Visual analysis method and system for multidimensional data of geophysical network

Abstract

The invention discloses a geophysical station network multi-dimensional data visualization analysis method and system, and particularly relates to the technical field of physical station data visualization. According to the method, an association mechanism of seismic data, geomagnetic data and gravity data is established by creating seismic numbers, dispersed multi-source data is integrated into a structured seismic data packet and an abnormal data packet to form a complete seismic data file, and then the seismic data file is obtained through methods of sub-time zone division, historical reference comparison and the like. Parameters such as a vertical component, a horizontal component, a magnetic declination and gravitational acceleration of a magnetic field are converted into quantifiable indexes such as a vertical representation value and a horizontal representation value, and a closed quadrangle is constructed to visually present a multi-dimensional data relationship, so that the problem that in the prior art, a complete seismic data file cannot be formed, and deep mining of seismic precursor signals is restricted is solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of physical station data visualization, and more particularly, to a geophysical station network multidimensional data visualization analysis method and system. BACKGROUND

[0002] The geophysical station network is an important infrastructure for obtaining geophysical field information, which can continuously collect various types of data such as earthquakes, geomagnetism, gravity, etc. These data are of great significance to the study of the internal structure of the earth, geological disaster monitoring and warning, resource exploration, etc.

[0003] However, the existing technology still has the following deficiencies in processing geophysical station network data: On the one hand, various types of geophysical data are often stored in a scattered and heterogeneous form, lacking a unified data correlation mechanism, making it difficult to efficiently integrate and analyze multi-source data such as earthquakes, geomagnetism, gravity, etc., and unable to form a complete earthquake data archive, which restricts the deep mining of earthquake precursor signals; On the other hand, data processing is mostly limited to simple analysis in a single dimension, lacking comprehensive quantification and visualization of multidimensional data, and traditional methods are difficult to convert the dynamic changes of parameters such as magnetic field vertical component, horizontal component, magnetic declination angle, and gravity acceleration into intuitive and interpretable feature information, making it difficult for researchers to quickly identify abnormal patterns in the data, and also unable to effectively compare the relevance of real-time monitoring data and historical abnormal data, greatly affecting the accuracy and timeliness of geological disaster warning such as earthquakes.

[0004] Therefore, a geophysical station network multidimensional data visualization analysis method and system are proposed. SUMMARY

[0005] In order to overcome the above-mentioned defects of the prior art, the embodiments of the present application provide a geophysical station network multidimensional data visualization analysis method and system.

[0006] To achieve the above-mentioned purpose, the present application provides the following technical solutions: A geophysical station network multidimensional data visualization analysis method, comprising: Multidimensional data construction: real-time collection of earthquake data when an earthquake occurs and geomagnetic and gravity data before the occurrence, association with the created earthquake number and earthquake, geomagnetic and gravity data after preprocessing, and construction of an earthquake data package; the geomagnetic data includes magnetic field vertical component, horizontal component, magnetic declination angle; the gravity data includes gravity acceleration; Multi-dimensional data processing: parsing the seismic data packet, determining the longitude and latitude coordinates of the seismic source from the seismic data, defining a spatial analysis area with the seismic source as the center and a radius of a certain kilometer, extracting the geomagnetic data and gravity data of the spatial analysis area within a certain time interval before the earthquake from the seismic data packet, and performing comprehensive processing to construct an abnormal data packet, which is then stored in the database in association with the earthquake number and the seismic data packet; wherein the abnormal data packet includes vertical performance value, horizontal performance value, angle performance value, gravity performance value, and a constructed closed quadrilateral; Visual presentation: after the staff extracts the abnormal data packet of the corresponding spatial analysis area, triggers the evaluation signaling and monitors the geomagnetic data and gravity data of the corresponding spatial analysis area in real time, and performs associated processing between the abnormal data packet, generates an association analysis report after processing and pushes it to the staff.

[0007] Specifically, the geomagnetic data and gravity data of the spatial analysis area within a certain time interval before the earthquake are extracted from the seismic data packet and are comprehensively processed, specifically: Divide the set time interval into each sub-time zone according to the preset division ratio, identify the magnetic field vertical component corresponding to different time points of each sub-time zone, extract the maximum value and minimum value of the magnetic field vertical component in each sub-time zone, and calculate the difference to obtain the vertical variation of each sub-time zone; for the vertical variation of each sub-time zone, the ratio calculation is performed between the pre-stored historical average vertical variation of the spatial analysis area, and the average of each group of calculated ratios is obtained to obtain the vertical performance value within the set time interval; Identify the magnetic field horizontal component corresponding to different time points of each sub-time zone, extract the maximum value and minimum value of the magnetic field horizontal component in each sub-time zone, and calculate the difference to obtain the horizontal variation of each sub-time zone; for the horizontal variation of each sub-time zone, the ratio calculation is performed between the pre-stored historical average horizontal variation of the spatial analysis area, and the average of each group of calculated ratios is obtained to obtain the horizontal performance value within the set time interval.

[0008] Specifically, the geomagnetic data and gravity data of the spatial analysis area within a certain time interval before the earthquake are extracted from the seismic data packet and are comprehensively processed, specifically: Identify the magnetic declination angle corresponding to different time points of each sub-time zone, calculate the average of each group of magnetic declination angles of each sub-time zone to determine the declination angle mean value of each sub-time zone; calculate the declination angle mean value of each sub-time zone using the standard deviation formula to determine the angle offset value within the set time interval, and calculate the ratio between the angle offset value and the pre-stored historical average angle offset value of the spatial analysis area to obtain the angle performance value within the set time interval; Extract the gravity acceleration at each time point in the set time interval, and respectively calculate the ratio of the gravity acceleration at each time point to the pre-stored historical average gravity acceleration of the spatial analysis area, take the average of each group of calculated ratios to obtain the gravity performance value in the set time interval.

[0009] Specifically, the specific process of constructing the abnormal data packet is: Take the origin of the plane rectangular coordinate system as the starting point, extend 4 rays in the plane at equal angles from the origin, wherein the 4 rays respectively represent the vertical performance value, the horizontal performance value, the angle performance value, and the gravity performance value; The vertical performance value, the horizontal performance value, the angle performance value, and the gravity performance value are respectively taken as the extension lengths of the 4 rays, and the rays are extended from the origin along the ray direction; in the order of the rays, the endpoints of the rays are connected in turn to construct a closed quadrilateral; the vertical performance value, the horizontal performance value, the angle performance value, and the gravity performance value in the set time interval and the constructed closed quadrilateral are integrated to construct an abnormal data packet.

[0010] Specifically, the triggering of the evaluation signaling and the real-time monitoring of the geomagnetic data and the gravity data of the corresponding spatial analysis area, and the correlation processing between the abnormal data packet, are specifically: After triggering the evaluation signaling, the geomagnetic data and the gravity data of the corresponding spatial analysis area are monitored in real time until the monitoring time length accumulates to the length of the set time interval, and then the monitoring is stopped; the geomagnetic data and the gravity data in the monitoring time interval are analyzed to construct a real-time data packet, wherein the real-time data packet includes the vertical performance value, the horizontal performance value, the angle performance value, the gravity performance value, and the constructed closed quadrilateral; The closed quadrilateral in the real-time data packet is extracted as an evaluation graph, and the closed quadrilaterals of each group of abnormal data packets in the corresponding spatial analysis area are extracted as matching graphs; The vertical performance value, the horizontal performance value, the angle performance value, and the gravity performance value in the real-time data packet are extracted, and the vertical change amount of each time zone, the horizontal change amount of each time zone, the average angle of each time zone, and the gravity acceleration at each time point are analyzed from the vertical performance value, the horizontal performance value, the angle performance value, and the gravity performance value; The value points of the vertical change amount of each time zone are plotted in the coordinate system, and the adjacent value points are connected to construct a vertical broken line; similarly, the horizontal change amount of each time zone, the average angle of each time zone, and the gravity acceleration at each time point are plotted to construct a horizontal broken line, an angle broken line, and a gravity broken line; The vertical broken line, the horizontal broken line, the angle broken line, and the gravity broken line corresponding to the real-time data packet are integrated into a real-time broken line packet; The vertical performance value, the horizontal performance value, the angle performance value, and the gravity performance value of each group of abnormal data packets in the corresponding spatial analysis area are extracted, and the vertical broken line, the horizontal broken line, the angle broken line, and the gravity broken line are constructed in the same way. The vertical broken line, the horizontal broken line, the angle broken line, and the gravity broken line corresponding to the abnormal data packets are integrated into an abnormal broken line packet.

[0011] Specifically, the specific process of generating the correlation analysis report is as follows: The evaluation graph and the matching graph are comprehensively processed to obtain the graph abnormal coincidence index between the evaluation graph and each group of matching graphs; and the real-time broken line packet and the abnormal broken line packet are comprehensively processed to obtain the numerical abnormal coincidence index Ej between the real-time broken line packet and the abnormal broken line packet. The graph abnormal coincidence index and the numerical abnormal coincidence index Ej obtained by analyzing the real-time data packet and the abnormal data packet are extracted and filled into the pre-constructed report template to generate the correlation analysis report.

[0012] Specifically, the graph abnormal coincidence index between the evaluation graph and each group of matching graphs is obtained as follows: The center points of the evaluation graph and each group of matching graphs are determined, the center of the evaluation graph is aligned with the center points of each group of matching graphs, the overlapping area between the evaluation graph and each group of matching graphs is obtained after alignment, the ratio between each group of overlapping areas and the graph area of the corresponding matching graph is calculated, and the calculated ratio is subtracted by an integer and then the absolute value is taken to obtain the matching area value. For the corresponding edge length of the evaluation graph and each group of matching graphs, the formula is used to calculate the matching edge value Y; wherein and respectively represent the lengths of the corresponding edges of the evaluation graph and the matching graph; i is the edge length number. The matching area value and the matching edge value calculated between the evaluation graph and the matching graph are extracted, multiplied by the preset area weight coefficient and the edge weight coefficient respectively, and then summed to obtain the graph abnormal coincidence index between the evaluation graph and each group of matching graphs.

[0013] Specifically, the numerical abnormal coincidence index Ej between the real-time broken line packet and the abnormal broken line packet is obtained as follows: For each pair of corresponding broken lines in the real-time broken line packet and the abnormal broken line packet, the number of successfully matched inflection points in the corresponding two broken lines is counted, denoted as the inflection point coincidence value E1. The slopes of each line segment in the two broken lines are obtained, and the formula is used for calculation; the calculation result is subtracted by an integer and then the absolute value is taken to obtain the slope coincidence value E2 of the two broken lines; wherein is the slope of the gth segment of the broken line in the real-time broken line packet, Slope of the gth segment of the abnormal polyline envelope for the polyline in the envelope; () is a sign function, 1 when the same sign, and -1 when the opposite sign; The inflection point coincidence value E1 and the slope coincidence value E2 calculated for each corresponding two polylines in the real-time polyline envelope and the abnormal polyline envelope are normalized, and the formula The numerical anomaly coincidence index Ej of the real-time polyline envelope and the abnormal polyline envelope is calculated; wherein E1 and E2 are the weight coefficients corresponding to the inflection point coincidence value and the slope coincidence value, respectively, and Ez is the polyline similarity index calculated for each corresponding two polylines in the real-time polyline envelope and the abnormal polyline envelope; is a preset weight coefficient corresponding to different polyline similarity indexes in the real-time polyline envelope and the abnormal polyline envelope, and z is the number of different polyline similarity indexes.

[0014] A geophysical network multi-dimensional data visualization analysis system, comprising: The data acquisition module is used for real-time acquisition of seismic data when an earthquake occurs and geomagnetic and gravity data before the occurrence, association with the created earthquake number and the seismic, geomagnetic and gravity data after preprocessing, and construction of a seismic data package; The data processing module is used for parsing the seismic data package, determining the epicenter latitude and longitude coordinates from the seismic data, demarcating a spatial analysis area with the epicenter as the center and a radius of a set kilometer, extracting the geomagnetic data and gravity data of the spatial analysis area in a set time interval before the occurrence of the earthquake from the seismic data package, and after comprehensive processing, constructing an abnormal data package and storing it in the database in association with the earthquake number and the seismic data package; The visualization evaluation module is used for triggering evaluation signaling and real-time monitoring of the geomagnetic data and gravity data of the corresponding spatial analysis area after the staff extracts the abnormal data package of the corresponding spatial analysis area, and performing association processing between the abnormal data package, generating an association analysis report after processing and pushing it to the staff.

[0015] The technical effects and advantages of the present application are: (1) By creating an earthquake number to establish an association mechanism for seismic, geomagnetic and gravity data, the dispersed multi-source data is integrated into structured seismic data packages and abnormal data packages, forming a complete seismic data archive, and then through sub-time zone division, historical benchmark comparison and other methods, the magnetic field vertical component, horizontal component, magnetic declination angle and gravity acceleration and other parameters are converted into quantifiable vertical performance values, horizontal performance values and other indicators, and a closed quadrilateral is constructed to intuitively present the multi-dimensional data relationship, solving the problem that the existing technology cannot form a complete seismic data archive, which restricts the depth of mining of the earthquake precursor signal; (2) By monitoring the geomagnetic and gravity data of the spatial analysis area in real time after the staff triggers the assessment signal, a real-time data packet is constructed, and it is correlated with the abnormal data packet to generate a correlation analysis report, thereby realizing dynamic monitoring and efficient decision support. This solves the problem that the existing technology cannot effectively compare the correlation between real-time monitoring data and historical abnormal data, which greatly affects the accuracy and timeliness of early warning of geological disasters such as earthquakes. (3) By using the graphical anomaly overlap index and the numerical anomaly overlap index, the correlation between real-time monitoring data and historical anomaly data is reflected from different perspectives, enabling rapid comparison between real-time data and historical anomaly data, and providing intuitive decision-making basis for earthquake early warning and geological research. Attached Figure Description

[0016] Figure 1 This is a flowchart of a multi-dimensional data visualization and analysis method for geophysical network according to the present invention; Figure 2 This is a schematic diagram of a multi-dimensional data visualization and analysis system for geophysical networks according to the present invention. Detailed Implementation

[0017] 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. Example 1

[0018] like Figure 1 As shown, a method for visualizing and analyzing multidimensional data from geophysical networks includes: Multidimensional data construction: Data acquisition equipment is deployed at various monitoring stations of the geophysical network to ensure real-time acquisition of seismic data at the time of an earthquake, as well as geomagnetic and gravity data prior to the earthquake. After preprocessing, the data is associated with the earthquake, geomagnetic, and gravity data through a created earthquake number, and an earthquake data package is constructed. The earthquake data includes magnitude, focal depth, and earthquake waveform; the geomagnetic data includes the vertical component, horizontal component, and magnetic declination of the magnetic field; and the gravity data includes gravitational acceleration. The data acquisition equipment transmits the acquired data to the data processing center via wired or wireless communication. Preprocessing: For data of different formats, data cleaning software tools, such as the Pandas library in Python, are used to perform preprocessing operations such as noise reduction, deduplication, and missing value imputation. For example, abnormal spike signals in seismic wave data are removed by setting a reasonable threshold; and missing values ​​in geomagnetic data are imputed by linear interpolation. According to the unified data format specification, use the format conversion tool to convert data of different formats into a standard format that can be recognized by the system, such as converting the common seismic data format SAC into the HDF5 format.

[0019] A database management system is established, and a reasonable data table structure is designed. The pre-processed data is stored according to the data type, time, space and other dimensions, and the correlation between the data is established to ensure the integrity and consistency of the data. For example, in the earthquake data table, the occurrence time, magnitude, focal depth and other information of the earthquake are stored, and the correlation with the geomagnetic, gravity and other related data tables is established through the unique earthquake number.

[0020] Multi-dimensional data processing: parsing the earthquake data packet, determining the focal latitude and longitude coordinates from the earthquake data, and defining a spatial analysis area with the focal point as the center and a radius of a certain number of kilometers (set to 300-800 kilometers, dynamically adjusted according to the magnitude), extracting the geomagnetic data and gravity data of the spatial analysis area within a certain time interval before the earthquake from the earthquake data packet, and after comprehensive processing, constructing an abnormal data packet and storing it in the database in association with the earthquake number and earthquake data packet, forming a complete earthquake data archive; Specifically: Divide the set time interval into sub-time zones according to the preset division ratio, identify the magnetic field vertical component corresponding to different time points in each sub-time zone, extract the maximum value and minimum value of the magnetic field vertical component in each sub-time zone, and after difference calculation, obtain the vertical variation of each sub-time zone; For the vertical variation of each sub-time zone, the ratio calculation is performed between the pre-stored historical average vertical variation of the spatial analysis area, and the average value of each group of calculated ratios is obtained as the vertical performance value within the set time interval; Identify the magnetic field horizontal component corresponding to different time points in each sub-time zone, extract the maximum value and minimum value of the magnetic field horizontal component in each sub-time zone, and after difference calculation, obtain the horizontal variation of each sub-time zone; For the horizontal variation of each sub-time zone, the ratio calculation is performed between the pre-stored historical average horizontal variation of the spatial analysis area, and the average value of each group of calculated ratios is obtained as the horizontal performance value within the set time interval; It is supplemented that the set time interval is divided into sub-time zones according to the preset ratio (such as by day, hour or sliding window, etc.), the maximum value and minimum value of the magnetic field vertical component of each sub-time zone are extracted, and the difference value (i.e. variation = maximum value - minimum value) is calculated, the variation of each sub-time zone is compared with the historical maximum variation (to obtain the ratio, which can be understood as the "relative fluctuation intensity"), and then the "magnetic field performance value" is obtained by averaging, which can normalize the comparison between short-term fluctuations and historical background, eliminate the influence of absolute numerical difference, and more objectively reflect the activity degree of the vertical magnetic field within the set time interval.

[0021] After extracting the horizontal component of the magnetic field at each time point in the set time interval, the average of the horizontal component of the interval is obtained by arithmetic mean (reflecting the overall intensity level of the horizontal magnetic field in the period), and then it is compared with the historical maximum horizontal component of the magnetic field in the spatial analysis area (representing the strongest disturbance degree of the horizontal magnetic field in the history of the area). The obtained horizontal performance value is essentially a "normalized index of the current average intensity of the horizontal magnetic field relative to the historical extreme value", and its core is to eliminate the absolute value difference by comparing with the historical extreme value and highlight the relative activity degree of the current horizontal magnetic field.

[0022] The magnetic declination corresponding to different time points in each sub-time zone is identified, and the average of each group of magnetic declination in each sub-time zone is calculated to determine the average declination of each sub-time zone. The average declination of each sub-time zone is calculated using the standard deviation formula to determine the angle offset value in the set time interval. The angle offset value is compared with the historical average angle offset value stored in the spatial analysis area to obtain the angle performance value in the set time interval. It is noted that the angle performance value is calculated by the ratio of the standard deviation of the average magnetic declination (angle offset value) in the sub-time zone to the historical maximum angle offset value, which reflects the relative relationship between the dispersion degree of the magnetic declination and the historical extreme fluctuation.

[0023] The gravitational acceleration at each time point in the set time interval is extracted, and the gravitational acceleration at each time point is compared with the historical average gravitational acceleration stored in the spatial analysis area (the ratio is calculated), and then the "gravitational performance value" is obtained by averaging, which reflects the overall deviation of the current gravitational field from the historical average state. It is noted that the gravitational acceleration at each time point in the set time interval is extracted, and compared with the historical average gravitational acceleration (the ratio is calculated), and then the "gravitational performance value" is obtained by averaging, which reflects the overall deviation of the current gravitational field from the historical average state. The origin of the plane rectangular coordinate system is taken as the starting point (0, 0), and 4 rays are extended in the plane at equal angles from the origin, of which 4 rays represent the vertical performance value, the horizontal performance value, the angle performance value and the gravitational performance value. The vertical performance value, the horizontal performance value, the angle performance value and the gravitational performance value are respectively taken as the extension length of the 4 rays, and the terminal points of the rays are connected in turn to form a closed quadrilateral. The vertical performance value, the horizontal performance value, the angle performance value and the gravitational performance value in the set time interval and the closed quadrilateral are integrated to construct an abnormal data package. It is noted that through this visualization method, the performance degree of each parameter can be quickly judged, which provides intuitive reference for comprehensive analysis of earthquake precursors. Visual presentation: after the staff extracts the abnormal data packets corresponding to the spatial analysis area, triggers the evaluation signaling and monitors the geomagnetic data and gravity data of the corresponding spatial analysis area in real time, and processes the correlation between the abnormal data packets, generates a correlation analysis report after processing and pushes it to the staff; Specifically: After triggering the evaluation signaling, the geomagnetic data and gravity data of the corresponding spatial analysis area are monitored in real time until the monitoring time interval reaches the set time interval, and then the monitoring is stopped. The geomagnetic data and gravity data in the monitoring time interval are analyzed, and a real-time data packet is constructed, wherein the real-time data packet includes vertical performance value, horizontal performance value, angle performance value, gravity performance value and constructed closed quadrilateral; The closed quadrilateral in the real-time data packet is extracted as the evaluation graph, and the closed quadrilateral of each group of abnormal data packets in the corresponding spatial analysis area is extracted as the matching graph; The center points of the evaluation graph and each group of matching graphs are determined, and the centers of the evaluation graph are respectively aligned with the center points of each group of matching graphs. After alignment, the overlapping area between the evaluation graph and each group of matching graphs is obtained. The ratio between each group of overlapping areas and the corresponding matching graph area is calculated. The calculated ratio is subtracted by an integer and the absolute value is obtained to obtain the matching area value; For the corresponding edge length of the evaluation graph and each group of matching graphs, the formula is used to calculate the matching edge value Y; the smaller the result, the more consistent the length of each edge (for example, the length of a certain edge of the real-time quadrilateral is 2.0, and the length of the corresponding edge of the abnormal quadrilateral is 2.2, the relative error is 0.09, and the matching degree is higher); wherein and respectively represent the length of each edge corresponding to the evaluation graph and the matching graph; i is the edge length number; The matching area value and the matching edge value calculated between the evaluation graph and the matching graph are extracted, and then multiplied by the preset face value weight coefficient and edge value weight coefficient respectively, and then summed to obtain the graph abnormal overlap index between the evaluation graph and each group of matching graphs; It is supplemented that the analysis process also focuses on the spatial form coincidence degree of the graph (matching area value, reflecting the overall contour similarity) and the proportion consistency of each dimension (matching edge value, reflecting whether the relative change trend of the vertical, horizontal, angle and gravity four components is consistent), and the two are fused into "graph abnormal overlap index" through weight coefficient. This multi-dimensional fusion method not only considers the overall form of the graph, but also takes into account the proportional relationship of each key indicator, which can more comprehensively identify real-time data that is highly similar to historical abnormal patterns and reduce false positives that may be caused by single-dimensional analysis; Extract the vertical performance value, horizontal performance value, angle performance value, and gravity performance value in the real-time data packet, and analyze the vertical change amount of each sub-time zone, the horizontal change amount of each sub-time zone, the average deflection angle of each sub-time zone, and the gravity acceleration at each time point from the vertical performance value, horizontal performance value, angle performance value, and gravity performance value. Draw the numerical points of the vertical change amount of each sub-time zone in the coordinate system, and connect adjacent numerical points to construct a vertical broken line; similarly, draw the horizontal change amount of each sub-time zone, the average deflection angle of each sub-time zone, and the gravity acceleration at each time point to construct a horizontal broken line, a deflection angle broken line, and a gravity broken line. Integrate the vertical broken line, horizontal broken line, deflection angle broken line, and gravity broken line corresponding to the real-time data packet into a real-time broken line packet. Extract the vertical performance value, horizontal performance value, angle performance value, and gravity performance value of each group of abnormal data packets in the spatial analysis area, and similarly construct a vertical broken line, a horizontal broken line, a deflection angle broken line, and a gravity broken line. Integrate the vertical broken line, horizontal broken line, deflection angle broken line, and gravity broken line corresponding to the abnormal data packet into an abnormal broken line packet. For each pair of corresponding broken lines in the real-time broken line packet and the abnormal broken line packet, count the number of successfully matched inflection points, denoted as the inflection point coincidence value E1. The inflection point represents the point where the slope of the broken line changes from positive to negative or from negative to positive (such as the time point where the upward trend changes to downward trend), reflecting the frequency of trend changes. Obtain the slope of each line segment in the two broken lines, and calculate according to the formula The closer the result is to 1, the higher the synchronization of the upward / downward trend (such as when the real-time broken line rises, the abnormal broken line also rises). Subtract the integer from the calculation result and take the absolute value to obtain the slope coincidence value E2 of the two broken lines; wherein is the slope of the gth segment of the broken line in the real-time broken line packet, is the slope of the gth segment of the broken line in the abnormal broken line packet. is the sign function, which is 1 when the signs are the same and -1 when the signs are different. Normalize the inflection point coincidence value E1 and the slope coincidence value E2 calculated for each pair of corresponding broken lines in the real-time broken line packet and the abnormal broken line packet according to the formula Calculate the numerical anomaly coincidence index Ej of the real-time broken line packet and the abnormal broken line packet; wherein E1 and E2 are the weight coefficients corresponding to the inflection point coincidence value E1 and the slope coincidence value E2, respectively, and Ez is the broken line similarity index calculated for each pair of corresponding broken lines in the real-time broken line packet and the abnormal broken line packet. is the preset weight coefficient corresponding to the different broken line similarity index in the real-time broken line packet and the abnormal broken line packet, and z is the number of different broken line similarity indexes. It is to be noted that, by focusing on dynamic trend characteristics, multi-dimensional weighted fusion, and standardized quantification, the similarity between real-time geophysical field changes and historical seismic anomaly patterns is accurately identified, which provides strong support for dynamic monitoring and scientific warning of seismic precursors, and effectively improves the accuracy and reliability of anomaly identification.

[0024] The graphic anomaly coincidence index and the numerical anomaly coincidence index Ej analyzed from the real-time data packet and the abnormal data packet are extracted and filled into the pre-constructed report template to generate a correlation analysis report. Embodiment 2

[0025] Please refer to Figure 2 As shown in the figure, based on the multi-dimensional data visualization analysis method of the geophysical network provided in Embodiment 1 of the present application, Embodiment 2 of the present application proposes a multi-dimensional data visualization analysis system of the geophysical network. Embodiment 2 is only a preferred mode of Embodiment 1, and the implementation of Embodiment 2 will not affect the implementation of Embodiment 1 alone.

[0026] Specifically, the multi-dimensional data visualization analysis system of the geophysical network provided in Embodiment 2 of the present application is different in that it comprises: The data acquisition module is used to collect real-time seismic data when an earthquake occurs and geomagnetic and gravity data before the occurrence of the earthquake, and after preprocessing, the created earthquake number is associated with the seismic, geomagnetic and gravity data, and the seismic data packet is constructed; The data processing module is used to parse the seismic data packet, determine the epicenter latitude and longitude coordinates from the seismic data, and define a spatial analysis area with the epicenter as the center and a radius of a certain number of kilometers. The geomagnetic data and gravity data of the spatial analysis area within a certain time interval before the occurrence of the earthquake are extracted from the seismic data packet, and after comprehensive processing, the abnormal data packet is constructed and stored in the database in association with the earthquake number and the seismic data packet; The visualization evaluation module is used to trigger evaluation signaling and monitor the geomagnetic data and gravity data of the corresponding spatial analysis area in real time after the staff extracts the abnormal data packet of the corresponding spatial analysis area, and performs association processing between the abnormal data packet. After processing, the correlation analysis report is generated and pushed to the staff; The above formulas are all dimensionless values, and the specific dimensionless can be standardized or other means, which will not be described here. The formula is obtained by software simulation of a large amount of data to reflect the current situation, and the preset parameters in the formula are set by the person skilled in the art according to the actual situation.

[0027] The above-described embodiments can be implemented in part or in whole through software, hardware, firmware or any combination thereof. When implemented in software, the above-described embodiments can be implemented using one or more computer programs written in any suitable programming language. Such programs can be stored in one or more storage media or memory devices (e.g., a computer readable medium) associated with the computer or other suitable devices. The memory devices can include, but are not limited to, RAM, ROM, EEPROM, flash memory or other suitable memory devices. The computer programs can be loaded and / or executed on the computer or other suitable devices to produce a computer implemented process, such that the actions specified in the computer programs are performed. The computer programs can be executed on a single computer or on multiple computers.

[0028] It should be understood that the sequence of the above processes is not intended to mean the execution order of the processes, and the execution order of the processes should be determined according to the functions and inherent logic of the processes, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0029] Those skilled in the art can understand that the units and algorithm steps of the examples described in combination with the embodiments disclosed herein can be realized by electronic hardware or a combination of computer software and electronic hardware. Whether the functions are realized in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0030] In several embodiments provided in the present application, it should be understood that the disclosed system, device and method can be implemented in other ways. For example, the above-described device embodiments are merely illustrative, for example, the division of units is merely a logical function division, and actual implementation can have another division manner, for example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the shown or discussed units can be indirect coupling or communication connection through some interfaces, devices or units, and can be electrical, mechanical or other forms.

[0031] The units described as separate components may or may not be physically separate, and the components displayed as units may or may not be physical units, which may be located in one place or distributed on multiple network units. Part or all of the units can be selected according to actual needs to achieve the purpose of the embodiment scheme.

[0032] In addition, the functional units in each embodiment of the present application can be integrated in one processing unit, or each unit can be physically present alone, or two or more units can be integrated in one unit.

[0033] The functions, if realized in the form of software functional units and sold or used as independent products, can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application or the part of the present application that essentially contributes to the prior art or the part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: a U disk, a mobile ATA hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various program code storage media.

[0034] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of changes or replacements within the technical scope disclosed in the present application, which should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A method for visualizing and analyzing multidimensional data from geophysical networks, characterized in that, include: Multidimensional data construction: Real-time acquisition of seismic data at the time of an earthquake, as well as geomagnetic and gravity data prior to the earthquake, followed by preprocessing and association with the earthquake, geomagnetic, and gravity data through a created earthquake number, and construction of an earthquake data package; geomagnetic data includes the vertical component, horizontal component, and magnetic declination of the magnetic field; gravity data includes gravitational acceleration; Multidimensional data processing: The seismic data packets are parsed to determine the latitude and longitude coordinates of the epicenter. A spatial analysis area with a radius of kilometers centered on the epicenter is delineated. Geomagnetic and gravity data within a predetermined time interval before the earthquake are extracted from the seismic data packets. After comprehensive processing, anomaly data packets are constructed and stored in the database in association with the earthquake number and the seismic data packets. The anomaly data packets include vertical, horizontal, angular, and gravity values, as well as a constructed closed quadrilateral. Visual presentation: After extracting abnormal data packets in the corresponding spatial analysis area, staff trigger an evaluation signal and monitor the geomagnetic and gravity data in the corresponding spatial analysis area in real time. The data packets are then correlated with the abnormal data packets, and a correlation analysis report is generated and pushed to the staff.

2. The method for multidimensional data visualization and analysis of geophysical networks according to claim 1, characterized in that, The process of extracting geomagnetic and gravity data of the spatial analysis area from the seismic data packet within a predetermined time interval before the earthquake, and then performing comprehensive processing, specifically involves: The set time interval is divided into sub-time zones according to a preset division ratio. The vertical magnetic field components corresponding to different time points in each sub-time zone are identified. The highest and lowest values ​​of the vertical magnetic field components in each sub-time zone are extracted, and the difference is calculated to obtain the vertical change of each sub-time zone. The vertical change of each sub-time zone is compared with the historical average vertical change pre-stored in the spatial analysis area. The average of the calculated ratios is taken to obtain the vertical performance value within the set time interval. Identify the horizontal magnetic field components corresponding to different time points in each sub-time zone, extract the highest and lowest values ​​of the horizontal magnetic field components in each sub-time zone, and calculate the difference to obtain the horizontal change in each sub-time zone; calculate the ratio between the horizontal change in each sub-time zone and the historical average change in the spatial analysis area, and obtain the mean value of each calculated ratio to obtain the horizontal performance value within the set time interval.

3. The method for multidimensional data visualization and analysis of geophysical network according to claim 2, characterized in that, The process of extracting geomagnetic and gravity data of the spatial analysis area from the seismic data packet within a predetermined time interval before the earthquake, and then performing comprehensive processing, specifically involves: Identify the magnetic declination at different time points in each sub-time zone, calculate the average value of each group of magnetic declinations in each sub-time zone, and determine the mean value of the declination in each sub-time zone. Calculate the mean value of the declination in each sub-time zone using the standard deviation formula, determine the angle offset value within the set time interval, and calculate the ratio between the angle offset value and the historical average angle offset value pre-stored in the spatial analysis area to obtain the angle performance value within the set time interval. Extract the gravitational acceleration at each time point within the set time interval, and calculate the ratio of the gravitational acceleration at each time point to the historical average gravitational acceleration pre-stored in the spatial analysis area. Take the average of the calculated ratios to obtain the gravitational performance value within the set time interval.

4. The method for multidimensional data visualization and analysis of geophysical networks according to claim 3, characterized in that, The specific process for constructing the abnormal data packet is as follows: Starting from the origin of the Cartesian coordinate system, four rays extend at equal angles in the plane, representing the vertical, horizontal, angular, and gravitational values, respectively. The vertical, horizontal, angular, and gravitational values ​​are used as the extension lengths of four rays, extending from the origin along the ray direction. The endpoints of each ray are connected sequentially to construct a closed quadrilateral. The vertical, horizontal, angular, and gravitational values ​​within a set time interval, along with the constructed closed quadrilateral, are integrated to construct an abnormal data packet.

5. The method for multidimensional data visualization and analysis of geophysical network according to claim 1, characterized in that, The trigger evaluation signaling and real-time monitoring of geomagnetic and gravity data in the corresponding spatial analysis area, and the correlation processing with abnormal data packets, specifically involve: After triggering the evaluation signal, the geomagnetic and gravity data of the corresponding spatial analysis area are monitored in real time until the cumulative monitoring time reaches the set time interval and then the monitoring stops. The geomagnetic and gravity data within the monitoring time interval are analyzed to construct a real-time data packet, which includes vertical performance values, horizontal performance values, angular performance values, gravity performance values, and a constructed closed quadrilateral. The closed quadrilaterals within the real-time data packets are extracted as evaluation graphics, and the closed quadrilaterals of each group of abnormal data packets within the corresponding spatial analysis area are extracted as matching graphics. Extract the vertical, horizontal, angular, and gravitational values ​​from the real-time data packets. Analyze the vertical variation, horizontal variation, mean skew angle, and gravitational acceleration of each time zone from these values. Plot the vertical changes of each sub-time zone to the corresponding numerical points in the coordinate system, and connect adjacent numerical points to construct a vertical polyline; similarly, plot the horizontal changes of each sub-time zone, the mean angular deviation of each sub-time zone, and the gravitational acceleration at each time point, and construct horizontal polylines, angular polylines, and gravity polylines. The vertical, horizontal, angular, and gravity lines corresponding to the real-time data packets are integrated into a real-time line packet. Extract the vertical, horizontal, angular, and gravity values ​​of each group of abnormal data packets within the corresponding spatial analysis area. Similarly, construct vertical, horizontal, angular, and gravity polylines. Integrate the vertical, horizontal, angular, and gravity polylines corresponding to the abnormal data packets into an abnormal polyline packet.

6. The method for multidimensional data visualization and analysis of geophysical network according to claim 5, characterized in that, The specific process for generating the correlation analysis report is as follows: The evaluation graph and the matching graph are processed together to obtain the graph anomaly overlap index between the evaluation graph and each set of matching graphs; the real-time polyline package and the abnormal polyline package are processed together to obtain the numerical anomaly overlap index Ej between the real-time polyline package and the abnormal polyline package. Extract the graphical anomaly overlap index and numerical anomaly overlap index Ej obtained from the analysis of real-time data packets and abnormal data packets, and fill them into a pre-built report template to generate a correlation analysis report.

7. The method for multidimensional data visualization and analysis of geophysical networks according to claim 6, characterized in that, The obtained graph anomaly overlap index between the evaluated graph and each set of matching graphs is specifically as follows: Determine the center point of the evaluation graphic and each set of matching graphics. Align the center of the evaluation graphic with the center point of each set of matching graphics. After alignment, obtain the overlapping area between the evaluation graphic and each set of matching graphics. Calculate the ratio between the obtained overlapping area of ​​each set and the graphic area of ​​the corresponding matching graphic. Subtract the integer from the calculated ratio and take the absolute value to obtain the matching surface value. To evaluate the corresponding side lengths of the graphic and each set of matching graphics, the formula is used. Calculate the matching edge value Y; where and These represent the lengths of the sides corresponding to the evaluated and matched figures, respectively; i is the side length number. Extract the calculated matching face value and matching edge value between the evaluation graphic and the matching graphic, and multiply them by the preset face value weight coefficient and edge value weight coefficient respectively. Then sum them to obtain the graphic abnormality overlap index between the evaluation graphic and each set of matching graphics.

8. The method for multidimensional data visualization and analysis of geophysical networks according to claim 6, characterized in that, The numerical anomaly overlap index Ej between the real-time polyline package and the abnormal polyline package is obtained as follows: For each pair of corresponding lines in the real-time line package and the abnormal line package, count the number of inflection points that are successfully matched between the two corresponding lines, and denote it as the inflection point coincidence value E1. Obtain the slope of each segment in the two broken lines, and apply the formula... Perform the calculation; subtract the integer from the calculation result and take the absolute value to obtain the coincidence value of the slopes of the two broken lines, E2; where This represents the slope of the g-th segment of the polyline within the real-time polyline package. The slope of the g-th segment of the polyline within the abnormal polyline package; () represents a sign function; the result is 1 when the signs are the same and -1 when the signs are opposite. The inflection point coincidence value E1 and slope coincidence value E2 calculated for the two corresponding lines in the real-time line package and the abnormal line package are normalized according to the formula. Calculate the numerical anomaly overlap index Ej between the real-time polyline package and the abnormal polyline package; where E1 and E2 are the weighting coefficients corresponding to the inflection point coincidence value and the slope coincidence value, respectively, and Ez is the similarity index of the two corresponding lines in the real-time line package and the abnormal line package. The preset weighting coefficients are the similarity indices of different lines in the real-time line package and the abnormal line package, where z is the number of the different line similarity indices.

9. A multi-dimensional data visualization and analysis system for geophysical networks, applied to the multi-dimensional data visualization and analysis method for geophysical networks as described in any one of claims 1-8, characterized in that, include: The data acquisition module is used to collect earthquake data during an earthquake and geomagnetic and gravity data before the earthquake occurs in real time. After preprocessing, the data is associated with earthquake, geomagnetic, and gravity data through the created earthquake number, and an earthquake data package is constructed. The data processing module is used to parse earthquake data packets, determine the latitude and longitude coordinates of the epicenter from the earthquake data, delineate a spatial analysis area with the epicenter as the center and a radius of kilometers, extract geomagnetic and gravity data of the spatial analysis area within a set time interval before the earthquake from the earthquake data packets, and after comprehensive processing, construct anomaly data packets and store them in the database in association with the earthquake number and earthquake data packets. The visualization assessment module is used to trigger assessment signals and monitor the geomagnetic and gravity data of the corresponding spatial analysis area in real time after the staff extracts the abnormal data packets in the corresponding spatial analysis area. It also performs correlation processing between the data packets and the abnormal data packets, generates a correlation analysis report, and pushes it to the staff.

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