standardized data;

[0040] A modeling submodule, for performing abnormal index joint distribution modeling on the standardized data using multivariate Gaussian distribution to obtain a probability distribution model;

[0041] The abnormal probability calculation submodule is used to obtain the abnormal probability at each moment according to the probability distribution model;

[0042] A cluster analysis submodule, used to cluster the abnormality probabilities at each moment to obtain initial true abnormality data and initial fault data;

[0043] The result optimization submodule is used to perform secondary classification on the initial true abnormal data and the initial fault data to obtain final true abnormal data and final fault data.

[0044] Preferably, the expression of the probability distribution model is:

[0045] ;

[0046] in, is the probability distribution model, The standardized data includes: the initial abnormality degree after standardization, the overall deviation degree after standardization and the actual abnormality degree after standardization. is the covariance matrix, is the mean vector.

[0047] Preferably, the abnormal probability calculation expression is:

[0048] ;

[0049] in, is the abnormal probability.

[0050] Preferably, the second detection module includes:

[0051] A position conversion submodule, used to convert the position information of the final real abnormal data into a unified spatial coordinate system to obtain unified position information data;

[0052] A spatial first processing submodule, used for analyzing the unified position information data according to a spatial autocorrelation algorithm to obtain spatial distribution characteristics;

[0053] The second spatial processing submodule is used to classify the spatial distribution features and extract abnormal features using a spatial clustering algorithm to obtain spatial abnormal data.

[0054] Preferably, the second spatial processing submodule includes:

[0055] A clustering unit, used to perform cluster analysis on the spatial distribution characteristics using a spatial clustering algorithm to obtain spatial cluster anomalies and spatial isolated anomalies;

[0056] An anomaly extraction unit is used to extract anomaly features according to the spatially clustered anomalies and the spatially isolated anomalies to obtain spatially clustered anomaly features and spatially isolated anomaly features;

[0057] The classification unit is used to classify the spatially aggregated abnormal features and the spatially isolated abnormal features to obtain spatial abnormal data.

[0058] Preferably, the abnormality extraction unit comprises:

[0059] A clustering center position determination subunit, used to determine the coordinate mean of the data points in the spatial clustering anomaly and the spatial isolated anomaly area to obtain the clustering center;

[0060] A clustering radius determination subunit, used to determine the maximum distance from all data points in the spatial clustering anomaly and spatial isolated anomaly area to the coordinate mean to obtain a clustering radius;

[0061] A cluster density determination subunit, used to determine the cluster density of all data points in the spatial cluster anomaly and spatial isolated anomaly region to obtain a cluster density;

[0062] The feature determination subunit is used to determine the spatial clustering anomaly feature and the spatial isolation anomaly feature according to the clustering center, clustering radius and clustering density.

[0063] The present invention discloses the following technical effects:

[0064] The present invention provides a prospecting data anomaly detection and early warning system, comprising: a data acquisition module, which is used to collect a variety of prospecting data within a preset time period to obtain raw data; a data fusion module, which performs data fusion on the raw data to obtain a multidimensional data set corresponding to each moment; a data processing module, which is used to calculate anomaly detection indicators according to the differences of the multidimensional data sets at adjacent moments, and the anomaly detection indicators include: initial anomaly degree, overall deviation degree and actual anomaly degree; a first detection module, which is used to determine real anomaly data and fault data according to the distribution of the anomaly detection indicators; a second detection module, which is used to determine the spatial anomaly characteristics of the real anomaly data according to a spatial analysis algorithm to obtain spatial anomaly data; an early warning module, which is used to generate corresponding early warning information according to the spatial anomaly data. The present invention significantly improves the comprehensiveness and accuracy of prospecting data anomaly detection through multi-dimensional data fusion, multi-level anomaly detection and spatial anomaly analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0066] Figure 1 A schematic diagram of the structure of a prospecting data anomaly detection and early warning system provided in an embodiment of the present invention.

[0067] Description of reference numerals:

[0068] 1-data acquisition module, 2-data fusion module, 3-data processing module, 4-first detection module, 5-second detection module, 6-early warning module. DETAILED DESCRIPTION

[0069] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0070] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0071] like Figure 1 As shown, the present invention provides a prospecting data anomaly detection and early warning system, comprising:

[0072] The data acquisition module 1 is used to collect various prospecting data within a preset time period to obtain raw data;

[0073] Specifically, prospecting data includes:

[0074] Geological data: such as rock type, stratigraphic structure, mineral composition, etc.; geophysical data: such as gravity, magnetism, electrical method, earthquake, etc.; geochemical data: such as element content in soil, water samples, and gas; remote sensing data: such as satellite images, aerial photographs, etc.; drilling data: such as core, well wall, well depth, etc.

[0075] Data fusion module 2, which performs data fusion on the original data to obtain a multidimensional data set corresponding to each moment;

[0076] Data processing module 3, used to calculate anomaly detection indicators according to the differences of multidimensional data sets at adjacent moments, wherein the anomaly detection indicators include: initial anomaly degree, overall deviation degree and actual anomaly degree;

[0077] A first detection module 4, used to determine real abnormal data and fault data according to the distribution of the abnormal detection index;

[0078] The second detection module 5 is used to determine the spatial abnormality characteristics of the real abnormal data according to the spatial analysis algorithm to obtain the spatial abnormality data;

[0079] The early warning module 6 is used to generate corresponding early warning information according to the spatial abnormal data.

[0080] Furthermore, the data fusion module 2 includes:

[0081] The data preprocessing submodule is used to clean and normalize the raw data to obtain the raw data in the same dimension; and scale the data to the range of [0, 1] or [-1, 1].

[0082] The trend calculation submodule is used to decompose the prospecting data of each dimension of the original data using a time series decomposition algorithm to obtain a data change trend sequence; use the time series decomposition algorithm to decompose the data into trend, seasonality and residual parts and extract the change trend sequence.

[0083] The correlation calculation submodule is used to calculate the Pearson correlation coefficient between the change trend sequences of each dimension data to obtain the multi-dimensional trend correlation;

[0084] Specifically, the local change rate (such as difference or derivative) of the trend sequence in each dimension is calculated; the size of the sliding window is dynamically adjusted according to the change rate: the window is reduced when the change rate is large, and the window is expanded when the change rate is small; the Pearson correlation coefficient between trend sequences is calculated in each window; the results of all windows are combined to obtain a multi-dimensional trend correlation matrix.

[0085] A trend shift sequence calculation submodule is used to calculate the absolute value of the difference between the data sequence of each dimension of the original data and the trend sequence to obtain a trend shift sequence;

[0086] An abnormal moment detection submodule, used to detect abnormal data in the trend offset sequence using a LOF algorithm to obtain the abnormal moment of the trend;

[0087] The trend jump degree calculation submodule is used to calculate the trend jump degree according to the distribution of trend abnormal moments;

[0088] A fusion participation degree calculation module is used to calculate the fusion participation degree according to the trend jump degree and the multi-dimensional trend correlation;

[0089] A weight calculation submodule, used for calculating the fusion weight according to the fusion participation degree;

[0090] The generating submodule is used to perform data fusion on the original data according to the fusion weight to obtain a multidimensional data set corresponding to each moment.

[0091] Furthermore, the data processing module 3 includes:

[0092] A first indicator calculation submodule, used to calculate the initial abnormality degree of the multidimensional data set;

[0093] A second indicator calculation submodule, used for calculating the overall deviation degree according to the initial abnormality degree;

[0094] The third indicator calculation submodule is used to calculate the actual abnormality degree according to the overall deviation degree.

[0095] Furthermore, the expression of the initial abnormality degree is:

[0096] ;

[0097] in, is the initial abnormality level, For the Dimensions in The observed value at time, For the Dimensions in The sliding standard deviation of the moment, For the Dimensions in The sliding mean at each moment, n is the total number of dimensions; this formula uses the sliding mean and standard deviation to dynamically capture the changing trend of the data, avoid misjudgment caused by a fixed threshold, and eliminate the influence of different dimensional dimensions through standardized processing, making the results more comparable.

[0098] The calculation formula of the overall deviation degree is:

[0099] ;

[0100] in, is the overall deviation degree, For the Dimensions in The predicted value at the moment;

[0101] Specifically, a time series prediction model is introduced to predict the data values ​​under normal conditions, which are compared with the actual observed values ​​to capture the overall deviation trend and use the Euclidean distance to measure the overall deviation degree, taking into account the joint abnormal characteristics of multidimensional data.

[0102] The calculation expression of the actual abnormality degree is:

[0103] ;

[0104] in, is the actual degree of abnormality, is the local consistency factor, , and are respectively the first weight coefficient, the second weight coefficient and the third weight coefficient;

[0105] The calculation expression of the local consistency factor is:

[0106] ;

[0107] in, is the size of the local time window, is the mean of the standard deviations of all dimensions in the time window, For the The standard deviation of the dimension in the time window, For the A data sequence in a time window.

[0108] Specifically, by calculating the standard deviation in the local time window, the stability characteristics of the data in the local time window are captured, and the The local consistency is normalized, and the larger the value, the more stable the data.

[0109] Furthermore, the first detection module 4 includes:

[0110] A standardization submodule is used to standardize the anomaly detection index to obtain standardized data;

[0111] A modeling submodule, for performing abnormal index joint distribution modeling on the standardized data using multivariate Gaussian distribution to obtain a probability distribution model;

[0112] The abnormal probability calculation submodule is used to obtain the abnormal probability at each moment according to the probability distribution model;

[0113] A cluster analysis submodule, used to cluster the abnormality probabilities at each moment to obtain initial true abnormality data and initial fault data;

[0114] Specifically, clustering algorithms (such as K-Means and DBSCAN) are used to cluster the anomaly probabilities.

[0115] According to the clustering results, the data is divided into initial true abnormal data and initial fault data.

[0116] The result optimization submodule is used to perform secondary classification on the initial true abnormal data and the initial fault data to obtain final true abnormal data and final fault data.

[0117] Specifically, a classification algorithm (such as SVM, random forest) is used to perform secondary classification on the initial classification data. According to the classification results, the final true abnormal data and the final fault data are obtained.

[0118] Furthermore, the expression of the probability distribution model is:

[0119] ;

[0120] in, is the probability distribution model, The standardized data includes: the initial abnormality degree after standardization, the overall deviation degree after standardization and the actual abnormality degree after standardization. is the covariance matrix, is the mean vector.

[0121] Specifically, modeling the standardized anomaly detection indicators through multivariate Gaussian distribution can effectively capture the correlation of multidimensional data and provide a probabilistic anomaly detection method.

[0122] Furthermore, the abnormal probability calculation expression is:

[0123] ;

[0124] in, is the abnormal probability.

[0125] Specifically, if the data point at a certain moment falls within the distribution range of normal data (i.e. larger), then the probability that it is an abnormal data is smaller;

[0126] If a data point at a certain moment Far from the distribution range of normal data (i.e. smaller), then the probability that it is an abnormal data It is larger.

[0127] The mean of the distribution of normal data is =[0.1,0.2,0.3], the covariance matrix is =I (identity matrix); at time t, =[0.1,0.2,0.3], at this time, Larger (because near ),so is smaller, which means the data at that moment is more likely to be normal data;

[0128] At time t', =[5,5,5], then Smaller (because keep away ),so The larger it is, the data at that moment is more likely to be abnormal data.

[0129] Furthermore, the second detection module 5 includes:

[0130] A position conversion submodule, used to convert the position information of the final real abnormal data into a unified spatial coordinate system to obtain unified position information data;

[0131] A spatial first processing submodule, used for analyzing the unified position information data according to a spatial autocorrelation algorithm to obtain spatial distribution characteristics;

[0132] Specifically, check whether the data has missing values ​​or outliers. If there are missing values, interpolation or mean filling can be used to process them; if there are outliers, the 3σ principle or IQR method can be used to eliminate or correct them. Arrange the location information data into a format suitable for spatial autocorrelation analysis (such as a two-dimensional array or GeoDataFrame) to obtain the cleaned unified location information data; determine the neighborhood relationship of each point based on the location information; construct a spatial weight matrix based on the neighborhood relationship; global spatial autocorrelation analysis: use Moran's I or Geary's C algorithm to calculate the global spatial autocorrelation index; local spatial autocorrelation analysis: use the local Moran's I (LISA) algorithm to calculate the local spatial autocorrelation index of each point; spatial distribution feature extraction:

[0133] Summarize global characteristics: According to the global spatial autocorrelation index, summarize the overall spatial distribution characteristics of the data (such as aggregation, dispersion or randomness).

[0134] Extract local features: According to the local spatial autocorrelation index, extract local spatial clustering areas (such as HH or LL areas) and abnormal areas (such as HL or LH areas), where HH and LL represent positive spatial autocorrelation, that is, the values ​​of an area are similar to those of its surrounding areas (high value clustering or low value clustering).

[0135] HL and LH: Indicates negative spatial autocorrelation, that is, the value of an area is very different from that of its surrounding areas (high value isolation or low value isolation).

[0136] Generate spatial distribution characteristics report: Integrate global and local characteristics into a spatial distribution characteristics report, including charts and text descriptions.

[0137] The second spatial processing submodule is used to classify the spatial distribution features and extract abnormal features using a spatial clustering algorithm to obtain spatial abnormal data.

[0138] Furthermore, the second spatial processing submodule includes:

[0139] A clustering unit, used to perform cluster analysis on the spatial distribution characteristics using a spatial clustering algorithm to obtain spatial cluster anomalies and spatial isolated anomalies;

[0140] An anomaly extraction unit is used to extract anomaly features according to the spatially clustered anomalies and the spatially isolated anomalies to obtain spatially clustered anomaly features and spatially isolated anomaly features;

[0141] The classification unit is used to classify the spatially aggregated abnormal features and the spatially isolated abnormal features to obtain spatial abnormal data.

[0142] Furthermore, the abnormality extraction unit includes:

[0143] A clustering center position determination subunit, used to determine the coordinate mean of the data points in the spatial clustering anomaly and the spatial isolated anomaly area to obtain the clustering center;

[0144] A clustering radius determination subunit, used to determine the maximum distance from all data points in the spatial clustering anomaly and spatial isolated anomaly area to the coordinate mean to obtain a clustering radius;

[0145] A cluster density determination subunit, used to determine the cluster density of all data points in the spatial cluster anomaly and spatial isolated anomaly region to obtain a cluster density;

[0146] The feature determination subunit is used to determine the spatial clustering anomaly feature and the spatial isolation anomaly feature according to the clustering center, clustering radius and clustering density.

[0147] Specifically, according to the coordinates of the data points in the spatial clustered anomaly and the spatially isolated anomaly area, the mean of the data point coordinates (such as longitude and latitude or UTM coordinates) in each anomaly area is calculated to obtain the coordinates of the clustering center; according to the coordinates of the data points in the spatial clustered anomaly and the spatially isolated anomaly area and their clustering center, for each data point, the Euclidean distance to the clustering center is calculated to obtain the clustering radius; according to the coordinates of the data points in the spatial clustered anomaly and the spatially isolated anomaly area and their clustering radius, the area of ​​the anomaly area is calculated and the clustering density is determined; the characteristics of the spatial clustered anomaly are determined:

[0148] Aggregation center: indicates the core location of the abnormal area.

[0149] Aggregation radius: indicates the size of the abnormal area.

[0150] Cluster density: indicates the density of data points in the abnormal area.

[0151] Determine spatially isolated anomaly characteristics:

[0152] Cluster center: indicates the location of isolated outliers.

[0153] Cluster radius: Indicates the influence range of isolated outliers (usually small).

[0154] Cluster density: Indicates the density of isolated outliers (usually low).

[0155] Generate feature report: Integrate spatial clustering anomaly features and spatial isolated anomaly features into a feature report, including charts and text descriptions.

[0156] Furthermore, the early warning system divides the warning levels into three levels: low, medium and high according to the degree of abnormality (such as abnormality probability, impact range, etc.).

[0157] Response strategy formulation: Differentiated response strategies are formulated for each warning level, such as low-level warnings only recording logs, medium-level warnings notifying relevant personnel, and high-level warnings initiating emergency plans.

[0158] Real-time graded warning: According to the abnormal detection results, the corresponding level of warning is triggered in real time and the corresponding response strategy is executed.

[0159] More specifically, the early warning system is implemented as follows:

[0160] Data visualization: Abnormal data and warning information can be intuitively displayed through charts, heat maps, maps, etc.

[0161] Interactive analysis: supports users to interactively analyze abnormal data, such as screening, magnification, and comparison.

[0162] Real-time monitoring: Real-time updates of abnormal data and warning information ensure that users are always aware of the latest situation.

[0163] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0164] The principles and implementation methods of the present invention are described in this article using specific examples. The description of the above embodiments is only used to help understand the method and core idea of ​​the present invention. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A prospecting data anomaly detection and early warning system, characterized in that: include: The data acquisition module is used to collect various prospecting data within a preset time period to obtain raw data; A data fusion module performs data fusion on the original data to obtain a multidimensional data set corresponding to each moment; A data processing module, used to calculate anomaly detection indicators according to the differences of multidimensional data sets at adjacent moments, wherein the anomaly detection indicators include: initial anomaly degree, overall deviation degree and actual anomaly degree; A first detection module, used to determine real abnormal data and fault data according to the distribution of the abnormal detection index; A second detection module is used to determine the spatial anomaly characteristics of the real abnormal data according to a spatial analysis algorithm to obtain spatial anomaly data; The early warning module is used to generate corresponding early warning information according to the spatial abnormal data.

2. A mining data anomaly detection and early warning system according to claim 1, characterized in that: The data fusion module comprises: A data preprocessing submodule is used to perform data cleaning and normalization on the raw data to obtain raw data of the same dimension; A trend calculation submodule is used to decompose the prospecting data of each dimension of the original data using a time series decomposition algorithm to obtain a data change trend sequence; The correlation calculation submodule is used to calculate the Pearson correlation coefficient between the change trend sequences of each dimension data to obtain the multi-dimensional trend correlation; A trend shift sequence calculation submodule is used to calculate the absolute value of the difference between the data sequence of each dimension of the original data and the trend sequence to obtain a trend shift sequence; An abnormal moment detection submodule, used to detect abnormal data in the trend offset sequence using a LOF algorithm to obtain the abnormal moment of the trend; The trend jump degree calculation submodule is used to calculate the trend jump degree according to the distribution of trend abnormal moments; A fusion participation degree calculation module is used to calculate the fusion participation degree according to the trend jump degree and the multi-dimensional trend correlation; A weight calculation submodule, used for calculating the fusion weight according to the fusion participation degree; The generating submodule is used to perform data fusion on the original data according to the fusion weight to obtain a multidimensional data set corresponding to each moment.

3. A prospecting data anomaly detection and early warning system according to claim 1, characterized in that: The data processing module comprises: A first indicator calculation submodule, used to calculate the initial abnormality degree of the multidimensional data set; A second indicator calculation submodule, used for calculating the overall deviation degree according to the initial abnormality degree; The third indicator calculation submodule is used to calculate the actual abnormality degree according to the overall deviation degree.

4. A prospecting data anomaly detection and early warning system according to claim 1, characterized in that: The expression of the initial abnormality degree is: ； in, is the initial abnormality level, For the Dimensions in The observed value at time, For the Dimensions in The sliding standard deviation of the moment, For the Dimensions in The sliding mean at the moment, n is the total number of dimensions; The calculation formula of the overall deviation degree is: ； in, is the overall deviation degree, For the Dimensions in The predicted value at the moment; The calculation expression of the actual abnormality degree is: ； in, is the actual degree of abnormality, is the local consistency factor, , and are respectively the first weight coefficient, the second weight coefficient and the third weight coefficient; The calculation expression of the local consistency factor is: ； in, is the size of the local time window, is the mean of the standard deviations of all dimensions in the time window, For the The standard deviation of the dimension in the time window, For the A data sequence in a time window.

5. A prospecting data anomaly detection and early warning system according to claim 1, characterized in that: The first detection module comprises: A standardization submodule is used to standardize the anomaly detection index to obtain standardized data; A modeling submodule, for performing abnormal index joint distribution modeling on the standardized data using multivariate Gaussian distribution to obtain a probability distribution model; The abnormal probability calculation submodule is used to obtain the abnormal probability at each moment according to the probability distribution model; A cluster analysis submodule, used to cluster the abnormality probabilities at each moment to obtain initial true abnormality data and initial fault data; The result optimization submodule is used to perform secondary classification on the initial true abnormal data and the initial fault data to obtain final true abnormal data and final fault data.

6. A prospecting data anomaly detection and early warning system according to claim 5, characterized in that: The expression of the probability distribution model is: ； in, is the probability distribution model, The standardized data includes: the initial abnormality degree after standardization, the overall deviation degree after standardization and the actual abnormality degree after standardization. is the covariance matrix, is the mean vector.

7. A mining data anomaly detection and early warning system according to claim 6, characterized in that: The abnormal probability calculation expression is: ； in, is the abnormal probability.

8. A prospecting data anomaly detection and early warning system according to claim 5, characterized in that: The second detection module includes: A position conversion submodule, used to convert the position information of the final real abnormal data into a unified spatial coordinate system to obtain unified position information data; A spatial first processing submodule, used for analyzing the unified position information data according to a spatial autocorrelation algorithm to obtain spatial distribution characteristics; The second spatial processing submodule is used to classify the spatial distribution features and extract abnormal features using a spatial clustering algorithm to obtain spatial abnormal data.

9. A mining data anomaly detection and early warning system according to claim 8, characterized in that: The second spatial processing submodule comprises: A clustering unit, used to perform cluster analysis on the spatial distribution characteristics using a spatial clustering algorithm to obtain spatial cluster anomalies and spatial isolated anomalies; An anomaly extraction unit is used to extract anomaly features according to the spatially clustered anomalies and the spatially isolated anomalies to obtain spatially clustered anomaly features and spatially isolated anomaly features; The classification unit is used to classify the spatially aggregated abnormal features and the spatially isolated abnormal features to obtain spatial abnormal data.

10. A mining data anomaly detection and early warning system according to claim 9, characterized in that: The abnormality extraction unit comprises: A clustering center position determination subunit, used to determine the coordinate mean of the data points in the spatial clustering anomaly and the spatial isolated anomaly area to obtain the clustering center; A clustering radius determination subunit, used to determine the maximum distance from all data points in the spatial clustering anomaly and spatial isolated anomaly area to the coordinate mean to obtain a clustering radius; A cluster density determination subunit, used to determine the cluster density of all data points in the spatial cluster anomaly and spatial isolated anomaly region to obtain a cluster density; The feature determination subunit is used to determine the spatial clustering anomaly feature and the spatial isolation anomaly feature according to the clustering center, clustering radius and clustering density.

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