Earthquake rapid report information management system and data processing method

Abstract

The application relates to the technical field of data management, and discloses a seismic rapid report information management system and a data processing method; the system comprises a data synchronization module, a concurrent prediction module, a mode selection module, a saturation rapid report module and a conventional rapid report module; the data synchronization module constructs a seismic rapid report information source library; the concurrent prediction module predicts the query concurrency of available channels; the mode selection module determines the query mode of the available channels; the saturation rapid report module processes high-level requests and general requests; and the conventional rapid report module processes parallel request groups and query requests; the application can be upgraded to saturation processing when the overall load is high, and can only perform saturation processing on specific channels when the local load is high; the application ensures that high-priority query requests are preferentially responded to, avoids the delay of key request data, avoids the centralized occupation of running resources by a large number of remote query accesses, prevents the server from being congested, jammed or even shut down, and improves the operation stability of the seismic rapid report service.

Description

Technical Field

[0001] This invention relates to the field of data management technology, and more specifically, to an earthquake rapid reporting information management system and data processing method. Background Technology

[0002] Earthquake early warning is a crucial link in earthquake monitoring and emergency response. With the continuous densification of earthquake monitoring networks and the improvement of data processing capabilities, earthquake early warning systems need to process multimodal earthquake data from fixed stations, mobile networks, and strong-motion networks every day, resulting in an explosive growth in data processing volume. This places higher demands on the data synchronization, storage, query, and distribution capabilities of information management systems.

[0003] Reference patent application CN117830003A discloses a novel method for managing seismic exploration results data. This method includes: setting the types and quantities of data assets required for project archiving based on different project types, forming an archiving list catalog for each type of project; acquiring geological exploration results data files, classifying and cataloging the results data files to form a data asset catalog of exploration results data; inventorying the data asset catalog to form an asset ledger for each project; verifying and comparing the project asset ledger with the project archiving list, generating a verification report to confirm whether the exploration results data has been uploaded completely, and if any files are missing, supplementing the files according to the verification report. Existing earthquake rapid reporting information management systems typically employ a single concurrent processing mechanism to handle massive concurrent query requests. This mechanism is unable to perform joint assessments of overall and local concurrent pressure on query requests. Consequently, in high-concurrency scenarios, a large number of ordinary requests compete for resources with high-priority requests, causing server congestion, lag, or even crashes. Furthermore, in localized high-concurrency scenarios, it is impossible to implement targeted saturation processing for certain channels, which can easily lead to unreasonable resource allocation and reduce the operational stability and reliability of earthquake rapid reporting services.

[0004] In view of this, the present invention proposes an earthquake rapid reporting information management system and data processing method to solve the above problems. Summary of the Invention

[0005] To overcome the aforementioned deficiencies of the prior art and to achieve the above objectives, the present invention provides the following technical solution: an earthquake rapid reporting information management system, comprising: The data synchronization module synchronizes multi-mode seismic data from the static and dynamic levels to the data tables in the benchmark database for rapid reporting and matching, and converts the benchmark database into a source database for rapid seismic reporting information. The concurrency prediction module predicts the query concurrency of available channels in the next query period by using the periodic concurrency characteristics of available channels in the earthquake rapid reporting information source database during the query period. The periodic concurrency characteristics include rated concurrency, concurrency surge rate, concurrency dimension value, and peak prominence ratio. The mode selection module, based on a dual filtering mechanism, performs filtering analysis on query concurrency to determine the query mode of available channels in the next query cycle. The query modes include saturation load mode and normal load mode. The saturation rapid reporting module, under saturation load mode, divides the query requests of available channels into high-level requests and general requests, and performs saturation rapid reporting processing on high-level requests and general requests. The routine rapid reporting module, under normal load mode, filters out parallel request groups from the query requests of available channels and performs routine rapid reporting processing on the parallel request groups and query requests.

[0006] Furthermore, multi-mode earthquake data includes geographic location, time dimension, morphological attributes, and record address; data tables include catalog table, time table, latitude table, longitude table, magnitude table, depth table, duration table, place name table, and type table; The conversion method for the earthquake rapid reporting information source database is as follows: From the geographical location of earthquake event A, we can extract the longitude, latitude, and epicenter name; from the time dimension, we can extract the earthquake time and time taken; from the morphological attributes, we can extract the magnitude, depth, and earthquake type; and from the record address, we can extract the directory ID. Arrange the catalog table, time table, latitude table, longitude table, magnitude table, depth table, duration table, place name table, and type table in the benchmark database horizontally in sequence to generate A table queues; According to the arrangement of the data tables, the longitude, latitude, epicenter name, time of occurrence, duration, magnitude, depth, earthquake type and directory ID of earthquake event A are summarized in sequence to generate data queue A, and the data in data queue A are imported one by one into the data table of table queue A. B channel ports are set up on the benchmark database, and encrypted access channels are established on the channel ports through the TSL encryption protection protocol, so as to convert the benchmark database into an earthquake rapid reporting information source database.

[0007] Furthermore, the available channel filtering methods are as follows: Find the start time of the first execution data record in the earthquake rapid reporting information source database, take the current time as the end time, and record the time period between the start time and the end time as the verification period; The gateway system queries the packet loss rate and latency of B access channels during the verification period. The difference between the packet loss rate and the standard packet loss threshold is calculated and compared with the standard packet loss threshold to calculate the packet loss over-limit ratio. Access channels with a packet loss over-limit ratio less than the specified over-limit ratio and a latency less than the specified latency value are recorded as candidate channels. Using a time interval as a standard, mark C non-adjacent verification times in the verification period, detect the noise power of the candidate channel at the C verification times, and plot the C noise powers into a noise power graph with the verification time as the horizontal axis and the noise power as the vertical axis, and mark the power curves in the noise power graph. The power curve is divided into D segmented curves, with three verification times as a group. The slope of each of the D segmented curves is measured one by one using the slope calculation function. The segmented curves with a slope greater than the calibrated slope value are recorded as fluctuation curves, and the positions of all fluctuation curves are marked in the power curve. The candidate channels in the power curve that do not have two adjacent fluctuation curves are recorded as available channels, resulting in E available channels.

[0008] Furthermore, the method for obtaining the peak prominence ratio is as follows: Capture all query requests from the available channels during the query period using a packet capture tool, count the number of query requests at the same time, record it as the request concurrency value, and calculate the actual concurrency value by subtracting the request concurrency value from the base concurrency value of the available channels. The actual concurrency values ​​that are greater than the specified concurrency value are recorded as valid concurrency values, and all valid concurrency values ​​are sorted in descending order to obtain a concurrency queue; The duration between the top three valid concurrent values ​​in the concurrent queue is recorded as the concurrent duration. The top three valid concurrent values ​​in the concurrent queue are summed and compared with the concurrent duration to calculate the unit concurrent value. The peak prominence ratio is calculated by subtracting the unit concurrency value from the unit concurrency upper limit and then comparing it with the unit concurrency upper limit.

[0009] Furthermore, the dual filtering mechanism is as follows: first, all available channels are filtered as a whole, and then each available channel is filtered individually. The method for determining the query pattern is as follows: The total concurrency is calculated by summing the query concurrency of the E available channels; When the total concurrency is greater than or equal to the calibrated total concurrency threshold, all E available channels will be set to saturation load mode in the next query cycle. When the total concurrency is less than the calibrated total concurrency threshold, the query concurrency of each of the E available channels is compared with the corresponding rated concurrency. If the query concurrency is greater than or equal to the rated concurrency, the available channel will be determined to be in saturation load mode in the next query cycle; If the query concurrency is less than the rated concurrency, the available channels will be determined to be in normal load mode in the next query cycle.

[0010] Furthermore, when distinguishing between advanced requests and general requests, the encryption permissions of all query requests within the available channels are queried one by one. Query requests with encryption permissions higher than the initial permissions of the available channels are recorded as advanced requests, and the remaining query requests are recorded as general requests.

[0011] Furthermore, the method for handling saturation rapid reporting is as follows: The advanced concurrency is calculated by summing up the concurrency of all advanced requests within the available channels. When the high concurrency is greater than or equal to the rated concurrency, all high-level requests are simultaneously queried and matched with the earthquake rapid reporting information source database to obtain the first matching data. In chronological order, the remaining general requests are queried and matched with the earthquake rapid reporting information source database one by one to obtain the second matching data. The first and second matching data are then combined into rapid reporting data and displayed externally. When the high concurrency is less than the rated concurrency, the high concurrency is added to the concurrency of the general requests one by one to calculate the total concurrency, until the total concurrency is greater than or equal to the rated concurrency, or the concurrency of all general requests is added together. The high-level requests and general requests corresponding to the overall concurrency are simultaneously matched with the earthquake rapid reporting information source database to obtain the first matching data. The remaining general requests are matched with the earthquake rapid reporting information source database one by one to obtain the second matching data. The first matching data and the second matching data are then combined into rapid reporting data and displayed externally.

[0012] Furthermore, during the parallel request group filtering, semantic parsing is performed on all query requests within the available channels one by one, the request timeline of the query request is marked, query requests with the same query timeline are recorded as parallel requests, and the parallel requests with the same query timeline are aggregated to generate a parallel request group.

[0013] Furthermore, during routine rapid reporting processing, parallel request groups are matched with the earthquake rapid reporting information source database according to the order of the request timeline to obtain matching data groups. The matching data groups are then split into third matching data using a splitting technique. The remaining query requests are simultaneously matched with the earthquake rapid reporting information source database to obtain fourth matching data. The third and fourth matching data are then aggregated into rapid reporting data and displayed externally.

[0014] An earthquake rapid reporting data processing method, implemented based on an earthquake rapid reporting information management system, includes: S01: Data synchronization module, which synchronizes multi-mode seismic data from static and dynamic levels to the data tables of the benchmark database for rapid reporting and matching, and converts the benchmark database into a source database of earthquake rapid reporting information; S02: Based on the periodic concurrency characteristics of available channels in the earthquake rapid reporting information source database during the query period, predict the query concurrency of available channels in the next query period. S03: Based on a dual filtering mechanism, the query concurrency is filtered and analyzed to determine the query mode of the available channels in the next query cycle. The query modes include saturation load mode and normal load mode. S04: In saturated load mode, query requests for available channels are divided into high-level requests and general requests, and saturated rapid reporting is performed on high-level requests and general requests. S05: Under normal load mode, select parallel request groups from the query requests of available channels, and perform normal rapid reporting processing on the parallel request groups and query requests.

[0015] The technical advantages of the earthquake rapid reporting information management system and data processing method of the present invention are as follows: This invention employs a dual filtering mechanism combining overall filtering and individual filtering to determine the specific query mode of available channels. This allows for saturation processing during periods of overall high load, while saturation processing is applied only to specific channels during periods of localized high load. This ensures that high-priority query requests receive priority responses, avoiding delays in critical request data. It also prevents server congestion, lag, or even crashes caused by a large number of remote queries consuming server resources. This improves the operational stability of earthquake rapid reporting services and reduces the operational risks associated with earthquake rapid reporting. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of a module of an earthquake rapid reporting information management system provided in Embodiment 1 of the present invention; Figure 2 This is a flowchart illustrating an earthquake rapid reporting data processing method provided in Embodiment 2 of 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.

[0018] Example 1: Please refer to Figure 1 As shown in this embodiment, an earthquake rapid reporting information management system includes: The data synchronization module synchronizes multi-mode seismic data from the static and dynamic levels to the data tables in the benchmark database, and performs rapid reporting matching on the multi-mode seismic data in the data tables, thereby converting the benchmark database into a source database of rapid seismic reporting information. In earthquake rapid reporting information management, all data information of earthquake events needs to be stored in the same database to facilitate subsequent management and query operations of earthquake-related data information. At this time, the database does not exist directly in the existing technology, and the benchmark database needs to be upgraded and updated to obtain the earthquake rapid reporting information source database. Specifically, the earthquake early warning information source database is a database used to store multi-dimensional earthquake data information within different time periods.

[0019] The benchmark database is the basic version for building the earthquake rapid reporting information source database. At this stage, the benchmark database only contains the formal structure of the database and does not contain any substantive content.

[0020] The data in the earthquake early warning information source database usually includes a fixed static layer and a real-time changing dynamic layer, so as to achieve the effect of summarizing and storing multi-mode earthquake data from the past and the present on the timeline. Specifically, the static level refers to the historical time period earlier than the current moment; the dynamic level refers to the time period that corresponds to the current moment in real time.

[0021] Multimode seismic data refers to data imported into a benchmark database that can represent the specific meaning of relevant data information in seismic events, enabling multimode seismic data to comprehensively represent relevant data information of seismic events from different dimensions and modes; Specifically, multi-mode seismic data includes geographic location, time dimension, morphological attributes, and record address.

[0022] Geographical location refers to data information that reflects the actual geographical location of the earthquake event; time dimension refers to data information that reflects different times and durations of the earthquake event; morphological attributes refer to data information that reflects the earthquake type and impact range of the earthquake event; and record address refers to the address where relevant data is recorded and stored in the earthquake event.

[0023] It should be noted that the sources of multi-mode seismic data at the static and dynamic levels are not the same. Specifically, multi-mode seismic data at the static level is obtained by querying historical databases of older versions, while multi-mode seismic data at the dynamic level is obtained by capturing data in real time after accessing seismic network databases of different levels.

[0024] After obtaining the multi-mode seismic data, it is necessary to import the multi-mode seismic data into the data tables of the benchmark database so that the multi-mode seismic data can be matched with the data tables in the benchmark database, thereby providing accurate and clear support for the rapid reporting of subsequent seismic data. In this embodiment, the data table is the specific spatial location provided by the benchmark database for importing multimode seismic data, and provides reasonable distinctions and restrictions for multimode seismic data of different types and meanings; Specifically, the data tables include a catalog table, a timetable, a latitude table, a longitude table, a magnitude table, a depth table, a duration table, a place name table, and a type table.

[0025] It should be noted that the catalog table corresponds to the data storage ID of the earthquake event, the time table corresponds to the occurrence time of the earthquake event, the longitude table corresponds to the geographical location longitude of the earthquake event, the latitude table corresponds to the geographical location latitude of the earthquake event, the magnitude table corresponds to the earthquake magnitude of the earthquake event, the depth table corresponds to the earthquake impact depth of the earthquake event, the duration table corresponds to the shaking duration of the earthquake event, the place name table corresponds to the geographical administrative name of the earthquake event, and the type table corresponds to the earthquake type of the earthquake event.

[0026] It should be noted that the earthquake types in the type table include natural earthquakes, collapses, and suspected explosions; among them, collapses refer to earthquake events caused by the collapse of geological structures, and suspected explosions refer to earthquake events caused by the detonation of artificial explosives.

[0027] When importing multi-mode seismic data into the data table, it is not a one-to-one import. It is necessary to perform rapid reporting matching for different multi-mode seismic data in different seismic events to ensure that the multi-mode seismic data of each seismic event can be stored in an orderly manner. Specifically, the conversion method for the earthquake rapid reporting information source database is as follows: Semantic analysis is performed on each of the A earthquake events, including its geographical location, time dimension, morphological attributes, and record address. The geographical location is used to extract longitude, latitude, and epicenter name; the time dimension is used to extract the start time and duration; the morphological attributes are used to extract the magnitude, depth, and earthquake type; and the record address is used to extract the directory ID. Arrange the catalog table, time table, latitude table, longitude table, magnitude table, depth table, duration table, place name table, and type table in the benchmark database horizontally in sequence to generate A table queues; According to the arrangement of the data tables, the longitude, latitude, epicenter name, time of occurrence, duration, magnitude, depth, earthquake type and directory ID of earthquake event A are summarized in sequence to generate data queue A, and the data in data queue A are imported one by one into the data table of table queue A. B channel ports are set up on the benchmark database, and encrypted access channels are established using these channels as the starting points through the TSL encryption protection protocol, thereby converting the benchmark database into an earthquake rapid reporting information source database. The TSL encryption protection protocol is an encryption protocol used to provide confidentiality, data integrity, and authenticity between two communication applications.

[0028] It should be noted that all numerical data in the data table are pure numbers after removing dimensions, and pure text containing only text; for example, when the time taken is 128.6 seconds, the data in the duration table is 128.6.

[0029] The concurrency prediction module filters available channels from the earthquake rapid reporting information source database, obtains the periodic concurrency characteristics of available channels in the current query period, and predicts the query concurrency of available channels in the next query period through the concurrency prediction model. Available channels are access channels provided to users in the earthquake rapid reporting information source database for earthquake data querying and data interaction, and serve as the necessary path for users to query and access earthquake data in the earthquake rapid reporting information source database. Since the access channels provided to users for accessing and querying the earthquake rapid reporting information source database do not always maintain a stable working state, software network fluctuations at different times may affect the availability of the access channels. Therefore, it is necessary to analyze the specific operating status of the access channels and filter out the available channels that meet the needs of users for querying and accessing. Specifically, the available channel filtering methods are as follows: Find the start time of the first execution data record in the earthquake rapid reporting information source database, take the current time as the end time, and record the time period between the start time and the end time as the verification period; The gateway system queries the packet loss rate and latency of B access channels during the verification period. The difference between the packet loss rate and the standard packet loss threshold is calculated and compared with the standard packet loss threshold to calculate the packet loss over-limit ratio. The standard packet loss threshold is preset and recorded as the maximum packet loss rate of the available channels. It is obtained by averaging the maximum historical packet loss rates. The formula for calculating the packet loss exceeding the limit ratio is: ; In the formula, This is the packet loss exceeding the limit ratio. For packet loss rate, The standard packet loss threshold; The packet loss ratio and latency are compared with the calibrated over-limit ratio and calibrated latency value, respectively. Access channels with a packet loss ratio less than the calibrated over-limit ratio and latency less than the calibrated latency value are marked as candidate channels. The calibrated over-limit ratio and calibrated latency value refer to the maximum values ​​of the packet loss ratio and latency value of the channels marked as available, respectively, which can reflect the performance strength of the available channels from two dimensions. The calibrated over-limit ratio and calibrated latency value are also obtained by averaging historical data. Using a time interval as a standard, mark C non-adjacent verification times in the verification period, and detect the noise power of the candidate channel at the C verification times; With the verification time as the horizontal axis and the noise power as the vertical axis, plot the C noise powers into a noise power graph, and mark the power curves on the noise power graph; The power curve is divided into D segmented curves by taking three verification times as a group, and the slope of each of the D segmented curves is measured one by one by the slope calculation function. The expression for the slope calculation function is: ; In the formula, Let be the slope of the piecewise curve. This represents the noise power at the end of the piecewise curve. This represents the noise power at the tail end of the piecewise curve. This is the end-verification time for the piecewise curve. The end verification time of the piecewise curve; in this embodiment, the end noise power and the tail noise power are directly read from the noise power map, and the duration between the end verification time and the tail verification time in the piecewise curve is the duration span of 5 verification times. The slopes of each of the D piecewise curves are compared with the calibrated slope values. Piecewise curves with slopes greater than the calibrated slope values ​​are marked as fluctuation curves, and the positions of all fluctuation curves are marked in the power curve. The calibrated slope value refers to the minimum slope of the piecewise curve when it is marked as a fluctuation curve, thereby raising the threshold for analyzing and identifying fluctuation curves. The calibrated slope value is also obtained by averaging historical data. When there are no two fluctuating curves in adjacent positions in the power curve, it means that the noise power of the candidate channel corresponding to the power curve is relatively stable during the verification period. In this case, the candidate channel is recorded as a usable channel, and E usable channels are obtained.

[0030] Once an available channel is obtained, it serves as the interactive channel for subsequent data access and querying of the earthquake rapid reporting information source database. Since the capacity of the available channels is fixed and cannot be increased indefinitely, it is necessary to predict and respond in advance to potential surges in the number of access requests that may occur for each available channel in the future.

[0031] The query cycle refers to the specific timeframe for analyzing and predicting changes in the number of queries and accesses in the earthquake rapid reporting information source database and formulating corresponding measures, ensuring that subsequent response measures for the earthquake rapid reporting information source database correspond to changes within a query cycle. Specifically, when setting the query period, the maximum time required for a complete analysis, prediction, and measure formulation of all available channels in the earthquake rapid reporting information source database is recorded as the query period; for example, the query period is 30 seconds.

[0032] Periodic concurrency characteristics refer to the diverse characteristics that the available channels will affect the number of query requests within a query period. Query concurrency refers to the specific number of query requests that the available channels face and respond to within a query period, and different periodic concurrency characteristics correspond to different query concurrency. Specifically, periodic concurrency characteristics include rated concurrency, concurrency surge rate, concurrency dimension value, and peak spurt ratio.

[0033] Rated concurrency refers to the base value of the number of query requests pre-configured when the available channels are initially set up.

[0034] The concurrency surge rate refers to the magnitude of the increase in the number of query requests on available channels per unit of time. The higher the concurrency surge rate, the greater the query concurrency. In this embodiment, when obtaining the concurrency surge rate, the increase in the number of query requests within the query period is compared with the duration of the query period to calculate the concurrency surge rate.

[0035] The concurrency dimension value refers to the maximum number of query requests of the same dimension and type within the available channels. The larger the concurrency dimension value, the greater the query concurrency.

[0036] Peak spike ratio refers to the extent to which the number of query requests executed synchronously by the available channel at a certain moment within the query cycle exceeds the standard number. The larger the peak spike ratio, the greater the query concurrency. The method for obtaining the peak prominence ratio is as follows: Capture all query requests from the available channels during the query period using a packet capture tool, count the number of query requests at the same time, record it as the request concurrency value, and calculate the actual concurrency value by subtracting the request concurrency value from the base concurrency value of the available channels. The actual concurrency value is compared with the calibrated concurrency value. The actual concurrency value that is greater than the calibrated concurrency value is recorded as the effective concurrency value. All effective concurrency values ​​are sorted in descending order to obtain the concurrency queue. The calibrated concurrency value refers to the maximum allowed concurrency value preset in the available channel, which can be used as the upper limit of the number of concurrent requests in the available channel. The duration between the top three valid concurrent values ​​in the concurrent queue is recorded as the concurrent duration. The top three valid concurrent values ​​in the concurrent queue are summed and compared with the concurrent duration to calculate the unit concurrent value. The formula for calculating the unit concurrency value is: ; In the formula, For unit concurrent values, For the first One valid concurrency value, The number of effective concurrent values, =1,2,3 Concurrency duration; The peak-to-peak ratio is calculated by subtracting the unit concurrency value from the unit concurrency limit and then comparing it with the unit concurrency limit. The unit concurrency limit refers to the upper limit of the number of concurrent requests per unit time in the available channel. The formula for calculating the peak prominence ratio is: ; In the formula, The peak prominence ratio, The maximum number of concurrent connections is per unit.

[0037] After obtaining the periodic concurrency characteristics, the query concurrency of available channels in the next query cycle can be predicted in advance using a pre-trained concurrency prediction model. In this embodiment, the concurrency prediction model is an artificial intelligence model based on machine learning technology and LSTM model. It is obtained by training through multiple optimization iterations using a large number of periodic concurrency features and query concurrency as data, so that the concurrency prediction model can predict the query concurrency of the next query cycle after the current query cycle.

[0038] Specifically, when training the concurrency prediction model, it is necessary to collect the periodic concurrency features and query concurrency of multiple query cycles in advance, use the periodic concurrency features as input data, use the query concurrency as output data, and use the query concurrency of the next query cycle as the prediction target. At the same time, a precision threshold for the prediction results of the concurrency prediction model is set until the actual prediction precision reaches the precision threshold, and then the required concurrency prediction model can be obtained. The training process of this concurrency prediction model is existing technology and will not be described in detail here.

[0039] Once the concurrency prediction model is obtained, the periodic concurrency characteristics of the E available channels can be input into the concurrency prediction model to predict the query concurrency of the next query cycle, and finally obtain the E query concurrency values.

[0040] The mode selection module, based on a dual filtering mechanism, performs filtering analysis on query concurrency and determines the query mode of available channels in the next query cycle; the query modes include saturation load mode and normal load mode. After obtaining the query concurrency of the available channels, the query mode of the available channels in the next query cycle is selected and determined based on this, thereby providing an accurate working mode for the data information management and data processing of the earthquake rapid reporting information source database in future moments. Specifically, the query modes include saturation load mode and normal load mode; saturation load mode refers to the available channels being in an overloaded state with high concurrency, while normal load mode refers to the available channels being in a normal load state with low concurrency.

[0041] When determining the query pattern, a dual filtering mechanism is needed for filtering analysis to achieve the analysis effect of query concurrency at two levels. The dual filtering mechanism first filters all available channels as a whole, and then filters each available channel individually; thus, it achieves the effect of filtering available channels from a holistic filtering analysis to an individual filtering analysis, meeting the query needs under different concurrency levels.

[0042] The method for determining the query pattern is as follows: After summing the query concurrency of E available channels, the total concurrency is calculated, and the total concurrency is compared with the calibrated total concurrency threshold. The calibrated total concurrency threshold refers to the maximum value of the total concurrency allowed by all available channels. In this embodiment, the calibrated total concurrency threshold is obtained by summing the rated concurrency of all available channels. When the total concurrency is greater than or equal to the calibrated total concurrency threshold, all E available channels will be subjected to high load query requests. In this case, the query mode of all E available channels in the next query cycle will be determined to be the saturation load mode. When the total concurrency is less than the calibrated total concurrency threshold, the query concurrency of each of the E available channels is compared with the corresponding rated concurrency. If the query concurrency is greater than or equal to the rated concurrency, the available channel will be subjected to a high query request load. In this case, the query mode of the available channel in the next query cycle will be determined as the saturation load mode. If the query concurrency is less than the rated concurrency, the available channel will not be subjected to a high query request load. In this case, the query mode of the available channel in the next query cycle will be determined as the normal load mode.

[0043] It should be noted that the query mode of the available channels can be entirely in saturation load mode, or partially in saturation load mode and partially in normal load mode, in order to meet the actual query request needs of the next query cycle.

[0044] The saturation rapid reporting module, under saturation load mode, divides the query requests in the available channels into high-level requests and general requests, and performs saturation rapid reporting processing on high-level requests and general requests. Under saturation load requests, all available channels are operating at high capacity. Therefore, it is necessary to prioritize or distinguish the query requests based on their importance in order to differentiate between different query requests. In this embodiment, a high-priority request refers to a query request with a higher priority and greater importance, while a general request refers to a query request with a lower priority and less importance.

[0045] Specifically, when classifying query requests, the encryption permissions of all query requests within the available channels are queried one by one. Query requests with encryption permissions higher than the initial permissions of the available channels are recorded as advanced requests, and the remaining query requests are recorded as general requests.

[0046] It should be noted that the initial permissions of the available channels are set in advance when the available channels are set up, and are used to provide a basis for distinguishing the priority of query requests within the available channels.

[0047] After classifying query requests into advanced requests and general requests, it is necessary to perform targeted saturation rapid reporting processing on advanced requests and general requests based on their specific content, so as to retrieve the corresponding earthquake data from the earthquake rapid reporting information source database and display it externally. Specifically, the method for handling saturation rapid reporting is as follows: The high concurrency is calculated by summing up the concurrency of all high-level requests within the available channels, and then compared with the rated concurrency. When the high concurrency is greater than or equal to the rated concurrency, firstly, all high requests are simultaneously queried and matched with the earthquake rapid reporting information source database to obtain the first matching data. Then, in chronological order, the remaining general requests are queried and matched with the earthquake rapid reporting information source database one by one to obtain the second matching data. Finally, the first matching data and the second matching data are aggregated into rapid reporting data and displayed externally. When the advanced concurrency is less than the rated concurrency, firstly, the advanced concurrency is added to the concurrency of general requests one by one to calculate the comprehensive concurrency, until the comprehensive concurrency is greater than or equal to the rated concurrency, or the concurrency of all general requests is added together. Then, the advanced requests and general requests corresponding to the comprehensive concurrency are simultaneously queried and matched with the earthquake rapid reporting information source database to obtain the first matching data. Next, the remaining general requests are queried and matched with the earthquake rapid reporting information source database one by one to obtain the second matching data. Finally, the first matching data and the second matching data are summarized into rapid reporting data and displayed externally.

[0048] It should be noted that the specific content of the first and second matching data is the content of each data table in the earthquake rapid reporting information source database, and simultaneously includes longitude, latitude, epicenter location name, earthquake time, time taken, magnitude, depth, earthquake type and directory ID. After being summarized into rapid reporting data, it will be output and displayed after being sorted by position according to the data table arrangement.

[0049] The routine rapid reporting module, under normal load mode, filters out parallel request groups from the query requests of available channels and performs routine rapid reporting processing on the parallel request groups and query requests. Under normal load conditions, some available channels are operating at low load. At this time, the number of query requests in the available channel is not large. Therefore, query requests of the same type can be aggregated to form a parallel request group. In this embodiment, a parallel request group refers to a collection of query requests whose request timelines are in the same query period.

[0050] Specifically, when filtering parallel request groups from query requests, semantic parsing is performed on all query requests within the available channels one by one, the request timeline of the query requests is marked, query requests with the same query timeline are recorded as parallel requests, and parallel requests with the same query timeline are aggregated to generate parallel request groups.

[0051] It should be noted that each parallel request group contains at least two parallel requests, and the number of parallel request groups is also fixed.

[0052] After filtering out the parallel request group from the query requests, it is necessary to perform targeted routine rapid reporting processing on the parallel request group and ordinary query requests, so that the corresponding earthquake data can be retrieved from the earthquake rapid reporting information source database and displayed externally. Specifically, the standard procedure for handling rapid reports is as follows: First, in chronological order of the requests, the parallel request groups are queried and matched with the earthquake rapid reporting information source database to obtain matching data groups. Then, the matching data groups are split using a splitting technique to obtain the third matching data corresponding to the parallel requests. Next, the remaining query requests are simultaneously queried and matched with the earthquake rapid reporting information source database to obtain the fourth matching data. Finally, the third and fourth matching data are aggregated into rapid reporting data and displayed externally.

[0053] It should be noted that the specific content of the third and fourth matching data is the content of each data table in the earthquake rapid reporting information source database, and simultaneously includes longitude, latitude, epicenter location name, earthquake time, time taken, magnitude, depth, earthquake type and directory ID.

[0054] As a supplement to the above embodiments, after the user sends a query request to the earthquake rapid reporting information management system, it queries and matches 4 sets of matching data from the earthquake rapid reporting information source database, summarizes and arranges the 4 sets of matching data, and outputs and displays them to form an earthquake rapid reporting report. Specifically, the earthquake velocity report contains the following information: Earthquake rapid report Example 2: Please refer to Figure 2 As shown, parts not described in detail in this embodiment are described in Embodiment 1. This embodiment provides a method for processing earthquake rapid reporting data, implemented based on an earthquake rapid reporting information management system, including: S01: Data synchronization module, which synchronizes multi-mode seismic data from static and dynamic levels to the data tables of the benchmark database for rapid reporting and matching, and converts the benchmark database into a source database of earthquake rapid reporting information; S02: Based on the periodic concurrency characteristics of available channels in the earthquake rapid reporting information source database during the query period, predict the query concurrency of available channels in the next query period. S03: Based on a dual filtering mechanism, the query concurrency is filtered and analyzed to determine the query mode of the available channels in the next query cycle. The query modes include saturation load mode and normal load mode. S04: In saturated load mode, query requests for available channels are divided into high-level requests and general requests, and saturated rapid reporting is performed on high-level requests and general requests. S05: Under normal load mode, select parallel request groups from the query requests of available channels, and perform normal rapid reporting processing on the parallel request groups and query requests.

[0055] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. An earthquake rapid reporting information management system, characterized in that, include: The data synchronization module synchronizes multi-mode seismic data from the static and dynamic levels to the data tables in the benchmark database for rapid reporting and matching, and converts the benchmark database into a source database for rapid seismic reporting information. The concurrency prediction module predicts the query concurrency of available channels in the next query period by using the periodic concurrency characteristics of available channels in the earthquake rapid reporting information source database during the query period. The periodic concurrency characteristics include rated concurrency, concurrency surge rate, concurrency dimension value, and peak prominence ratio. The mode selection module, based on a dual filtering mechanism, performs filtering analysis on query concurrency to determine the query mode of available channels in the next query cycle. The query modes include saturation load mode and normal load mode. The saturation rapid reporting module, under saturation load mode, divides the query requests of available channels into high-level requests and general requests, and performs saturation rapid reporting processing on high-level requests and general requests. The routine rapid reporting module, under normal load mode, filters out parallel request groups from the query requests of available channels and performs routine rapid reporting processing on the parallel request groups and query requests.

2. The earthquake rapid reporting information management system according to claim 1, characterized in that, Multimode earthquake data includes geographic location, time dimension, morphological attributes, and record address; data tables include catalog table, time table, latitude table, longitude table, magnitude table, depth table, duration table, place name table, and type table; The conversion method for the earthquake rapid reporting information source database is as follows: From the geographical location of earthquake event A, we can extract the longitude, latitude, and epicenter name; from the time dimension, we can extract the earthquake time and time taken; from the morphological attributes, we can extract the magnitude, depth, and earthquake type; and from the record address, we can extract the directory ID. Arrange the catalog table, time table, latitude table, longitude table, magnitude table, depth table, duration table, place name table, and type table in the benchmark database horizontally in sequence to generate A table queues; According to the arrangement of the data tables, the longitude, latitude, epicenter name, time of occurrence, duration, magnitude, depth, earthquake type and directory ID of earthquake event A are summarized in sequence to generate data queue A, and the data in data queue A are imported one by one into the data table of table queue A. B channel ports are set up on the benchmark database, and encrypted access channels are established on the channel ports through the TSL encryption protection protocol, so as to convert the benchmark database into an earthquake rapid reporting information source database.

3. The earthquake rapid reporting information management system according to claim 2, characterized in that, The available channel filtering method is as follows: Find the start time of the first execution data record in the earthquake rapid reporting information source database, take the current time as the end time, and record the time period between the start time and the end time as the verification period; The gateway system queries the packet loss rate and latency of B access channels during the verification period. The difference between the packet loss rate and the standard packet loss threshold is calculated and compared with the standard packet loss threshold to calculate the packet loss over-limit ratio. Access channels with a packet loss over-limit ratio less than the specified over-limit ratio and a latency less than the specified latency value are recorded as candidate channels. Using a time interval as a standard, mark C non-adjacent verification times in the verification period, detect the noise power of the candidate channel at the C verification times, and plot the C noise powers into a noise power graph with the verification time as the horizontal axis and the noise power as the vertical axis, and mark the power curves in the noise power graph. The power curve is divided into D segmented curves, with three verification times as a group. The slope of each of the D segmented curves is measured one by one using the slope calculation function. The segmented curves with a slope greater than the calibrated slope value are recorded as fluctuation curves, and the positions of all fluctuation curves are marked in the power curve. The candidate channels in the power curve that do not have two adjacent fluctuation curves are recorded as available channels, resulting in E available channels.

4. The earthquake rapid reporting information management system according to claim 3, characterized in that, The method for obtaining the peak prominence ratio is as follows: Capture all query requests from the available channels during the query period using a packet capture tool, count the number of query requests at the same time, record it as the request concurrency value, and calculate the actual concurrency value by subtracting the request concurrency value from the base concurrency value of the available channels. The actual concurrency values ​​that are greater than the specified concurrency value are recorded as valid concurrency values, and all valid concurrency values ​​are sorted in descending order to obtain a concurrency queue; The duration between the top three valid concurrent values ​​in the concurrent queue is recorded as the concurrent duration. The top three valid concurrent values ​​in the concurrent queue are summed and compared with the concurrent duration to calculate the unit concurrent value. The peak prominence ratio is calculated by subtracting the unit concurrency value from the unit concurrency upper limit and then comparing it with the unit concurrency upper limit.

5. The earthquake rapid reporting information management system according to claim 4, characterized in that, The dual filtering mechanism is as follows: first, all available channels are filtered as a whole, and then each available channel is filtered individually. The method for determining the query pattern is as follows: The total concurrency is calculated by summing the query concurrency of the E available channels; When the total concurrency is greater than or equal to the calibrated total concurrency threshold, all E available channels will be set to saturation load mode in the next query cycle. When the total concurrency is less than the calibrated total concurrency threshold, the query concurrency of each of the E available channels is compared with the corresponding rated concurrency. If the query concurrency is greater than or equal to the rated concurrency, the available channel will be determined to be in saturation load mode in the next query cycle; If the query concurrency is less than the rated concurrency, the available channels will be determined to be in normal load mode in the next query cycle.

6. The earthquake rapid reporting information management system according to claim 5, characterized in that, When distinguishing between advanced requests and general requests, the encryption permissions of all query requests within the available channels are queried one by one. Query requests with encryption permissions higher than the initial permissions of the available channels are recorded as advanced requests, and the remaining query requests are recorded as general requests.

7. The earthquake rapid reporting information management system according to claim 6, characterized in that, The method for handling saturation rapid reports is as follows: The advanced concurrency is calculated by summing up the concurrency of all advanced requests within the available channels. When the high concurrency is greater than or equal to the rated concurrency, all high-level requests are simultaneously queried and matched with the earthquake rapid reporting information source database to obtain the first matching data. In chronological order, the remaining general requests are queried and matched with the earthquake rapid reporting information source database one by one to obtain the second matching data. The first and second matching data are then combined into rapid reporting data and displayed externally. When the high concurrency is less than the rated concurrency, the high concurrency is added to the concurrency of the general requests one by one to calculate the total concurrency, until the total concurrency is greater than or equal to the rated concurrency, or the concurrency of all general requests is added together. The high-level requests and general requests corresponding to the overall concurrency are simultaneously matched with the earthquake rapid reporting information source database to obtain the first matching data. The remaining general requests are matched with the earthquake rapid reporting information source database one by one to obtain the second matching data. The first matching data and the second matching data are then combined into rapid reporting data and displayed externally.

8. The earthquake rapid reporting information management system according to claim 7, characterized in that, When filtering parallel request groups, semantic parsing is performed on all query requests in the available channels one by one, the request timeline of the query request is marked, query requests with the same query period are recorded as parallel requests, and parallel requests with the same query period are aggregated to generate parallel request groups.

9. An earthquake rapid reporting information management system according to claim 8, characterized in that, During routine rapid reporting processing, parallel request groups are matched with the earthquake rapid reporting information source database according to the order of the request timeline to obtain matching data groups. The matching data groups are then split into third matching data groups using a splitting technique. The remaining query requests are simultaneously matched with the earthquake rapid reporting information source database to obtain fourth matching data groups. The third and fourth matching data groups are then combined into rapid reporting data and displayed externally.

10. A method for processing earthquake rapid reporting data, implemented based on an earthquake rapid reporting information management system according to any one of claims 1-9, characterized in that, include: S01: Data synchronization module, which synchronizes multi-mode seismic data from static and dynamic levels to the data tables of the benchmark database for rapid reporting and matching, and converts the benchmark database into a source database of earthquake rapid reporting information; S02: Based on the periodic concurrency characteristics of available channels in the earthquake rapid reporting information source database during the query period, predict the query concurrency of available channels in the next query period. S03: Based on a dual filtering mechanism, the query concurrency is filtered and analyzed to determine the query mode of the available channels in the next query cycle. The query modes include saturation load mode and normal load mode. S04: In saturated load mode, query requests for available channels are divided into high-level requests and general requests, and saturated rapid reporting is performed on high-level requests and general requests. S05: Under normal load mode, select parallel request groups from the query requests of available channels, and perform normal rapid reporting processing on the parallel request groups and query requests.

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