Submission state processing method and device based on water level flow data and medium

Abstract

The invention discloses a submission state processing method and device based on water level flow data and a medium, and relates to the technical field of water conservancy data processing, and the method comprises the steps: obtaining water level flow report data, and carrying out the report state analysis of the water level flow report data, so as to determine the report state data of the water level flow report data; based on the reported state data, tracing through a water level flow station to obtain transaction blocking nodes; carrying out report state recovery analysis on the transaction blocking nodes to determine an exception recovery strategy of the water level flow report data; according to the exception recovery strategy, obtaining a predicted recovery time threshold value of the water level flow reporting data through self-adaptive optimization in a hydrological time domain environment; and based on the predicted recovery time threshold value, performing periodic optimization on the exception recovery strategy to obtain a self-adaptive exception recovery strategy. According to the method, the technical problem that reporting of the water level flow data of the water level station is blocked in the prior art is solved.

Description

Technical Field

[0001] This application relates to the technical field of water conservancy data processing, and particularly to a method, device, and medium for processing the submission status based on water level and flow data. Background Art

[0002] With the continuous development of digital water conservancy projects, digital water conservancy projects based on the Digital Twin system have gradually become the mainstream analysis system. Based on the digital twin water conservancy analysis system, the deployment of flow observation devices in each basin has also increased day by day. Generally, flow observation devices include radar water level gauges, electronic water gauges, float-type water level gauges, etc. The data reporting frequency of the above flow observation data is relatively frequent.

[0003] A data transaction is an operation sequence that accesses and may operate on various data items. These operations have the characteristics of either all being executed or all not being executed, and a data transaction is an indivisible unit of work. In the case of batch data upload (insert into) involving various parameters of water level and flow, or updating (update) other water conservancy data, the data transaction may cause congestion of water conservancy platform data and paralysis of the application system due to untimely submission or data retention of water level and flow data. Moreover, as the number of flow observation devices deployed in each basin increases, the prior art cannot meet the adaptive processing of abnormal water level and flow reporting. Summary of the Invention

[0004] Embodiments of this application provide a method, device, and medium for processing the submission status based on water level and flow data, which solves the technical problem of data reporting congestion of water level and flow data at a water level station in the prior art.

[0005] In a first aspect, embodiments of this application provide a method for processing the submission status based on water level and flow data, characterized in that the method includes: obtaining water level and flow reporting data, and analyzing the reporting status of the water level and flow reporting data to determine the reporting status data of the water level and flow reporting data; based on the reporting status data, tracing back through the water level and flow station to obtain the transaction congestion node; analyzing the reporting status recovery of the transaction congestion node to determine the abnormal recovery strategy of the water level and flow reporting data; according to the abnormal recovery strategy, obtaining the expected recovery time threshold of the water level and flow reporting data through adaptive optimization in the hydrological time domain environment; based on the expected recovery time threshold, performing periodic optimization on the abnormal recovery strategy to obtain an adaptive abnormal recovery strategy.

[0006] In an implementation manner of the present application, the reporting status of the water level and flow rate reporting data is analyzed to determine the reporting status data of the above water level and flow rate reporting data, which specifically includes: based on the water level and flow rate reporting data, querying through the pg_stat_activity view to determine the first blocked session; where the fields corresponding to the first blocked session include: uncommitted transactions, in-execution queries; determining the lock holding status of the first blocked session to obtain the second blocked session; where the lock holding status for determining the lock holding status includes: lock holding status, lock waiting status; converting the second blocked session into an output table format to determine the above reporting status data.

[0007] In an implementation manner of the present application, based on the reporting status data, the transaction block node is obtained through tracing of the water level and flow rate station, which specifically includes: performing transaction positioning on the reporting status data to determine the uncommitted transaction data; extracting the station field of the uncommitted transaction data and performing flow station mapping matching on the station field to obtain the water level and flow rate station data of the uncommitted transaction data; based on the water level and flow rate station data, determining the transaction block node through uncommitted transaction duration series analysis.

[0008] In an implementation manner of the present application, the reporting status recovery analysis is performed on the transaction block node to determine the abnormal recovery strategy of the water level and flow rate reporting data, which specifically includes: based on the transaction block node, dividing through the data transaction reporting queue to determine the reporting queue type of the above water level and flow rate reporting data; where the reporting queue type includes: single queue single task, single queue multi-task; in the case where the reporting queue type is single queue single task, canceling the reporting of the water level and flow rate reporting transaction corresponding to the reporting queue type to obtain the first abnormal recovery strategy; in the case where the reporting queue type is single queue multi-task, constructing the priority label of the above water level and flow rate reporting data and, based on the priority label, adjusting through the reporting queue to obtain the second abnormal recovery strategy; determining the abnormal recovery strategy according to the first abnormal recovery strategy or the second abnormal recovery strategy.

[0009] In an implementation manner of the present application, according to the abnormal recovery strategy, the estimated recovery time threshold of the water level and flow rate reporting data is obtained through adaptive optimization in the hydrological time domain environment, which specifically includes: obtaining the recovery time threshold of the abnormal recovery strategy and performing seasonal division on the recovery time threshold to obtain the seasonal recovery time threshold; based on the seasonal recovery time threshold, determining the abnormal determination baseline threshold through adaptive processing of the recovery threshold; adjusting the periodic threshold range of the abnormal determination baseline threshold to obtain the estimated recovery time threshold.

[0010] In an implementation manner of the present application, based on the seasonal recovery time threshold, through adaptive processing of the recovery threshold, an abnormal determination baseline threshold is determined, which specifically includes: performing data preprocessing on the seasonal recovery time threshold to obtain a baseline recovery time threshold; obtaining transaction backlog value data, and based on the transaction backlog value, through backlog sensitivity analysis, obtaining a transaction backlog critical parameter; wherein, the transaction backlog value data includes: the current transaction backlog value and the peak backlog value; according to the transaction backlog critical parameter, performing seasonal adaptive processing on the baseline recovery time threshold to obtain an abnormal determination baseline threshold.

[0011] In an implementation manner of the present application, based on the predicted recovery time threshold, the abnormal recovery strategy is cyclically optimized to obtain an adaptive abnormal recovery strategy, which specifically includes: inserting the predicted recovery time threshold into the preset time threshold field of the abnormal recovery strategy to obtain an updated strategy of the abnormal recovery strategy; cyclically monitoring the updated strategy of the abnormal recovery strategy, and performing queue adaptability analysis on the monitoring data obtained by the cyclic monitoring to determine the updated strategy adaptability; based on the updated strategy adaptability, constructing a mapping relationship between the updated strategy and the water level flow reporting queue to obtain an adaptive abnormal recovery strategy.

[0012] In an implementation manner of the present application, before obtaining the water level flow reporting data, the method further includes: constructing a water level data table, and performing interface configuration of the water level flow station on the water level flow table to obtain a water level flow data table; based on the water level flow data table, through unified configuration of the water level flow device fields, obtaining unified fields of the water level flow device.

[0013] In a second aspect, an embodiment of the present application further provides a submission status processing device based on water level flow data, characterized in that the device includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein, the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to: obtain water level flow reporting data, and perform reporting status analysis on the water level flow reporting data to determine the reporting status data of the water level flow reporting data; based on the reporting status data, through water level flow station traceability, obtain a transaction blockage node; perform reporting status recovery analysis on the transaction blockage node to determine an abnormal recovery strategy for the water level flow reporting data; according to the abnormal recovery strategy, through adaptive optimization in the hydrological time domain, obtain a predicted recovery time threshold for the water level flow reporting data; based on the predicted recovery time threshold, cyclically optimize the abnormal recovery strategy to obtain an adaptive abnormal recovery strategy.

[0014] In a third aspect, an embodiment of the present application further provides a non-volatile computer storage medium for processing the submission status based on water level and flow data, storing computer-executable instructions, characterized in that the computer-executable instructions are set to: obtain the water level and flow report data, and perform a submission status analysis on the water level and flow report data to determine the submission status data of the above water level and flow report data; based on the submission status data, trace back through the water level and flow station to obtain the transaction blockage node; perform a submission status recovery analysis on the transaction blockage node to determine the abnormal recovery strategy of the water level and flow report data; according to the abnormal recovery strategy, obtain the estimated recovery time threshold of the water level and flow report data through adaptive optimization in the hydrological time domain environment; based on the estimated recovery time threshold, perform periodic optimization on the abnormal recovery strategy to obtain an adaptive abnormal recovery strategy.

[0015] An embodiment of the present application provides a method, device and medium for processing the submission status based on water level and flow data. By combining the analysis of the data transaction blockage status reported by the water level and flow, the abnormal processing analysis according to different queues, and the adaptive update of the abnormal recovery strategy for the water level and flow report under seasonal factors, the technical problem of data submission blockage of the water level and flow at the water level station in the prior art is solved, the abnormal processing of the water level and flow report transaction is realized quickly, the data stability and transaction fluency of the water conservancy platform are enhanced, and the probability of long-term blockage of the water level and flow report transaction is reduced. Brief Description of the Drawings

[0016] The drawings described herein are used to provide a further understanding of the present application, and constitute a part of the present application. The illustrative embodiments and descriptions thereof of the present application are used to explain the present application and do not constitute an improper limitation to the present application. In the drawings: Figure 1 It is a flowchart of a method for processing the submission status based on water level and flow data provided by an embodiment of the present application; Figure 2 It is a schematic internal structure diagram of a device for processing the submission status based on water level and flow data provided by an embodiment of the present application. Detailed Embodiments

[0017] To make the objectives, technical solutions and advantages of the present application clearer, the technical solutions of the present application will be clearly and completely described below in conjunction with the specific embodiments of the present application and the corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

[0018] The embodiments of the present application provide a method, device, and medium for processing the submission status based on water level and flow data. By combining the analysis of data transaction congestion status reported by water level and flow, the analysis of exception handling for different queues, and the adaptive update of the abnormal recovery strategy for water level and flow reporting under seasonal factors, the technical problem of data reporting congestion of water level and flow at water level stations in the prior art is solved, the abnormal processing of water level and flow reporting transactions is realized quickly, the data stability and transaction fluency of the water conservancy platform are enhanced, and the probability of long-term congestion of water level and flow reporting transactions is reduced.

[0019] The technical solutions proposed in the embodiments of the present application are described in detail below with reference to the accompanying drawings.

[0020] Figure 1 It is a flowchart of a method for processing the submission status based on water level and flow data provided by the embodiments of the present application. As Figure 1 shown, a method for processing the submission status based on water level and flow data provided by the embodiments of the present application specifically includes the following steps: Step 101, obtain the water level and flow reporting data, and perform a reporting status analysis on the water level and flow reporting data to determine the reporting status data of the above water level and flow reporting data.

[0021] Exemplarily, in the digital transaction reporting process of the hydrological platform, due to the inherent attributes of data transactions, if a certain transaction item stalls at this stage due to some error or special situation during the reporting process, it will cause all subsequent transactions in the same queue to be blocked at this node. Through the reporting status analysis, users can quickly find the status of the current water conservancy reporting transaction, improving the processing efficiency of abnormal reporting status.

[0022] Specifically, performing a reporting status analysis on the water level and flow reporting data to determine the reporting status data of the above water level and flow reporting data includes: based on the water level and flow reporting data, querying through the pg_stat_activity view to determine the first blocked session; wherein, the fields corresponding to the first blocked session include: uncommitted transactions, queries in execution; determining the second blocked session by judging the lock holding status of the first blocked session; wherein, the lock holding status for the lock holding status judgment includes: lock holding status, lock waiting status; converting the second blocked session into an output table format to determine the above reporting status data.

[0023] Before obtaining the water level and flow reporting data, the method further includes: constructing a water level data table, and performing interface configuration of the water level and flow stations on the water level and flow table to obtain a water level and flow data table; based on the water level and flow data table, performing unified configuration of water level and flow device fields to obtain unified fields of water level and flow devices.

[0024] In one embodiment, the creation of the water level and flow data table is represented by the following parts: hyldb=>create table r_wl_hulsq (stcd int, stcdnm varchar(100), rz varchar(50), tm timestamp); CREATE TABLE For different water level and flow rate devices, since their initial numbers are marked according to the national standard scheme, in actual applications, counting the water level and flow rate device numbers according to the original numbers will result in overly complex data. Therefore, the devices are uniformly numbered again, and the reported data is based on the device numbers, which is represented by the following part: hyldb=>insert into r_wl_hulsq values (5310001, 'A Ridge Outflow Discharge Station', '91.23', now()); INSERT 0 1 hyldb=>insert into r_wl_hulsq values (5310002, 'B Ridge Outflow Discharge Station', '90.41', now()); INSERT 0 1 hyldb=>select * from r_wl_hulsq; stcd|stcdnm|rz|tm ---------+------------------+-------+---------------------------- 5310001 | A Ridge Outflow Discharge Station | 91.23 | 202x-1x-27 15:13:27.064xxx 5310002 | B Ridge Outflow Discharge Station | 90.41 | 202x-1x-27 15:13:52.867xxx Among them, data is reported for a water level and flow rate station at the A Ridge Outflow Discharge Station, but no transaction is committed. The water conservancy platform gets: hyldb=>begin; BEGIN hyldb=>insert into r_wl_hulsq values (5310001, 'Hubu Ridge Outflow Discharge Station', '91.22', now()); INSERT 0 1 hyldb=>select * from r_wl_hulsq; station code|station name|water level|time ---------+------------------+-------+---------------------------- 5310001 | A Ridge Outflow Discharge Station | 91.23 | 202x-1x-27 15:13:27.064xxx 5310002 | B Ridge Outflow Discharge Station | 90.41 | 202x-1x-27 15:13:52.867xxx 5310001 | A Ridge Outflow Discharge Station | 91.22 | 202x-1x-27 15:17:18.891xxx hydraulic database => update r_wl_hulsq set rz='91.99' where stcd=5310001; UPDATE 2 hydraulic database => BEGIN hydraulic database => insert into r_wl_hulsq values(5310002,'B Ridge Reservoir Outflow Discharge Station','89.98',now()); INSERT 0 1 hydraulic database => update r_wl_hulsq set rz='91.97' where stcd=5310001; This transaction has been in an uncommitted state, which will subsequently prevent other water level and flow stations from submitting data. At this time, another hydrological flow station also attempts to report data, resulting in an inability to operate.

[0025] Step 102: Based on the reported status data, trace back through the water level and flow stations to obtain the transaction blocking nodes.

[0026] Exemplarily, after determining the transaction status of each hydrological station, due to the very high frequency of data reported by the water level and flow observation equipment, it is necessary to promptly identify the water level and flow reporting data corresponding to transactions that have not been submitted in the transaction system in a timely manner. By tracing back through the water level and flow stations, determine the nodes where the transaction is blocked to identify the abnormal transactions that need to be processed.

[0027] Specifically, based on the reported status data, the transaction blocking nodes are obtained by tracing back through the water level and flow stations, including: performing transaction location on the reported status data to determine uncommitted transaction data; extracting the station fields of the uncommitted transaction data, and performing flow station mapping and matching on the station fields to obtain the water level and flow station data of the uncommitted transaction data; based on the water level and flow station data, determining the transaction blocking nodes through the analysis of the uncommitted transaction duration sequence.

[0028] In one embodiment, the tracing back through the water level and flow stations is explained by the following parts: First, perform transaction location on the reported status data and extract the station fields of the uncommitted transaction data; hyldb=>select datname,usename,state,query from pg_stat_activity; datname| usename|state|query ----------+----------+---------------------+----------------------------------------------------------- hyldb| hyl| idle in transaction | update r_wl_hulsq set rz='91.99'where stcd=5310001; hyldb| hyl| active| update r_wl_hulsq set rz='91.97' where stcd=5310001; Then, cancel the transactions of the reported water level and flow stations; hyldb=>select pg_terminate_backend(28076) from pg_stat_activity; pg_terminate_backend ---------------------- t (1 row record) hyldb=> It should be noted that for the water level station reporting transaction with the longest time in the abnormal state, it is first determined as the transaction blocking node.

[0029] Through tracing by water level and flow rate stations, mapping and matching of relevant water level and flow rate stations, and time series analysis, the rapid judgment of transaction blockage nodes is realized, and the efficiency of discovering transaction blockage nodes is improved.

[0030] Step 103: Conduct an analysis on the reporting status recovery of transaction blockage nodes to determine the abnormal recovery strategy for water level and flow rate reporting data.

[0031] Exemplarily, due to the queue situation of data transactions and the differences in transaction blockage nodes, different recoveries need to be performed on them to enable the continuous reporting of data transactions.

[0032] Specifically, conducting an analysis on the reporting status recovery of transaction blockage nodes to determine the abnormal recovery strategy for water level and flow rate reporting data specifically includes: based on transaction blockage nodes, through the division of data transaction reporting queues, determining the reporting queue type of the above-mentioned water level and flow rate reporting data; where the reporting queue type includes: single queue single task, single queue multiple tasks; in the case where the reporting queue type is single queue single task, cancel the reporting of the water level and flow rate reporting transaction corresponding to the reporting queue type to obtain the first abnormal recovery strategy; in the case where the reporting queue type is single queue multiple tasks, construct the priority label of the above-mentioned water level and flow rate reporting data, and based on the priority label, through adjusting the reporting queue, obtain the second abnormal recovery strategy; determine the abnormal recovery strategy according to the first abnormal recovery strategy or the second abnormal recovery strategy.

[0033] In one embodiment, for a single queue single task, canceling the transaction of the water level and flow rate station with abnormal reporting can enable subsequent transactions to complete reporting, which is specifically explained through the following part: hyldb=>select pg_terminate_backend(28076) from pg_stat_activity; pg_terminate_backend ---------------------- t (1 row record) hyldb=> After canceling the transaction of the water level and flow rate station with reporting canceled, the blockage of other water level and flow rate stations will be lifted, and data can continue to be reported normally.

[0034] For the case of single queue multiple tasks, it is necessary to analyze different water level and flow rate stations. For transactions exceeding the preset reporting threshold, the transaction will be automatically lifted.

[0035] Step 104: According to the abnormal recovery strategy, through adaptive optimization in the hydrological time domain environment, obtain the predicted recovery time threshold for water level and flow rate reporting data.

[0036] Exemplarily, due to the recovery time threshold Specifically, according to the abnormal recovery strategy, through adaptive optimization in the hydrological time domain environment, the predicted recovery time threshold of the water level and flow reporting data is obtained, which specifically includes: obtaining the recovery time threshold of the abnormal recovery strategy and performing seasonal division on the recovery time threshold to obtain the seasonal recovery time threshold; based on the seasonal recovery time threshold, through adaptive processing of the recovery threshold, determining the abnormal determination baseline threshold; and adjusting the periodic threshold range of the abnormal determination baseline threshold to obtain the predicted recovery time threshold.

[0037] Furthermore, based on the seasonal recovery time threshold, through adaptive processing of the recovery threshold, determining the abnormal determination baseline threshold specifically includes: performing data preprocessing on the seasonal recovery time threshold to obtain the baseline recovery time threshold; obtaining the transaction backlog value data, and based on the transaction backlog value, through backlog sensitivity analysis, obtaining the transaction backlog critical parameter; where the transaction backlog value data includes: the current transaction backlog value, the peak backlog value; according to the transaction backlog critical parameter, performing seasonal adaptive processing on the baseline recovery time threshold to obtain the abnormal determination baseline threshold.

[0038] In one embodiment, the load difference between the rainy season (June - September) and the dry season (December - February) is significant, and it is necessary to dynamically adjust the recovery time threshold to ensure the rapid recovery of abnormal data backlog.

[0039] First, through adaptive processing of the recovery threshold, the abnormal determination baseline threshold is determined, and the abnormal determination baseline threshold is 10 minutes. Configure the seasonal coefficient according to different seasons, where the seasonal coefficient in the rainy season is and the seasonal coefficient in the dry season is and the seasonal coefficient in the normal season is .

[0040] Then, obtain the transaction backlog value data, where the current transaction backlog value B1 = 1200 items and the historical peak backlog value B2 = 1500 items.

[0041] The ratio of the current transaction backlog value to the historical peak backlog value characterizes the severity of the current transaction backlog, and multiplying it by the backlog sensitivity coefficient can characterize the critical degree of the current transaction backlog, that is, the transaction backlog critical parameter.

[0042] Finally, since the relationship between the seasonal coefficient and the abnormal determination baseline threshold is positively correlated, given the known recovery time threshold, the predicted recovery time threshold can be determined.

[0043] Step 105: Based on the predicted recovery time threshold, perform periodic optimization on the abnormal recovery strategy to obtain an adaptive abnormal recovery strategy.

[0044] Through periodic optimization of the exception recovery strategy, this application obtains an adaptive exception recovery strategy, realizes further update and optimization of the exception recovery strategy, and enhances the adaptability and robustness of the exception recovery strategy for transaction reporting exceptions.

[0045] Specifically, based on the predicted recovery time threshold, the exception recovery strategy is periodically optimized to obtain an adaptive exception recovery strategy, which specifically includes: inserting the predicted recovery time threshold into the preset time threshold field of the exception recovery strategy to obtain an updated strategy for the exception recovery strategy; periodically monitoring the updated strategy of the exception recovery strategy, and performing queue adaptability analysis on the monitoring data obtained from the periodic monitoring to determine the adaptability of the updated strategy; based on the adaptability of the updated strategy, constructing a mapping relationship between the updated strategy and the water level and flow reporting queue to obtain an adaptive exception recovery strategy.

[0046] The above is the method embodiment proposed by this application. Based on the same inventive concept, the embodiments of this application also provide a submission status processing device based on water level and flow data, and its structure is as Figure 2 shown.

[0047] Figure 2 It is a schematic internal structure diagram of a submission status processing device based on water level and flow data provided by the embodiment of this application. As Figure 2 shown, the device includes: At least one processor 201; And a memory 202 communicatively connected to at least one processor; Wherein, the memory 202 stores instructions executable by at least one processor, and the instructions are executed by at least one processor 201, so that at least one processor 201 can: Obtain water level and flow reporting data, and perform reporting status analysis on the water level and flow reporting data to determine the reporting status data of the above water level and flow reporting data; based on the reporting status data, trace back through the water level and flow station to obtain the transaction blocking node; perform reporting status recovery analysis on the transaction blocking node to determine the exception recovery strategy of the water level and flow reporting data; according to the exception recovery strategy, through adaptive optimization in the hydrological time domain, obtain the predicted recovery time threshold of the water level and flow reporting data; based on the predicted recovery time threshold, periodically optimize the exception recovery strategy to obtain an adaptive exception recovery strategy.

[0048] Some embodiments of this application provide a non-volatile computer storage medium corresponding to Figure 1 for submission status processing based on water level and flow data, storing computer-executable instructions, and the computer-executable instructions are set as: Obtain the reported water level and flow data, and conduct an analysis of the reporting status of the reported water level and flow data to determine the reporting status data of the above-mentioned reported water level and flow data; based on the reporting status data, trace back through the water level and flow station to obtain the transaction blocking node; conduct an analysis of the reporting status recovery of the transaction blocking node to determine the abnormal recovery strategy for the reported water level and flow data; according to the abnormal recovery strategy, through adaptive optimization in the hydrological time domain environment, obtain the predicted recovery time threshold for the reported water level and flow data; based on the predicted recovery time threshold, conduct periodic optimization of the abnormal recovery strategy to obtain an adaptive abnormal recovery strategy.

[0049] Each embodiment in this application is described in a progressive manner. For the same or similar parts among the embodiments, reference can be made to each other. Each embodiment focuses on the differences from other embodiments. In particular, for the embodiments of the Internet of Things devices and media, since they are basically similar to the method embodiments, the description is relatively simple, and for the relevant parts, reference can be made to the partial description of the method embodiments.

[0050] The systems and media provided in the embodiments of this application correspond one-to-one with the methods. Therefore, the systems and media also have beneficial technical effects similar to those of their corresponding methods. Since the beneficial technical effects of the methods have been described in detail above, the beneficial technical effects of the systems and media will not be elaborated here.

[0051] Those skilled in the art should understand that the embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, this application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0052] This application is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of this application. It should be understood that each process and / or block in the flowchart and / or block diagram can be implemented by computer program instructions, and the combination of processes and / or blocks in the flowchart and / or block diagram can also be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate a device for implementing the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0053] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory produce a manufacture including an instruction device that implements the functions specified in one or more processes and / or blocks Figure 1 in one or more processes and / or blocks Figure 1 specified in the function.

[0054] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to produce a computer-implemented process, so that the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one or more processes and / or blocks Figure 1 in one or more processes and / or blocks Figure 1 specified in the function.

[0055] In a typical configuration, a computing device includes one or more processors (CPUs), an input / output interface, a network interface, and memory.

[0056] The memory may include non-permanent memory in the form of computer-readable media, random access memory (RAM), and / or non-volatile memory such as read-only memory (ROM) or flash memory (flash RAM). Memory is an example of computer-readable media.

[0057] Computer-readable media includes permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology for information storage. The information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory, or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD), or other optical storage, magnetic cassette tapes, magnetic tape magnetic disk storage, or other magnetic storage devices, or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory computer-readable media, such as modulated data signals and carrier waves.

[0058] It should also be noted that the term "including", "comprising" or any other variant thereof is intended to cover non-exclusive inclusion, such that a process, method, commodity or device comprising a series of elements not only includes those elements but also includes other elements not expressly listed, or further includes elements inherent to such process, method, commodity or device. Without further limitation, an element defined by the statement "comprising an..." does not exclude the presence of additional identical elements in the process, method, commodity or device comprising the element.

[0059] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, various changes and modifications can be made to the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included within the scope of the claims of the present application.

Claims

1. A submission status processing method based on water level flow data, characterized in that: The method comprises: Acquire water level and flow reporting data, and perform reporting status analysis on the water level and flow reporting data to determine reporting status data of the water level and flow reporting data; Based on the reported status data, the transaction blocking node is obtained by tracing back through the water level flow station; Performing a reporting status recovery analysis on the transaction blocked node to determine an abnormal recovery strategy for the water level flow reporting data; According to the abnormal recovery strategy, an estimated recovery time threshold of the water level and flow reporting data is obtained through adaptive optimization in a hydrological time domain environment; Based on the estimated recovery time threshold, the abnormality recovery strategy is periodically optimized to obtain an adaptive abnormality recovery strategy.

2. A method for processing submission status based on water level flow data according to claim 1, characterized in that: The reporting status analysis of the water level and flow reporting data is performed to determine the reporting status data of the water level and flow reporting data, specifically including: Based on the water level flow reporting data, the first blocked session is determined through the pg_stat_activity view query; wherein the fields corresponding to the first blocked session include: uncommitted transactions, and executing queries; Performing a lock holding state determination on the first blocking session to obtain a second blocking session; wherein the lock holding state of the lock holding state determination includes: a lock holding state and a lock waiting state; The second blocked session is converted into an output table format to determine the above-mentioned reporting status data.

3. A method for processing submission status based on water level flow data according to claim 1, characterized in that: Based on the reported status data, the transaction blocking node is obtained through tracing back the water level flow station, including: Performing transaction positioning on the reported status data to determine uncommitted transaction data; Extracting the site field of the uncommitted transaction data, and performing flow station mapping and matching on the site field to obtain the water level flow station data of the uncommitted transaction data; Based on the water level flow station data, the transaction blocking node is determined by analyzing the uncommitted transaction duration sequence.

4. A method for processing submission status based on water level flow data according to claim 1, characterized in that: Performing a reporting status recovery analysis on the transaction blocked node to determine an abnormal recovery strategy for the water level flow reporting data, specifically including: Based on the transaction blocking node, the reporting queue type of the water level flow reporting data is determined by dividing the data transaction reporting queue; wherein the reporting queue type includes: single queue single task, single queue multi-task; In the case where the reporting queue type is a single queue and single task, the water level flow reporting transaction corresponding to the reporting queue type is canceled to obtain a first abnormality recovery strategy; In the case where the reporting queue type is single queue multi-task, constructing a priority tag for the water level flow reporting data, and adjusting the reporting queue based on the priority tag to obtain a second abnormality recovery strategy; The abnormality recovery strategy is determined according to the first abnormality recovery strategy or the second abnormality recovery strategy.

5. A method for processing submission status based on water level flow data according to claim 1, characterized in that: According to the abnormal recovery strategy, the estimated recovery time threshold of the water level and flow reporting data is obtained through adaptive optimization in the hydrological time domain environment, specifically including: Acquire a recovery time threshold of the abnormal recovery strategy, and divide the recovery time threshold into seasonal categories to obtain a seasonal recovery time threshold; Based on the seasonal recovery time threshold, determining an abnormality determination baseline threshold through recovery threshold adaptive processing; The abnormality determination baseline threshold is adjusted by a periodic threshold range to obtain the estimated recovery time threshold.

6. A method for processing submission status based on water level flow data according to claim 5, characterized in that: Based on the seasonal recovery time threshold, an abnormality determination baseline threshold is determined through recovery threshold adaptive processing, specifically including: Performing data preprocessing on the seasonal recovery time threshold to obtain a baseline recovery time threshold; Acquire transaction backlog value data, and based on the transaction backlog value, obtain transaction backlog critical parameters through backlog sensitivity analysis; wherein the transaction backlog value data includes: current transaction backlog value and peak backlog value; According to the transaction backlog critical parameter, seasonal adaptive processing is performed on the baseline recovery time threshold to obtain the abnormality determination baseline threshold.

7. A method for processing submission status based on water level flow data according to claim 1, characterized in that: Based on the estimated recovery time threshold, the abnormality recovery strategy is periodically optimized to obtain an adaptive abnormality recovery strategy, which specifically includes: Inserting the estimated recovery time threshold into the preset time threshold field of the abnormal recovery strategy to obtain an update strategy of the abnormal recovery strategy; Periodically monitoring the update strategy of the abnormal recovery strategy, and performing queue fitness analysis on the monitoring data obtained by the periodic monitoring to determine the fitness of the update strategy; Based on the adaptability of the update strategy, a mapping relationship between the update strategy and the water level flow reporting queue is constructed to obtain the adaptive abnormality recovery strategy.

8. A method for processing submission status based on water level flow data according to claim 1, characterized in that: Before obtaining the water level and flow reporting data, the method further includes: Constructing a water level data table, and performing interface configuration of a water level flow station on the water level flow table to obtain a water level flow data table; Based on the water level flow data table, the water level flow device fields are uniformly configured to obtain a unified field for water level flow devices.

9. A submission status processing device based on water level flow data, characterized in that: The device comprises: at least one processor; and, a memory communicatively coupled to the at least one processor; The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to: Acquire water level and flow reporting data, and perform reporting status analysis on the water level and flow reporting data to determine reporting status data of the water level and flow reporting data; Based on the reported status data, the transaction blocking node is obtained by tracing back through the water level flow station; Performing a reporting status recovery analysis on the transaction blocked node to determine an abnormal recovery strategy for the water level flow reporting data; According to the abnormal recovery strategy, an estimated recovery time threshold of the water level and flow reporting data is obtained through adaptive optimization in a hydrological time domain environment; Based on the estimated recovery time threshold, the abnormality recovery strategy is periodically optimized to obtain an adaptive abnormality recovery strategy.

10. A non-volatile computer storage medium based on water level flow data submission status processing, storing computer executable instructions, characterized in that: The computer executable instructions are configured to: Acquire water level and flow reporting data, and perform reporting status analysis on the water level and flow reporting data to determine reporting status data of the water level and flow reporting data; Based on the reported status data, the transaction blocking node is obtained by tracing back through the water level flow station; Performing a reporting status recovery analysis on the transaction blocked node to determine an abnormal recovery strategy for the water level flow reporting data; According to the abnormal recovery strategy, an estimated recovery time threshold of the water level and flow reporting data is obtained through adaptive optimization in a hydrological time domain environment; Based on the estimated recovery time threshold, the abnormality recovery strategy is periodically optimized to obtain an adaptive abnormality recovery strategy.

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