Method, device and equipment for automatic evaluation of multi-source data quality in seismological observation

Abstract

The application provides a kind of method, device and equipment for the automatic evaluation of multi-source data quality in seismology observation, and relates to the technical field of seismology observation.The method comprises: receiving the quality evaluation request of multi-source data in seismology observation of different stations, and verifying the legality of the data; judging whether the quality index type in the legal quality evaluation request belongs to the type of periodic repetition calculation; if it belongs, accessing the cache using the unique identifier of the data in the quality evaluation request, and determining whether the cache hits; if the cache hits, output the quality evaluation result corresponding to the cache; if it does not belong, or belongs but the cache does not hit, extract the observation time sequence and verify the integrity of the observation time sequence, and calculate the observation time sequence according to the quality index type to obtain the calculation result; if the observation time sequence has integrity, output the calculation result as the quality evaluation result, and write the quality evaluation result into the cache.The application realizes the automatic quality evaluation of multi-source heterogeneous seismic observation data.

Description

Technical Field

[0001] This invention relates to the field of seismic observation technology, and in particular to an automatic assessment method, apparatus and equipment for the quality of multi-source data in seismic observation. Background Technology

[0002] In existing technologies, the quality assessment of seismic network observation data mainly relies on manual inspection or statistical methods based on simple completeness rates. Specifically, technicians need to periodically review geophysical observation curves to manually identify anomalies and manually record missing data segments. However, this approach is only suitable for simple scenarios involving a single station and a small number of measurements. As the scale of the seismic network expands, facing multiple stations, multiple measurement items, and diverse observation methods such as borehole strain, geoelectric field, geomagnetism, and geotilt, existing manual inspection methods suffer from limitations such as low efficiency and inability to provide comprehensive coverage, failing to meet the quality control requirements of large-scale, multi-type observation data.

[0003] Existing automated assessment schemes rely on a data access approach centered on a single relational database. The waveform and geophysical databases are separate and have heterogeneous schemas, making cross-source associations difficult within the databases. Geophysical data is dynamically partitioned, making it difficult to adapt static Structured Query Language (SQL) / Object-Relational Mapping (ORM) to the data. Furthermore, arbitrary table name concatenation by the caller poses security and misinterpretation risks. The consequence is that it is impossible to securely and reliably acquire multi-source, multi-table observation sequences within the same automated workflow.

[0004] For professional indicators that require simultaneous calculation of multiple test items over long time series, existing solutions often employ multiple joins and alignments within the database. Under conditions of missing test data, unequal sequence lengths, and massive sampling points, this approach is complex, performs poorly, and outliers still largely rely on manual interpretation. Each indicator operates independently and repeatedly accesses the database, lacking integrity checks tied to the sampling rate and reusable caching, making it difficult to support routine automatic evaluation across multiple types and sites. Summary of the Invention

[0005] This invention provides a method, apparatus, and equipment for automatic quality assessment of multi-source data in seismic observations, in order to solve the problem that it is difficult to achieve automatic quality assessment of existing multi-source heterogeneous seismic observation data.

[0006] In a first aspect, embodiments of the present invention provide an automatic assessment method for the quality of multi-source data in seismic observations, comprising: Receive quality assessment requests for multi-source data from seismic observations at different stations and verify the legality of the data in the quality assessment requests; Determine whether the quality indicator type in a legitimate quality assessment request belongs to the periodic repetitive calculation type; If the quality indicator type is a periodically repetitive calculation type, then the unique identifier of the data in the quality assessment request is used to access the cache and determine whether the cache is hit. If a cache hit occurs, output the quality assessment result corresponding to that cache. If the quality indicator type is not a periodically repetitive calculation type, or if the quality indicator type is a periodically repetitive calculation type but the cache is not hit, then the corresponding observation time series is extracted according to the quality assessment request, the integrity of the observation time series is verified, and the observation time series is calculated according to the quality indicator type to obtain the calculation result; If the observation time series has the required integrity, the calculation result is output as the quality assessment result, and the quality assessment result is written to the cache.

[0007] In one possible implementation, the quality assessment request for receiving multi-source data from seismic observations at different stations includes: The quality assessment request is received through a unified interface; Based on the quality assessment request, the application service accesses and integrates data from different databases to obtain the observation time series.

[0008] In one possible implementation, the multi-source data in the seismological observations corresponding to the quality assessment request includes geophysical observation data and seismometry waveform observation data, which are stored in different databases.

[0009] In one possible implementation, the step of accessing and integrating data from different databases in the application service based on the quality assessment request to obtain the observation time series includes: When the data type corresponding to the quality assessment request is the geophysical observation data, the station identifier, measuring point identifier, and measuring item identifier are verified by the configuration table to generate a dynamic table name, and the geophysical observation time series is obtained by querying the dynamic table name through the microservice interface. When the data type corresponding to the quality assessment request is the seismic waveform observation data, the seismic waveform observation time sequence and completeness statistics are obtained by querying the access interface of the database where the seismic waveform observation data is located. The geophysical observation time series and the seismic waveform observation time series form the observation time series.

[0010] In one possible implementation, accessing the cache using the unique identifier of the data in the quality assessment request and determining whether a cache hit occurs includes: When the data is geophysical observation data, the station identifier, measuring point identifier, measuring item identifier, time range and quality index type in the quality assessment request are combined into a string according to preset rules, and the string is hashed to generate a cache key as the unique access identifier. When the data is the seismic waveform observation data, the station identifier, channel identifier, time range and quality index type in the quality assessment request are combined into a string according to preset rules, and the string is hashed to generate a cache key as the unique access identifier. The cache key is used to query whether a corresponding quality assessment result exists in the cache. If a corresponding quality assessment result exists, the cache is hit; otherwise, the cache is not hit.

[0011] In one possible implementation, the cache key is the MD5 hash of the string.

[0012] In one possible implementation, the step of calculating the observation time series based on the quality assessment request type to obtain the calculation result includes: When the quality index type is a multi-item index, the observation time series is obtained for each item, the minimum length of each effective sequence is taken, and after alignment by the time axis, joint calculation is performed to obtain the multi-item joint analysis value as the calculation result; When the quality index type is a four-component strain self-test reliability index, a sliding window is used, with a preset number of points before and after the current point as the window, abnormal data in the window is removed, and the four-component strain self-test reliability is calculated based on the remaining valid data as the calculation result. When the quality indicator type is another preset type, the corresponding quality indicator is calculated as the calculation result.

[0013] In one possible implementation, verifying the integrity of the observation time series includes: The theoretical number of records within the corresponding time period of the observation time sequence is determined according to the preset sampling frequency; Count the actual number of records in the observation time series; The actual number of records is compared with the theoretical number of records. If they match, the observation time series has the completeness described. If they do not match, the observation time series does not have the completeness described.

[0014] Secondly, embodiments of the present invention provide an automatic assessment device for the quality of multi-source data in seismic observations, comprising: The dynamic table name secure access module is used to receive quality assessment requests for multi-source data from seismic observations at different stations and to verify the legality of the data in the quality assessment requests. The periodic repetition judgment module is used to determine whether the quality indicator type in a legitimate quality assessment request belongs to the periodic repetition calculation type. The cache hit determination module is used to access the cache using the unique identifier of the data in the quality assessment request and determine whether the cache hit occurs if the quality indicator type belongs to the periodic repetitive calculation type. The cache hit output module is used to output the quality assessment result corresponding to the cache if a cache hit occurs. The quality indicator calculation module is used to extract the corresponding observation time series according to the quality assessment request if the quality indicator type does not belong to the periodic repetitive calculation type, or if the quality indicator type belongs to the periodic repetitive calculation type but the cache is not hit, and to verify the integrity of the observation time series, and to calculate the observation time series according to the quality indicator type to obtain the calculation result. The calculation result output module is used to output the calculation result as a quality assessment result and write the quality assessment result into the cache if the observation time series has the completeness.

[0015] Thirdly, embodiments of the present invention provide an automatic assessment device for the quality of multi-source data in seismic observation, including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements the automatic assessment method for the quality of multi-source data in seismic observation as described in the first aspect or any possible implementation of the first aspect.

[0016] In this embodiment of the invention, by receiving quality assessment requests for multi-source data from seismic observations at different stations and verifying the legality of the data in the quality assessment requests, multi-source heterogeneous seismic observation data can be assessed within the same framework, avoiding resource waste caused by illegal requests entering subsequent procedures. The quality indicator type is determined to be a periodic repetitive calculation type, avoiding repeated data retrieval, verification, and calculation, thus improving response speed. For quality indicators of non-periodic repetitive calculation types, the observation time series is automatically extracted and integrity verification is performed, ensuring that the data participating in the quality calculation has a reliable basis and that the assessment results accurately reflect the true data quality. Only when the observation time series is complete is the result written to the cache, guaranteeing the high quality and reliability of data subsequently hit in the cache. Attached Figure Description

[0017] Figure 1 This is a flowchart illustrating the implementation of the automatic assessment method for multi-source data quality in seismic observations provided in this embodiment of the invention. Figure 2 This is a schematic diagram of the structure of the automatic assessment device for multi-source data quality in seismic observation provided in an embodiment of the present invention; Figure 3This is a schematic diagram of an automatic assessment device for the quality of multi-source data in seismic observations provided in an embodiment of the present invention. Detailed Implementation

[0018] The embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0019] like Figure 1 As shown in the embodiment of the present invention, the automatic assessment method for the quality of multi-source data in seismic observation includes: Step 101: Receive quality assessment requests for multi-source data from seismic observations at different stations and verify the legality of the data in the quality assessment requests.

[0020] In some embodiments, step 101 includes: Quality assessment requests are received through a unified interface.

[0021] Based on the quality assessment request, the application service accesses and integrates data from different databases to obtain the observation time series.

[0022] Verify the validity of the observation timeline in the quality assessment request.

[0023] Step 101 receives requests through a unified interface, completes cross-database data integration at the application service layer, and verifies the legality of the timing sequence in advance. This achieves automated decoupling of data acquisition and compliance checks in a heterogeneous data environment, improving the system's compatibility and robustness in request processing.

[0024] In this embodiment, the quality assessment request includes station identifier, measurement point identifier, measurement item identifier, assessment time range, and quality indicator type.

[0025] By using station identifiers, measurement point identifiers, measurement item identifiers, evaluation time ranges, and quality indicator types as request parameters, a standardized definition of quality assessment requests has been achieved.

[0026] The quality assessment request corresponds to multi-source data in seismological observations, including geophysical observation data and seismometry waveform observation data.

[0027] Geophysical observation data and seismic waveform observation data are stored in different databases. Based on a quality assessment request, the application service accesses and integrates the data from these different databases to obtain the observation time series, including: When the data type corresponding to the quality assessment request is geophysical observation data, the station identifier, measuring point identifier, and measuring item identifier are verified by the configuration table to generate a dynamic table name. The geophysical observation time series can be obtained by querying the dynamic table name through the microservice interface.

[0028] When the data type corresponding to the quality assessment request is seismic waveform observation data, the seismic waveform observation time sequence and completeness statistics can be obtained by querying the database where the seismic waveform observation data is located through the access interface.

[0029] Geophysical observation time series and seismic waveform observation time series form the observation time series.

[0030] By configuring differentiated database access paths for geophysical data and waveform data respectively, the unified integration of heterogeneous data is completed at the application service layer, enabling secure and efficient acquisition of cross-database data.

[0031] For example, step 101 specifically includes: Query the station-measurement item configuration table (qz_dict_station_item) to verify whether the station-measurement point-measurement item-sampling rate is valid.

[0032] Access is routed by data source: Geophysical observation data is retrieved from the geophysical database via microservice, and waveform data is retrieved from the waveform database via local interface; cross-database joins are not performed within the database.

[0033] Dynamic table names are generated according to rules.

[0034] The table name format is: QZ_{first 3 digits of the test item ID}_{data type}_{sampling rate}, such as QZ_232_DYU_01; the table name is derived from the configuration and is not directly input by the user.

[0035] Pass the table name and query parameters to the data access service to securely query the observation time series.

[0036] Returns a unified format of observation time series, which is timestamp + observation value, for subsequent quality index calculation.

[0037] Step 102: Determine whether the quality indicator type in the legitimate quality assessment request belongs to the periodic repetitive calculation type.

[0038] In this embodiment, step 102 includes: The system queries the preset indicator configuration table based on the quality indicator type. The indicator configuration table stores the periodic identifiers corresponding to each quality indicator type.

[0039] If the periodicity identifier corresponding to a quality indicator type is periodic, then the quality indicator type is determined to be periodic.

[0040] If the periodicity identifier corresponding to a quality indicator type is non-periodic, then the quality indicator type is determined to be non-periodic.

[0041] For example, when the periodicity flag in the preset indicator configuration table is 1, it means that the quality indicator type is periodic; when the periodicity flag in the preset indicator configuration table is 0, it means that the quality indicator type is non-periodic.

[0042] Step 103: If the quality indicator type is a periodic repetitive calculation type, then use the unique identifier of the data in the quality assessment request to access the cache and determine whether the cache is hit.

[0043] In some embodiments, step 103 includes: When the data is geophysical observation data, the station identifier, measurement point identifier, measurement item identifier, time range and quality index type in the quality assessment request are combined into a string according to preset rules, and the string is hashed to generate a cache key as a unique access identifier.

[0044] When the data is seismic waveform observation data, the station identifier, channel identifier, time range and quality index type in the quality assessment request are combined into a string according to preset rules, and the string is hashed to generate a cache key as a unique access identifier.

[0045] The cache key is used to check if a corresponding quality assessment result exists in the cache. If a corresponding quality assessment result exists, the cache is hit; otherwise, the cache is missed.

[0046] Step 103 generates a cache key by combining multiple parameters and hashing, which realizes the unique identifier and fast location of the evaluation request, and improves the hit accuracy and response speed of cache query.

[0047] In this embodiment, the cache key is the MD5 hash value of a string.

[0048] The MD5 algorithm is used to convert multi-parameter combined strings into fixed-length cache keys, which ensures uniqueness while taking into account storage space utilization and query efficiency.

[0049] For example, intermediate results that are calculated repeatedly on a daily basis and are complete are written to the Redis cache with MD5 (query parameter) as the key and are valid for 30 days.

[0050] Step 104: If a cache hit occurs, output the quality assessment result corresponding to the cache.

[0051] Step 105: If the quality indicator type is not a periodic repetitive calculation type, or if the quality indicator type is a periodic repetitive calculation type but the cache is not hit, then extract the corresponding observation time series according to the quality assessment request, verify the integrity of the observation time series, and calculate the observation time series according to the quality indicator type to obtain the calculation result.

[0052] In some embodiments, verifying the integrity of the observation time series includes: The theoretical number of records within the corresponding time period is determined based on the preset sampling frequency.

[0053] The actual number of records in the statistical observation time series.

[0054] The actual number of records is compared with the theoretical number of records. If they match, the observation time series is complete; otherwise, it is incomplete.

[0055] By automatically comparing the actual number of records with the theoretical number of records, an objective quantitative judgment of data integrity is achieved, providing reliable data for subsequent quality assessment.

[0056] For example, verifying the integrity of the observation time series specifically includes: The required data volume is determined based on the sampling rate. A sampling rate code of 01 with a sampling frequency of 1 record / minute results in 1440 records / day; a sampling rate code of 02 with a sampling frequency of 1 record / second results in 86400 records / day; a sampling rate code of 60 with a sampling frequency of 1 record / hour results in 24 records / day; and a sampling rate code of 90 with a sampling frequency of 1 record / day results in 1 record / day. The data is then compared to the actual number of records, and marked as complete or incomplete.

[0057] In some embodiments, the observation time series is calculated according to the quality assessment request type to obtain the calculation results, including: When the quality indicator type is a multi-item indicator, the observation time series is obtained for each item, the minimum length of each effective sequence is taken, and after aligning them according to the time axis, a joint calculation is performed to obtain the joint analysis value of the multi-item indicator as the calculation result.

[0058] When the quality index type is a four-component strain self-test reliability index, a sliding window is used, with a preset number of points before and after the current point as the window. Abnormal data within the window is removed, and the four-component strain self-test reliability is calculated based on the remaining valid data as the calculation result.

[0059] When the quality indicator type is other preset type, the corresponding quality indicator is calculated as the calculation result.

[0060] By configuring differentiated calculations for different quality indicator types, the accuracy and diversity of quality assessment are achieved, meeting the assessment needs of multiple types of physical observation indicators in the region.

[0061] In this embodiment, the observation time series is calculated according to the quality assessment request type to obtain the calculation results, specifically including: When the quality indicator type is a multi-item indicator, the observation time series is obtained separately for each item and associated in the application layer memory to avoid multiple JOINs within the library.

[0062] Take the minimum length of each measurement sequence and align them in order; handle cases of missing measurements and unequal lengths.

[0063] When the quality index type is four-component strain self-test reliability When evaluating indicators such as the four-component strain self-test reliability, a sliding window is used to identify and remove abnormal data. The sliding window has 180 points before and after each point, and a threshold of 1.5 times.

[0064] The system uses a dedicated algorithm to calculate quality metrics, including: Value, R-value, background noise, ground tilt noise, diurnal variation consistency, waveform integrity rate, etc.

[0065] For example, based on unified access to heterogeneous data sources of geophysical data and seismic waveform observation data, the unified quality computing service layer supports the following six quality indicators, including four-component strain self-check reliability, geoelectric field correlation coefficient, geomagnetic background noise, relative noise level of geotilt, geomagnetic diurnal variation consistency, and waveform data integrity rate. Among them, four-component strain self-check reliability, geoelectric field correlation coefficient, geomagnetic background noise, relative noise level of geotilt, and geomagnetic diurnal variation consistency are the quality assessment request types for geophysical data, and waveform data integrity rate is the quality assessment request type for seismic waveform observation data.

[0066] Four-component strain self-test reliability: The formula for calculating the four-component strain self-test reliability is: ; in, The four-component strain self-test reliability is used. The observed value of the first measurement item (2321) For the observation value of the second test item (2322), The observed value for the third measurement item (2323) The observed value for the fourth test item (2324).

[0067] Data source: 4 measurement items (2321, 2322, 2323, 2324), where 2321, 2322, 2323, and 2324 are the measurement item codes of the four components of the four-component strain gauge, representing strain observation channels in four different directions.

[0068] Earth electric field correlation coefficient (R value): Calculation method: Calculate the Pearson correlation coefficient for the hourly sampled sequence.

[0069] Data source: Long pitch (341 series) and short pitch (342 series).

[0070] Geomagnetic background noise: Calculation method: First-order difference is performed on the geomagnetic observation time series, and the mean square error of the difference series is calculated as a quantitative index of geomagnetic background noise. The smaller the index value, the higher the quality of geomagnetic data.

[0071] Data source: Geomagnetic observation data.

[0072] relative noise level due to ground tilt: The formula for calculating the relative noise level of ground tilt is: ; in, The relative noise level is determined by the ground tilt. For the first i Earth tilt observations at each sampling time, This represents the total number of sampling points.

[0073] Data source: Earth tilt observation data.

[0074] Consistency of diurnal geomagnetic variation: Calculation method: Compare the daily variation trends of the two stations.

[0075] Data source: Geomagnetic data from two stations.

[0076] Waveform data integrity rate: The formula for calculating waveform data integrity rate is: Completeness rate = (Expected data volume - Lost data volume) / Expected data volume; Data source: Waveform database table, table name is t_seed_channel_record.

[0077] For example, the sliding window algorithm includes: Select 180 data points before and after the current data point, for a total of 360 data points, and calculate the average of these 360 ​​data points. If the absolute value of the current data point exceeds 1.5 times the average, it is marked as outlier. Outliers are skipped during the calculation of quality indicators.

[0078] Step 106: If the observation time series is complete, output the calculation result as the quality assessment result and write the quality assessment result to the cache.

[0079] For example, the quality analysis results include index values, observation time series, and integrity status.

[0080] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0081] The following are device embodiments of the present invention. For details not described in detail, please refer to the corresponding method embodiments described above.

[0082] Figure 2 The diagram shows a schematic of the automatic assessment device for multi-source data quality in seismic observations provided in an embodiment of the present invention. For ease of explanation, only the parts relevant to the embodiment of the present invention are shown, and are described in detail below: like Figure 2 As shown, the automatic assessment device 2 for multi-source data quality in seismic observations includes: The dynamic table name secure access module 201 is used to receive quality assessment requests for multi-source data from seismic observations at different stations and to verify the legality of the data in the quality assessment request.

[0083] The periodic repetition judgment module 202 is used to determine whether the quality indicator type in a legitimate quality assessment request belongs to the periodic repetition calculation type.

[0084] The cache hit determination module 203 is used to access the cache using the unique identifier of the data in the quality assessment request if the quality indicator type belongs to the periodic repetitive calculation type, and to determine whether the cache hit is successful.

[0085] The cache hit output module 204 is used to output the quality evaluation result corresponding to the cache if a cache hit occurs.

[0086] The quality indicator calculation module 205 is used to extract the corresponding observation time series according to the quality assessment request if the quality indicator type does not belong to the periodic repetitive calculation type, or if the quality indicator type belongs to the periodic repetitive calculation type but the cache is not hit, and to verify the integrity of the observation time series, and to calculate the observation time series according to the quality indicator type to obtain the calculation result.

[0087] The calculation result output module 206 is used to output the calculation result as the quality assessment result and write the quality assessment result to the cache if the observation time series is complete.

[0088] Figure 3 This is a schematic diagram of an electronic device provided in an embodiment of the present invention. Figure 3 As shown, the electronic device 3 of this embodiment includes a processor 300 and a memory 301. The memory 301 stores a computer program 302. When the processor 300 executes the computer program 302, it implements the steps in the various method embodiments described above. Alternatively, when the processor 300 executes the computer program 302, it implements the functions of each module / unit in the various device embodiments described above.

[0089] For the sake of simplicity and clarity, only the above-described functional modules / units are used as examples. In practical applications, the functions described above can be assigned to different functional modules / units as needed. These modules / units can be implemented in hardware, software, or a combination of both.

[0090] In the above embodiments, the descriptions of each embodiment have their own emphasis. Parts not detailed or described in a particular embodiment can be referred to in the relevant descriptions of other embodiments. Unless otherwise specified or in conflict with logic, the terminology and / or descriptions between different embodiments are consistent and can be referenced interchangeably. Technical features in different embodiments can be combined to form new embodiments based on their inherent logical relationships.

[0091] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. An automatic quality assessment method for multi-source data in seismic observation, characterized in that, include: Receive quality assessment requests for multi-source data from seismic observations at different stations and verify the legality of the data in the quality assessment requests; Determine whether the quality indicator type in a legitimate quality assessment request belongs to the periodic repetitive calculation type; If the quality indicator type is a periodically repetitive calculation type, then the unique identifier of the data in the quality assessment request is used to access the cache and determine whether the cache is hit. If a cache hit occurs, output the quality assessment result corresponding to that cache. If the quality indicator type is not a periodically repetitive calculation type, or if the quality indicator type is a periodically repetitive calculation type but the cache is not hit, then the corresponding observation time series is extracted according to the quality assessment request, the integrity of the observation time series is verified, and the observation time series is calculated according to the quality indicator type to obtain the calculation result; If the observation time series has the required integrity, the calculation result is output as the quality assessment result, and the quality assessment result is written to the cache.

2. The automatic quality assessment method for multi-source data in seismic observations according to claim 1, characterized in that, The quality assessment request for receiving multi-source data from seismic observations at different stations includes: The quality assessment request is received through a unified interface; Based on the quality assessment request, the application service accesses and integrates data from different databases to obtain the observation time series.

3. The automatic quality assessment method for multi-source data in seismic observations according to claim 2, characterized in that, The multi-source data in the seismological observations corresponding to the quality assessment request include geophysical observation data and seismometry waveform observation data, which are stored in different databases.

4. The automatic quality assessment method for multi-source data in seismic observations according to claim 3, characterized in that, Based on the quality assessment request, the application service accesses and integrates data from different databases to obtain the observation time series, including: When the data type corresponding to the quality assessment request is the geophysical observation data, the station identifier, measuring point identifier, and measuring item identifier are verified by the configuration table to generate a dynamic table name, and the geophysical observation time series is obtained by querying the dynamic table name through the microservice interface. When the data type corresponding to the quality assessment request is the seismic waveform observation data, the seismic waveform observation time sequence and completeness statistics are obtained by querying the access interface of the database where the seismic waveform observation data is located. The geophysical observation time series and the seismic waveform observation time series form the observation time series.

5. The automatic quality assessment method for multi-source data in seismic observations according to claim 3, characterized in that, The step of accessing the cache using the unique identifier of the data in the quality assessment request and determining whether the cache is hit includes: When the data is geophysical observation data, the station identifier, measuring point identifier, measuring item identifier, time range and quality index type in the quality assessment request are combined into a string according to preset rules, and the string is hashed to generate a cache key as the unique access identifier. When the data is the seismic waveform observation data, the station identifier, channel identifier, time range and quality index type in the quality assessment request are combined into a string according to preset rules, and the string is hashed to generate a cache key as the unique access identifier. The cache key is used to query whether a corresponding quality assessment result exists in the cache. If a corresponding quality assessment result exists, the cache is hit; otherwise, the cache is not hit.

6. The automatic quality assessment method for multi-source data in seismic observations according to claim 5, characterized in that, The cache key is the MD5 hash value of the string.

7. The automatic quality assessment method for multi-source data in seismic observations according to claim 1, characterized in that, The step of calculating the observation time series based on the quality assessment request type to obtain the calculation result includes: When the quality index type is a multi-item index, the observation time series is obtained for each item, the minimum length of each effective sequence is taken, and after alignment by the time axis, joint calculation is performed to obtain the multi-item joint analysis value as the calculation result; When the quality index type is a four-component strain self-test reliability index, a sliding window is used, with a preset number of points before and after the current point as the window, abnormal data in the window is removed, and the four-component strain self-test reliability is calculated based on the remaining valid data as the calculation result. When the quality indicator type is another preset type, the corresponding quality indicator is calculated as the calculation result.

8. The automatic quality assessment method for multi-source data in seismic observations according to claim 1, characterized in that, Verifying the integrity of the observation time series includes: The theoretical number of records within the corresponding time period of the observation time sequence is determined according to the preset sampling frequency; Count the actual number of records in the observation time series; The actual number of records is compared with the theoretical number of records. If they match, the observation time series has the completeness described. If they do not match, the observation time series does not have the completeness described.

9. An automatic assessment device for the quality of multi-source data in seismic observation, characterized in that, include: The dynamic table name secure access module is used to receive quality assessment requests for multi-source data from seismic observations at different stations and to verify the legality of the data in the quality assessment requests. The periodic repetition judgment module is used to determine whether the quality indicator type in a legitimate quality assessment request belongs to the periodic repetition calculation type. The cache hit determination module is used to access the cache using the unique identifier of the data in the quality assessment request and determine whether the cache hit occurs if the quality indicator type belongs to the periodic repetitive calculation type. The cache hit output module is used to output the quality assessment result corresponding to the cache if a cache hit occurs. The quality indicator calculation module is used to extract the corresponding observation time series according to the quality assessment request if the quality indicator type does not belong to the periodic repetitive calculation type, or if the quality indicator type belongs to the periodic repetitive calculation type but the cache is not hit, and to verify the integrity of the observation time series, and to calculate the observation time series according to the quality indicator type to obtain the calculation result. The calculation result output module is used to output the calculation result as a quality assessment result and write the quality assessment result into the cache if the observation time series has the completeness.

10. An automatic assessment device for the quality of multi-source data in seismic observation, characterized in that, It includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the automatic assessment method for the quality of multi-source data in seismic observations as described in any one of claims 1 to 8.

Images