CA3142771A1 - Method of and device for monitoring business data, method of and device for generating rule data, and system - Google Patents
Method of and device for monitoring business data, method of and device for generating rule data, and system Download PDFInfo
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Abstract
The present invention makes public a method of and a device for monitoring business data, a method of and a device for generating monitoring rule data, and a corresponding system. The monitoring method comprises: obtaining, by a business node in a cluster, monitoring rule data based on a transmission mode of the monitoring rule data determined by an alarm platform, wherein the monitoring rule data includes: a business indicator preconfigured in the alarm platform and a statistical dimension corresponding to the business indicator; combining the business indicator with the statistical dimension corresponding thereto to form an indicator combination; receiving business flow data in real time, and obtaining business data; and making statistics on the business data according to the indicator combination, and monitoring and analyzing the business data.
Description
METHOD OF AND DEVICE FOR MONITORING BUSINESS DATA, METHOD OF
AND DEVICE FOR GENERATING RULE DATA, AND SYSTEM
BACKGROUND OF THE INVENTION
Technical Field [0001] The present invention relates to the field of data analysis technology, and more particularly to a method of and a device for monitoring business data, and a corresponding system.
Description of Related Art
AND DEVICE FOR GENERATING RULE DATA, AND SYSTEM
BACKGROUND OF THE INVENTION
Technical Field [0001] The present invention relates to the field of data analysis technology, and more particularly to a method of and a device for monitoring business data, and a corresponding system.
Description of Related Art
[0002] In prior-art technology dealing with multi-dimensionally combined business indicator monitoring, each business indicator has its own statistical dimension, and monitoring of the business indicator is counted on the basis of such dimensions. The OLAP
system is mostly employed in the state of the art for business indicators under multi-dimensional combinations, the essential method thereof is to ground business real-time data to the data acceleration layer, such as Druid, PostGreSQL or ClickHouse, the business indicator is subsequently combined with its corresponding dimension based on the data acceleration layer, timed polling is carried out on the business data according to the dimension of the combined business indicator, and comparison is then made with a preset threshold condition to perform early warning monitoring. This monitoring method is defective in the fact that it is impossible to realize real-time monitoring of the business data, and if there are too many indicators to be monitored, the load unto the data acceleration layer is also relatively great.
Date recue / Date received 2021-12-17 SUMMARY OF THE INVENTION
system is mostly employed in the state of the art for business indicators under multi-dimensional combinations, the essential method thereof is to ground business real-time data to the data acceleration layer, such as Druid, PostGreSQL or ClickHouse, the business indicator is subsequently combined with its corresponding dimension based on the data acceleration layer, timed polling is carried out on the business data according to the dimension of the combined business indicator, and comparison is then made with a preset threshold condition to perform early warning monitoring. This monitoring method is defective in the fact that it is impossible to realize real-time monitoring of the business data, and if there are too many indicators to be monitored, the load unto the data acceleration layer is also relatively great.
Date recue / Date received 2021-12-17 SUMMARY OF THE INVENTION
[0003] In order to solve the problems pending in the state of the art, embodiments of the present invention provide a method of and a device for monitoring business data, and a corresponding system. The technical solutions are as follows.
[0004] According to the first aspect, there is provided a method of monitoring business data, and the method comprises:
[0005] obtaining, by a business node in a cluster, monitoring rule data based on a transmission mode of the monitoring rule data determined by an alarm platform, wherein the monitoring rule data includes: a business indicator preconfigured in the alarm platform and a statistical dimension corresponding to the business indicator;
[0006] combining the business indicator with the statistical dimension corresponding thereto to form an indicator combination;
[0007] receiving business flow data in real time, and obtaining business data;
and
and
[0008] making statistics on the business data according to the indicator combination, and monitoring and analyzing the business data.
[0009] Further, the monitoring rule data further includes: preconfigured statistical frequency, alarm strategy, alarm mode, statistical sliding window;
[0010] the step of making statistics on the business data according to the indicator combination, and monitoring and analyzing the business data includes:
[0011] making statistics on the business data according to the indicator combination, monitoring and analyzing the business data according to the statistical frequency and the statistical sliding window, judging whether there is abnormality in the business data according to the alarm strategy, if yes, sending out alarm information according to the alarm mode.
[0012] Further, the step of making statistics on the business data according to the indicator combination, and monitoring and analyzing the business data includes:
Date recue / Date received 2021-12-17
Date recue / Date received 2021-12-17
[0013] obtaining current business data corresponding to the indicator combination from the business flow data;
[0014] obtaining historical business data corresponding to the indicator combination from a database; and
[0015] performing monitoring and analysis based on the current business data and the historical business data.
[0016] Further, the step of obtaining, by a business node in a cluster, monitoring rule data based on a transmission mode of the monitoring rule data determined by an alarm platform includes:
[0017] receiving rule flow data converted from the monitoring rule data, wherein the monitoring rule data is generated by the alarm platform, and the rule flow data is converted and generated by a flow data processing platform according to the monitoring rule data; and
[0018] analyzing the rule flow data, and obtaining the monitoring rule data.
[0019] Further, the step of obtaining, by a business node in a cluster, monitoring rule data based on a transmission mode of the monitoring rule data determined by an alarm platform includes:
[0020] asynchronously enquiring the monitoring rule data in a hot storage database, wherein the monitoring rule data is generated by the alarm platform and stored in the hot storage database.
[0021] Further, the step of obtaining, by a business node in a cluster, monitoring rule data based on a transmission mode of the monitoring rule data determined by an alarm platform includes:
[0022] periodically loading the monitoring rule data, wherein the monitoring rule data is generated and stored by the alarm platform.
[0023] According to the second aspect, there is provided a method of generating monitoring rule Date recue / Date received 2021-12-17 data, and the method comprises:
[0024] obtaining monitoring configuration data, wherein the monitoring configuration data includes: a business indicator, statistical frequency and statistical dimension corresponding to the business indicator;
[0025] generating monitoring rule data according to the monitoring configuration data; and
[0026] determining a transmission mode of the monitoring rule data according to the statistical frequency and statistical data demand volume to which the business indicator corresponds.
[0027] Further, the monitoring configuration data further includes statistical frequency, alarm strategy, alarm mode, statistical sliding window corresponding to the business indicator.
[0028] Further, when the statistical frequency and the statistical data demand volume of the business indicator satisfy a third threshold condition, the transmission mode of the monitoring rule data can be:
[0029] sending the monitoring rule data to a flow data processing platform, so that the flow data processing platform converts the monitoring rule data to rule flow data.
[0030] Further, when the statistical frequency and the statistical data demand volume of the business indicator satisfy a second threshold condition, the transmission mode of the monitoring rule data can be:
[0031] storing the monitoring rule data in a hot storage database, so as enable a business node in a cluster to perform asynchronous enquiry.
[0032] Further, when the statistical frequency and the statistical data demand volume of the business indicator satisfy a first threshold condition, the transmission mode of the monitoring rule data can be:
[0033] locally storing the monitoring rule data, or storing the monitoring rule data in Zookeeper.
[0034] According to the third aspect, there is provided a device for monitoring business data, Date recue / Date received 2021-12-17 and the device comprises:
[0035] a rule data obtaining module, for obtaining, by a business node in a cluster, monitoring rule data based on a transmission mode of the monitoring rule data determined by an alarm platform, wherein the monitoring rule data includes: a preconfigured business indicator and a statistical dimension corresponding to the business indicator;
[0036] a combining module, for combining the business indicator with the statistical dimension corresponding thereto to form an indicator combination;
[0037] a business data obtaining module, for receiving business flow data in real time, and obtaining business data; and
[0038] a monitoring module, for making statistics on the business data according to the indicator combination, and monitoring and analyzing the business data.
[0039] Further, the monitoring rule data further includes: preconfigured statistical frequency, alarm strategy, alarm mode, statistical sliding window.
[0040] The monitoring module is specifically employed for making statistics on the business data according to the indicator combination, monitoring and analyzing the business data according to the statistical frequency and the statistical sliding window, judging whether there is abnormality in the business data according to the alarm strategy, if yes, sending out alarm information according to the alarm mode.
[0041] Further, the monitoring module is specifically employed for:
[0042] obtaining current business data corresponding to the indicator combination from the business flow data;
[0043] obtaining historical business data corresponding to the indicator combination from a database; and
[0044] performing monitoring and analysis based on the current business data and the historical business data.
Date recue / Date received 2021-12-17
Date recue / Date received 2021-12-17
[0045] Further, the rule data obtaining module is specifically employed for:
[0046] receiving rule flow data converted from the monitoring rule data, wherein the monitoring rule data is generated by the alarm platform, and the rule flow data is converted and generated by a flow data processing platform according to the monitoring rule data; and
[0047] analyzing the rule flow data, and obtaining the monitoring rule data.
[0048] Further, the rule data obtaining module is specifically employed for:
[0049] asynchronously enquiring the monitoring rule data in a hot storage database, wherein the monitoring rule data is generated by the alarm platform and stored in the hot storage database.
[0050] Further, the rule data obtaining module is specifically employed for:
[0051] periodically loading the monitoring rule data, wherein the monitoring rule data is generated and stored by the alarm platform.
[0052] According to the fourth aspect, there is provided a device for generating monitoring rule data, and the device comprises:
[0053] a configuring module, for obtaining monitoring configuration data, wherein the monitoring configuration data includes: a business indicator, statistical frequency and statistical dimension corresponding to the business indicator;
[0054] a rule generating module, for generating monitoring rule data according to the monitoring configuration data; and
[0055] a transmission mode determining module, for determining a transmission mode of the monitoring rule data according to the statistical frequency and statistical data demand volume to which the business indicator corresponds.
[0056] Further, the monitoring configuration data further includes statistical frequency, alarm strategy, alarm mode, statistical sliding window corresponding to the business indicator.
Date recue / Date received 2021-12-17
Date recue / Date received 2021-12-17
[0057] Further, when the statistical frequency and the statistical data demand volume of the business indicator satisfy a third threshold condition, the transmission mode of the monitoring rule data can be:
[0058] sending the monitoring rule data to a flow data processing platform, so that the flow data processing platform converts the monitoring rule data to rule flow data.
[0059] Further, when the statistical frequency and the statistical data demand volume of the business indicator satisfy a second threshold condition, the transmission mode of the monitoring rule data can be:
[0060] storing the monitoring rule data in a hot storage database, so as enable a business node in a cluster to perform asynchronous enquiry.
[0061] Further, when the statistical frequency and the statistical data demand volume of the business indicator satisfy a first threshold condition, the transmission mode of the monitoring rule data can be:
[0062] locally storing the monitoring rule data, or storing the monitoring rule data in Zookeeper.
[0063] According to the fifth aspect, there is provided a computer system that comprises:
[0064] one or more processor(s); and
[0065] a memory, associated with the one or more processor(s), wherein the memory is employed to store a program instruction, and the program instruction performs the method according to anyone of the aforementioned first aspect when it is read and executed by the one or more processor(s).
[0066] Technical solutions provided by the embodiments of the present invention bring about the following advantageous effects.
[0067] The monitoring technical solution disclosed by the present invention makes it possible to monitor in real time alterations in the monitoring rule data, to obtain business data from Date recue / Date received 2021-12-17 business flow data, to monitor the business data in real time, to dispense with undue dependence upon the data acceleration layer as compared with prior-art technology, and to enhance real-time property of the data monitoring process.
[0068] The technical solution for generating monitoring rule data disclosed by the present invention proposes three types of transmission modes for the monitoring rule data according to statistical frequency and statistical dimension to which the business indicator corresponds, wherein the transmission mode of rule flow data and the transmission mode of hot storage do not require polling of the data acceleration layer, and reduce pressure of the data acceleration layer.
BRIEF DESCRIPTION OF THE DRAWINGS
BRIEF DESCRIPTION OF THE DRAWINGS
[0069] To more clearly describe the technical solutions in the embodiments of the present invention, drawings required to illustrate the embodiments will be briefly introduced below. Apparently, the drawings introduced below are merely directed to some embodiments of the present invention, while persons ordinarily skilled in the art may further acquire other drawings on the basis of these drawings without spending creative effort in the process.
[0070] Fig. 1 is a flowchart illustrating a method of monitoring business data provided by an embodiment of the present invention;
[0071] Fig. 2 is a flowchart illustrating a method of generating monitoring rule data provided by an embodiment of the present invention;
[0072] Fig. 3 is a view schematically illustrating the structure of a device for monitoring business data provided by an embodiment of the present invention;
Date recue / Date received 2021-12-17
Date recue / Date received 2021-12-17
[0073] Fig. 4 is a view schematically illustrating the structure of a device for generating monitoring rule data provided by an embodiment of the present invention; and
[0074] Fig. 5 is a view schematically illustrating the structure of a computer system provided by an embodiment of the present invention.
DETAILED DESCRIPTION OF THE INVENTION
DETAILED DESCRIPTION OF THE INVENTION
[0075] To make more lucid and clear the objectives, technical solutions and advantages of the present invention, the technical solutions in the embodiments of the present invention will be clearly and comprehensively described below with reference to the accompanying drawings in the embodiments of the present invention. Apparently, the embodiments as described are merely partial, rather than the entire, embodiments of the present invention.
Any other embodiments makeable by persons ordinarily skilled in the art on the basis of the embodiments in the present invention without creative effort shall all fall within the protection scope of the present invention.
Any other embodiments makeable by persons ordinarily skilled in the art on the basis of the embodiments in the present invention without creative effort shall all fall within the protection scope of the present invention.
[0076] As noted in the Description of Related Art, currently available multi-dimensional indicator data monitoring methods are based on the OLAP system. The OLAP
system is the On-Line Analytical Processing system, it mainly combines and segments dimensions in a dimensional cube through such operations as drilling down, drilling up, slicing, dicing and pivoting, and then periodically polls business data in the data acceleration layer according to dimensional combinations, to thereby achieve monitor and analysis of the business data. However, since this method obtains the business data based on the data acceleration layer, it is impossible to realize real-time monitor and analysis, moreover, once there are too many indicators to be monitored, or once the monitor frequency is unduly large, it will be required to frequently poll the data acceleration layer, whereby running pressure of the data acceleration layer is increased.
Date recue / Date received 2021-12-17
system is the On-Line Analytical Processing system, it mainly combines and segments dimensions in a dimensional cube through such operations as drilling down, drilling up, slicing, dicing and pivoting, and then periodically polls business data in the data acceleration layer according to dimensional combinations, to thereby achieve monitor and analysis of the business data. However, since this method obtains the business data based on the data acceleration layer, it is impossible to realize real-time monitor and analysis, moreover, once there are too many indicators to be monitored, or once the monitor frequency is unduly large, it will be required to frequently poll the data acceleration layer, whereby running pressure of the data acceleration layer is increased.
Date recue / Date received 2021-12-17
[0077] In order to solve the problems pending in the state of the art, the present invention provides a method of and a device for monitoring business data, a method of and a device for generating rule data, and a corresponding system, with technical solutions specified as follows.
[0078] As shown in Fig. 1, a method of monitoring business data comprises the following steps.
[0079] S 11 - obtaining, by a business node in a cluster, monitoring rule data based on a transmission mode of the monitoring rule data determined by an alarm platform, wherein the monitoring rule data includes: a business indicator preconfigured in the alarm platform and a statistical dimension corresponding to the business indicator.
[0080] The cluster mainly indicates a Flink cluster, and the Flink cluster is a computational framework and a distributed processing engine for performing state calculation on unbounded and bounded data flows. The Flink cluster contains a plurality of nodes that are classified as JobManager, TaskManager, JobClient according to distributed programs, of which JobClient is responsible for submitting tasks to JobManager, and JobManager then schedules the tasks to each TaskManager for execution. The business node in the present invention generally means one of the three nodes.
[0081] The monitoring rule data corresponds to the business indicator, the transmission mode of the monitoring rule data is predetermined by an alarm platform, the alarm platform is a kind of system capable of generating alarming rule data according to personnel configuration as proposed by the embodiments of the present invention, and the alarm platform can determine the transmission mode of the alarming rule data according to the business indicator and the statistical frequency in the alarm configuration data. The business node in the cluster should firstly base on the business indicator to determine the transmission mode of the corresponding monitoring rule, and thereafter obtain business indicator data according to the transmission mode. As should be noted, the obtainment in Date recue / Date received 2021-12-17 this context can be proactive loading and enquiring, and can also be passive receiving.
[0082] In one embodiment, the monitoring rule data further includes:
preconfigured statistical frequency, alarm strategy, alarm mode, statistical sliding window.
preconfigured statistical frequency, alarm strategy, alarm mode, statistical sliding window.
[0083] The aforementioned statistical frequency, alarm strategy, alarm mode, and statistical sliding window are all configured in the alarm platform, in which the alarm strategy indicates a threshold condition for judging whether there is abnormality in the business data, and the alarm mode indicates the type by which alarm information is sent out, such as flashing light alarm, tendency alarm, and fluctuation warning lamp. The statistical sliding window indicates a data sliding obtainment unit for making data statistics, for example, a window starting from [0,k-1], a summation thereof is recorded, the window is then moved rightwards to [1,k], again to [2,k+11, finally till the last tail end of the array.
[0084] In one embodiment, the aforementioned transmission mode can be to convert the monitoring rule data to rule flow data, and the rule flow data is broadcast to the business nodes in the cluster. Accordingly, step Sll can specifically be:
[0085] receiving rule flow data converted from the monitoring rule data, wherein the monitoring rule data is generated by the alarm platform, and the rule flow data is converted and generated by a flow data processing platform according to the monitoring rule data; and
[0086] analyzing the rule flow data, and obtaining the monitoring rule data.
[0087] Specifically, the monitoring rule data generated by the alarm platform is stored in Zookeeper, the monitoring function of Zookeeper is started to monitor alterations of the monitoring rule data, once the monitoring rule data of the corresponding business indicator is updated on the alarm platform, Zookeeper obtains the updated monitoring rule data and sends it to the flow data processing platform (which is mainly kafka) to be broadcast to the business nodes in the cluster by kafka.
Date recue / Date received 2021-12-17
Date recue / Date received 2021-12-17
[0088] The aforementioned method of obtaining the monitoring rule data is by way of passive obtainment, and this method is mainly directed to monitoring rule data in which the statistical data volume of business indicators is relatively small and statistics is highly frequently made on the business indicators.
[0089] The aforementioned transmission mode of the monitoring rule data can obtain in real time the updates of the monitoring rule data, and more facilitates to precisely monitor the business data in real time as compared with prior-art technology in which operation is made merely on the basis of the dimensional cube and there lacks real-time obtainment of alternation states of the rule data.
[0090] In one embodiment, the aforementioned transmission mode can be to store the monitoring rule data in such a hot storage database as Redis, Hbase, ES, etc.
Accordingly, step Sll is specifically:
Accordingly, step Sll is specifically:
[0091] asynchronously enquiring the monitoring rule data in a hot storage database, wherein the monitoring rule data is generated by the alarm platform and stored in the hot storage database.
[0092] The aforementioned method of obtaining the monitoring rule data is by way of active obtainment, and this method is mainly directed to monitoring rule data in which the statistical data volume of business indicators is large and statistics is relatively not frequently made on the business indicators.
[0093] In one embodiment, the aforementioned transmission mode can be to store the monitoring rule data in the alarm platform, or in Zookeeper. Accordingly, step Sll is specifically:
[0094] periodically loading the monitoring rule data, wherein the monitoring rule data is generated and stored by the alarm platform.
[0095] The aforementioned method of obtaining the monitoring rule data is by way of active Date recue / Date received 2021-12-17 obtainment, and this method is mainly directed to monitoring rule data in which the statistical data volume of business indicators is large and statistics is infrequently made on the business indicators.
[0096] S12 - combining the business indicator with the statistical dimension corresponding thereto to form an indicator combination.
[0097] One business indicator can correspond to plural statistical dimensions, and the correspondence relation therebetween is determined in the alarm platform. The monitoring rule data contains therein the correspondence relation between the business indicator and the statistical dimension(s), so combination can be directly performed after the monitoring rule data has been obtained.
[0098] In one embodiment, when the business indicator is combined with the statistical dimension, a merging process can be performed thereon with respect to the circumstance in which the same dimension has different dimension values, and the merging process can also be performed with respect to the same business indicators of the same business flow data.
[0099] S13 - receiving business flow data in real time, and obtaining business data.
[0100] The business flow data can be flow data issued by a business platform through kaflca, and mainly contains real-time business data. The business flow data can be classified according to business types as sales business flow data, flow rate business flow data, inventory business flow data, etc. Business data is obtained in the state of the art mainly from the data acceleration layer, so it is impossible to realize real-time monitoring, whereas the present invention makes use of the business flow data, whereby are achieved real-time data obtainment and real-time monitoring.
Date recue / Date received 2021-12-17
Date recue / Date received 2021-12-17
[0101] S14 - making statistics on the business data according to the indicator combination, and monitoring and analyzing the business data.
[0102] Making statistics on the business data according to the indicator combination mainly indicates to associate the indicator combination with the business data, and to divide the business data according to the indicator combination. Monitoring and analyzing the business data mainly indicates to compare the statistical result in accordance with the indicator combination with a threshold condition to judge whether there is abnormality in the business data.
[0103] In one embodiment, when the monitoring rule data further includes:
statistical frequency, alarm strategy, alarm mode, and statistical sliding window, step S14 specifically includes:
statistical frequency, alarm strategy, alarm mode, and statistical sliding window, step S14 specifically includes:
[0104] making statistics on the business data according to the indicator combination, monitoring and analyzing the business data according to the statistical frequency and the statistical sliding window, judging whether there is abnormality in the business data according to the alarm strategy, if yes, sending out alarm information according to the alarm mode.
[0105] There is the circumstance to compare and analyze contemporaneous data in the scenarios of actual application, for instance, to make an early warning within a range of business indicator data of the current period with business indicator data of the same period last year, it is therefore required to add historical business data in the data acceleration layer, which can be Druid, PostGreSQL, or ClickHouse.
[0106] Accordingly, in one embodiment, step S14 can specifically further include:
[0107] obtaining current business data corresponding to the indicator combination from the business flow data;
[0108] obtaining historical business data corresponding to the indicator combination from a database; and
[0109] performing monitoring and analysis based on the current business data and the historical Date recue / Date received 2021-12-17 business data.
[0110] The aforementioned database is preferably a data acceleration layer of the Flink cluster.
[0111] In one embodiment, the monitoring rule data can further include a rule validating condition, namely to set as to whether the updated monitoring rule is validated at the current statistical sliding window or the next statistical sliding window.
[0112] As shown in Fig. 2, based on the aforementioned method of monitoring business data, the present invention further makes public a method of generating monitoring rule data, and the method comprises the following steps.
[0113] S21 - obtaining monitoring configuration data, wherein the monitoring configuration data includes: a business indicator, statistical frequency and statistical dimension corresponding to the business indicator.
[0114] The aforementioned monitoring configuration data is generated by personnel based on inputting configuration data to the alarm platform.
[0115] In one embodiment, the monitoring configuration data further includes statistical frequency, alarm strategy, alarm mode, statistical sliding window corresponding to the business indicator.
[0116] The alarm strategy indicates a threshold condition for judging whether there is abnormality in the business data, and the alarm mode indicates the type by which alarm information is sent out. The statistical sliding window indicates a data sliding obtainment unit for making data statistics.
[0117] In one embodiment, the monitoring rule data can further include a rule validating Date recue / Date received 2021-12-17 condition, namely to set as to whether the updated monitoring rule is validated at the current statistical sliding window or the next statistical sliding window.
[0118] S22 - generating monitoring rule data according to the monitoring configuration data.
[0119] The generated monitoring rule data corresponds to the business indicator.
[0120] Based on the various configuration contents in the monitoring configuration data, the generated monitoring rule data also includes statistical frequency, alarm strategy, alarm mode, statistical sliding window, and rule validating condition corresponding to the business indicator.
[0121] S23 - determining a transmission mode of the monitoring rule data according to the statistical frequency and statistical data demand volume to which the business indicator corresponds.
[0122] Each transmission mode has a threshold condition of statistical data demand volume to which the corresponding business indicator corresponds and a threshold condition of the statistical frequency, the statistical data demand volume to which the business indicator corresponds and the statistical frequency are respectively compared with the above threshold conditions, and the transmission mode is determined according to the comparison result.
[0123] Specifically, the threshold conditions can include a first threshold condition, a second threshold condition, and a third threshold condition, the three threshold conditions are sequenced according to the statistical data demand volume to which the business indicator corresponds as: the first threshold condition > the second threshold condition > the third threshold condition, and sequenced according to the statistical frequency as:
the first threshold condition < the second threshold condition < the third threshold condition.
Date recue / Date received 2021-12-17
the first threshold condition < the second threshold condition < the third threshold condition.
Date recue / Date received 2021-12-17
[0124] In one embodiment, when the statistical frequency and the statistical data demand volume of the business indicator satisfy a third threshold condition, the transmission mode of the monitoring rule data can be:
[0125] sending the monitoring rule data to a flow data processing platform, so that the flow data processing platform converts the monitoring rule data to rule flow data.
[0126] Specifically, the monitoring rule data is stored in Zookeeper, Zookeeper monitors alterations of the monitoring rule data, once the monitoring rule data of the corresponding business indicator is updated on the alarm platform, Zookeeper obtains the updated monitoring rule data and sends it to the flow data processing platform to be broadcast by the flow data processing platform to the business nodes in the cluster.
[0127] In one embodiment, when the statistical frequency and the statistical data demand volume of the business indicator satisfy a second threshold condition, the transmission mode of the monitoring rule data can be:
[0128] storing the monitoring rule data in such a hot storage database as Redis, Hbase, ES, etc., so as enable a business node in a cluster to perform asynchronous enquiry.
[0129] In one embodiment, when the statistical frequency and the statistical data demand volume of the business indicator satisfy a first threshold condition, the transmission mode of the monitoring rule data can be:
[0130] locally storing the monitoring rule data, or storing the monitoring rule data in Zookeeper.
[0131] Moreover, each of different transmission modes has a business indicator table, and business indicators conforming to the rule are stored in the corresponding table, so that the business node in the cluster can obtain the transmission mode of the corresponding monitoring rule data according to the business indicator table, so as to obtain the monitoring rule data.
Date recue / Date received 2021-12-17
Date recue / Date received 2021-12-17
[0132] As shown in Fig. 3, based on the aforementioned method of monitoring business data, an embodiment of the present invention further provides a device for monitoring business data, and the device comprises the following modules.
[0133] A rule data obtaining module 301 is employed for obtaining, by a business node in a cluster, monitoring rule data based on a transmission mode of the monitoring rule data determined by an alarm platform, wherein the monitoring rule data includes: a preconfigured business indicator and a statistical dimension corresponding to the business indicator.
[0134] In one embodiment, the monitoring rule data further includes:
preconfigured statistical frequency, alarm strategy, alarm mode, statistical sliding window.
preconfigured statistical frequency, alarm strategy, alarm mode, statistical sliding window.
[0135] The aforementioned statistical frequency, alarm strategy, alarm mode, and statistical sliding window are all configured in the alarm platform, in which the alarm strategy indicates a threshold condition for judging whether there is abnormality in the business data, and the alarm mode indicates the type by which alarm information is sent out, such as flashing light alarm, tendency alarm, and fluctuation warning lamp. The statistical sliding window indicates a data sliding obtainment unit for making data statistics, for example, a window starting from [0,k-1], a summation thereof is recorded, the window is then moved rightwards to [1,k], again to [2,k+1], finally till the last tail end of the array.
[0136] In one embodiment, the rule data obtaining module 301 is specifically employed for:
[0137] receiving rule flow data converted from the monitoring rule data, wherein the monitoring rule data is generated by the alarm platform, and the rule flow data is converted and generated by a flow data processing platform according to the monitoring rule data; and
[0138] analyzing the rule flow data, and obtaining the monitoring rule data.
Date recue / Date received 2021-12-17
Date recue / Date received 2021-12-17
[0139] Specifically, the monitoring rule data generated by the alarm platform is stored in Zookeeper, the monitoring function of Zookeeper is started to monitor alterations of the monitoring rule data, once the monitoring rule data of the corresponding business indicator is updated on the alarm platform, Zookeeper obtains the updated monitoring rule data and sends it to the flow data processing platform (which is mainly kaflca) to be broadcast to the business nodes in the cluster by kaflca.
[0140] In one embodiment, the rule data obtaining module 301 is specifically employed for:
[0141] asynchronously enquiring the monitoring rule data in a hot storage database, wherein the monitoring rule data is generated by the alarm platform and stored in the hot storage database.
[0142] In one embodiment, the rule data obtaining module 301 is specifically employed for:
[0143] periodically loading the monitoring rule data, wherein the monitoring rule data is generated and stored by the alarm platform.
[0144] A combining module 302 is employed for combining the business indicator with the statistical dimension corresponding thereto to form an indicator combination.
[0145] One business indicator can correspond to plural statistical dimensions, and the correspondence relation therebetween is determined in the alarm platform. The monitoring rule data contains therein the correspondence relation between the business indicator and the statistical dimension(s), so combination can be directly performed after the monitoring rule data has been obtained.
[0146] In one embodiment, the combining module 302 performs a merging process with respect to the circumstance in which the same dimension has different dimension values, and performs the merging process with respect to the same business indicators of the same business flow data.
Date recue / Date received 2021-12-17
Date recue / Date received 2021-12-17
[0147] A business data obtaining module 303 is employed for receiving business flow data in real time, and obtaining business data.
[0148] The business flow data can be flow data issued by a business platform through kaflca, and mainly contains real-time business data.
[0149] A monitoring module 304 is employed for making statistics on the business data according to the indicator combination, and monitoring and analyzing the business data.
[0150] Making statistics on the business data according to the indicator combination mainly indicates to associate the indicator combination with the business data, and to divide the business data according to the indicator combination. Monitoring and analyzing the business data mainly indicates to compare the statistical result in accordance with the indicator combination with a threshold condition to judge whether there is abnormality in the business data.
[0151] In one embodiment, when the monitoring rule data further includes:
statistical frequency, alarm strategy, alarm mode, statistical sliding window, the monitoring module 304 is specifically employed for:
statistical frequency, alarm strategy, alarm mode, statistical sliding window, the monitoring module 304 is specifically employed for:
[0152] making statistics on the business data according to the indicator combination, monitoring and analyzing the business data according to the statistical frequency and the statistical sliding window, judging whether there is abnormality in the business data according to the alarm strategy, if yes, sending out alarm information according to the alarm mode.
[0153] In one embodiment, in order to adapt to the circumstance in which it is required to analyze historical business data, the monitoring module 304 is further specifically employed for:
[0154] obtaining current business data corresponding to the indicator combination from the business flow data;
Date recue / Date received 2021-12-17
Date recue / Date received 2021-12-17
[0155] obtaining historical business data corresponding to the indicator combination from a database; and
[0156] performing monitoring and analysis based on the current business data and the historical business data.
[0157] The aforementioned database is preferably a data acceleration layer of the Flink cluster.
[0158] In one embodiment, the monitoring rule data can further include a rule validating condition, namely to set as to whether the updated monitoring rule is validated at the current statistical sliding window or the next statistical sliding window.
[0159] As shown in Fig. 4, based on the aforementioned method of generating monitoring rule data, an embodiment of the present invention further provides a device for generating monitoring rule data, and the device comprises the following modules.
[0160] A configuring module 401 is employed for obtaining monitoring configuration data, wherein the monitoring configuration data includes: a business indicator, statistical frequency and statistical dimension corresponding to the business indicator.
[0161] The aforementioned monitoring configuration data is formed by personnel based on inputting configuration data to the alarm platform.
[0162] In one embodiment, the monitoring configuration data further includes alarm strategy, alarm mode, statistical sliding window corresponding to the business indicator.
[0163] The alarm strategy indicates a threshold condition for judging whether there is abnormality in the business data, and the alarm mode indicates the type by which alarm information is sent out. The statistical sliding window indicates a data sliding obtainment unit for making data statistics.
Date recue / Date received 2021-12-17
Date recue / Date received 2021-12-17
[0164] In one embodiment, the monitoring rule data can further include a rule validating condition, namely to set as to whether the updated monitoring rule is validated at the current statistical sliding window or the next statistical sliding window.
[0165] A rule generating module 402 is employed for generating monitoring rule data according to the monitoring configuration data.
[0166] The generated monitoring rule data corresponds to the business indicator.
[0167] Based on the various configuration contents in the monitoring configuration data, the generated monitoring rule data also includes statistical frequency, alarm strategy, alarm mode, statistical sliding window, and rule validating condition corresponding to the business indicator.
[0168] A transmission mode determining module 403 is employed for determining a transmission mode of the monitoring rule data according to the statistical frequency and statistical data demand volume to which the business indicator corresponds.
[0169] Each transmission mode has a threshold condition of statistical data demand volume to which the corresponding business indicator corresponds and a threshold condition of the statistical frequency, and the transmission mode determining module 403 is specifically employed for comparing the statistical data demand volume to which the business indicator corresponds and the statistical frequency respectively with the above threshold conditions, and determining the transmission mode according to the comparison result.
[0170] Specifically, the transmission mode includes three types respectively corresponding to one of three threshold conditions.
Date recue / Date received 2021-12-17
Date recue / Date received 2021-12-17
[0171] In one embodiment, when the statistical frequency and the statistical data demand volume of the business indicator satisfy a third threshold condition, the transmission mode of the monitoring rule data can be:
[0172] sending the monitoring rule data to a flow data processing platform, so that the flow data processing platform converts the monitoring rule data to rule flow data.
[0173] In one embodiment, when the statistical frequency and the statistical data demand volume of the business indicator satisfy a second threshold condition, the transmission mode of the monitoring rule data can be:
[0174] storing the monitoring rule data in such a hot storage database as Redis, Hbase, ES, etc., so as enable a business node in a cluster to perform asynchronous enquiry.
[0175] In one embodiment, when the statistical frequency and the statistical data demand volume of the business indicator satisfy a first threshold condition, the transmission mode of the monitoring rule data can be:
[0176] locally storing the monitoring rule data, or storing the monitoring rule data in Zookeeper.
[0177] Based on the aforementioned method of monitoring business data, the present invention further provides a computer system, which comprises:
[0178] one or more processor(s); and
[0179] a memory, associated with the one or more processor(s), wherein the memory is employed to store a program instruction, and the program instruction performs the aforementioned method of monitoring business data when it is read and executed by the one or more processor(s).
[0180] Fig. 5 exemplarily illustrates the framework of the computer system that can specifically include a processor 510, a video display adapter 511, a magnetic disk driver 512, an input/output interface 513, a network interface 514, and a memory 520. The processor 510, the video display adapter 511, the magnetic disk driver 512, the input/output Date recue / Date received 2021-12-17 interface 513, the network interface 514, and the memory 520 can be communicably connected with one another via a communication bus 530.
[0181] The processor 510 can be embodied as a general CPU (Central Processing Unit), a microprocessor, an ASIC (Application Specific Integrated Circuit), or one or more integrated circuit(s) for executing relevant program(s) to realize the technical solutions provided by the present application.
[0182] The memory 520 can be embodied in such a form as an ROM (Read Only Memory), an RAM (Random Access Memory), a static storage device, or a dynamic storage device.
The memory 520 can store an operating system 521 for controlling the running of an electronic equipment 500, and a basic input/output system 522 (BIOS) for controlling lower-level operations of the electronic equipment 500. In addition, the memory 520 can also store a web browser 523, a data storage management system 524, and an equipment identification information processing system 525, etc. The equipment identification information processing system 525 can be an application program that specifically realizes the aforementioned various step operations in the embodiments of the present application. To sum it up, when the technical solutions provided by the present application are to be realized via software or firmware, the relevant program codes are stored in the memory 520, and invoked and executed by the processor 510.
The memory 520 can store an operating system 521 for controlling the running of an electronic equipment 500, and a basic input/output system 522 (BIOS) for controlling lower-level operations of the electronic equipment 500. In addition, the memory 520 can also store a web browser 523, a data storage management system 524, and an equipment identification information processing system 525, etc. The equipment identification information processing system 525 can be an application program that specifically realizes the aforementioned various step operations in the embodiments of the present application. To sum it up, when the technical solutions provided by the present application are to be realized via software or firmware, the relevant program codes are stored in the memory 520, and invoked and executed by the processor 510.
[0183] The input/output interface 513 is employed to connect with an input/output module to realize input and output of information. The input/output module can be equipped in the device as a component part (not shown in the drawings), and can also be externally connected with the device to provide corresponding functions. The input means can include a keyboard, a mouse, a touch screen, a microphone, and various sensors etc., and the output means can include a display screen, a loudspeaker, a vibrator, an indicator light etc.
Date recue / Date received 2021-12-17
Date recue / Date received 2021-12-17
[0184] The network interface 514 is employed to connect to a communication module (not shown in the drawings) to realize intercommunication between the current device and other devices. The communication module can realize communication in a wired mode (via USB, network cable, for example) or in a wireless mode (via mobile network, WIFI, Bluetooth, etc.).
[0185] The bus 530 includes a passageway transmitting information between various component parts of the device (such as the processor 510, the video display adapter 511, the magnetic disk driver 512, the input/output interface 513, the network interface 514, and the memory 520).
[0186] Additionally, the electronic equipment 500 may further obtain information of specific collection conditions from a virtual resource object collection condition information database for judgment on conditions, and so on.
[0187] As should be noted, although merely the processor 510, the video display adapter 511, the magnetic disk driver 512, the input/output interface 513, the network interface 514, the memory 520, and the bus 530 are illustrated for the aforementioned device, the device may further include other component parts prerequisite for realizing normal running during specific implementation. In addition, as can be understood by persons skilled in the art, the aforementioned device may as well only include component parts necessary for realizing the solutions of the present application, without including the entire component parts as illustrated.
[0188] As can be known through the description to the aforementioned embodiments, it is clearly learnt by person skilled in the art that the present application can be realized through software plus a general hardware platform. Based on such understanding, the technical solutions of the present application, or the contributions made thereby over the state of the art, can be essentially embodied in the form of a software product, and such a Date recue / Date received 2021-12-17 computer software product can be stored in a storage medium, such as an ROM/RAM, a magnetic disk, an optical disk etc., and includes plural instructions enabling a computer equipment (such as a personal computer, a server, or a network device etc.) to execute the methods described in various embodiments or some sections of the embodiments of the present application.
[0189] The various embodiments are progressively described in the Description, identical or similar sections among the various embodiments can be inferred from one another, and each embodiment stresses what is different from other embodiments.
Particularly, with respect to the system or system embodiment, since it is essentially similar to the method embodiment, its description is relatively simple, and the relevant sections thereof can be inferred from the corresponding sections of the method embodiment. The system or system embodiment as described above is merely exemplary in nature, units therein described as separate parts can be or may not be physically separate, parts displayed as units can be or may not be physical units, that is to say, they can be located in a single site, or distributed over a plurality of network units. It is possible to base on practical requirements to select partial modules or the entire modules to realize the objectives of the embodied solutions. It is understandable and implementable by persons ordinarily skilled in the art without spending creative effort in the process.
Particularly, with respect to the system or system embodiment, since it is essentially similar to the method embodiment, its description is relatively simple, and the relevant sections thereof can be inferred from the corresponding sections of the method embodiment. The system or system embodiment as described above is merely exemplary in nature, units therein described as separate parts can be or may not be physically separate, parts displayed as units can be or may not be physical units, that is to say, they can be located in a single site, or distributed over a plurality of network units. It is possible to base on practical requirements to select partial modules or the entire modules to realize the objectives of the embodied solutions. It is understandable and implementable by persons ordinarily skilled in the art without spending creative effort in the process.
[0190] Technical solutions provided by the embodiments of the present invention bring about the following advantageous effects.
[0191] The monitoring technical solution disclosed by the present invention makes it possible to monitor in real time alterations in the monitoring rule data, to obtain business data from business flow data, to monitor the business data in real time, to dispense with undue dependence upon the data acceleration layer as compared with prior-art technology, and to enhance real-time property of the data monitoring process.
Date recue / Date received 2021-12-17
Date recue / Date received 2021-12-17
[0192] The technical solution for generating monitoring rule data disclosed by the present invention proposes three types of transmission modes for the monitoring rule data according to statistical frequency and statistical dimension to which the business indicator corresponds, wherein the transmission mode of rule flow data and the transmission mode of hot storage do not require polling of the data acceleration layer, and reduce pressure of the data acceleration layer.
[0193] All the above optional technical solutions can be randomly combined to form optional embodiments of the present invention, to which no repetition is made thereto in this context.
[0194] What is described above is merely directed to preferred embodiments of the present invention, and is not meant to restrict the present invention. Any amendment, equivalent replacement and improvement makeable within the spirit and principle of the present invention shall all fall within the protection scope of the present invention.
Date recue / Date received 2021-12-17
Date recue / Date received 2021-12-17
Claims (10)
1. A method of monitoring business data, characterized in comprising:
obtaining, by a business node in a cluster, monitoring rule data based on a transmission mode of the monitoring rule data determined by an alarm platform, wherein the monitoring rule data includes: a business indicator preconfigured in the alarm platform and a statistical dimension corresponding to the business indicator;
combining the business indicator with the statistical dimension corresponding thereto to form an indicator combination;
receiving business flow data in real time, and obtaining business data; and making statistics on the business data according to the indicator combination, and monitoring and analyzing the business data.
obtaining, by a business node in a cluster, monitoring rule data based on a transmission mode of the monitoring rule data determined by an alarm platform, wherein the monitoring rule data includes: a business indicator preconfigured in the alarm platform and a statistical dimension corresponding to the business indicator;
combining the business indicator with the statistical dimension corresponding thereto to form an indicator combination;
receiving business flow data in real time, and obtaining business data; and making statistics on the business data according to the indicator combination, and monitoring and analyzing the business data.
2. The method according to Claim 1, characterized in that the monitoring rule data further includes: preconfigured statistical frequency, alarm strategy, alarm mode, statistical sliding window; and that the step of making statistics on the business data according to the indicator combination, and monitoring and analyzing the business data includes:
making statistics on the business data according to the indicator combination, monitoring and analyzing the business data according to the statistical frequency and the statistical sliding window, judging whether there is abnormality in the business data according to the alarm strategy, if yes, sending out alarm information according to the alarm mode.
making statistics on the business data according to the indicator combination, monitoring and analyzing the business data according to the statistical frequency and the statistical sliding window, judging whether there is abnormality in the business data according to the alarm strategy, if yes, sending out alarm information according to the alarm mode.
3. The method according to Claim 1, characterized in that the step of making statistics on the business data according to the indicator combination, and monitoring and analyzing the business data includes:
obtaining current business data corresponding to the indicator combination from the business Date recue / Date received 2021-12-17 flow data;
obtaining historical business data corresponding to the indicator combination from a database;
and performing monitoring and analysis based on the current business data and the historical business data.
obtaining current business data corresponding to the indicator combination from the business Date recue / Date received 2021-12-17 flow data;
obtaining historical business data corresponding to the indicator combination from a database;
and performing monitoring and analysis based on the current business data and the historical business data.
4. The method according to anyone of Claims 1 to 3, characterized in that the step of obtaining, by a business node in a cluster, monitoring rule data based on a transmission mode of the monitoring rule data determined by an alarm platform includes:
receiving rule flow data converted from the monitoring rule data, wherein the monitoring rule data is generated by the alarm platform, and the rule flow data is converted and generated by a flow data processing platform according to the monitoring rule data; and analyzing the rule flow data, and obtaining the monitoring rule data.
receiving rule flow data converted from the monitoring rule data, wherein the monitoring rule data is generated by the alarm platform, and the rule flow data is converted and generated by a flow data processing platform according to the monitoring rule data; and analyzing the rule flow data, and obtaining the monitoring rule data.
5. The method according to anyone of Claims 1 to 3, characterized in that the step of obtaining, by a business node in a cluster, monitoring rule data based on a transmission mode of the monitoring rule data determined by an alarm platform includes:
asynchronously enquiring the monitoring rule data in a hot storage database, wherein the monitoring rule data is generated by the alarm platform and stored in the hot storage database.
asynchronously enquiring the monitoring rule data in a hot storage database, wherein the monitoring rule data is generated by the alarm platform and stored in the hot storage database.
6. The method according to anyone of Claims 1 to 3, characterized in that the step of obtaining, by a business node in a cluster, monitoring rule data based on a transmission mode of the monitoring rule data determined by an alarm platform includes:
periodically loading the monitoring rule data, wherein the monitoring rule data is generated and stored by the alarm platform.
periodically loading the monitoring rule data, wherein the monitoring rule data is generated and stored by the alarm platform.
7. A method of generating monitoring rule data, characterized in comprising:
obtaining monitoring configuration data, wherein the monitoring configuration data includes:
a business indicator, and statistical frequency and statistical dimension corresponding to the Date recue / Date received 2021-12-17 business indicator;
generating monitoring rule data according to the monitoring configuration data; and determining a transmission mode of the monitoring rule data according to the statistical frequency and statistical data demand volume to which the business indicator corresponds.
obtaining monitoring configuration data, wherein the monitoring configuration data includes:
a business indicator, and statistical frequency and statistical dimension corresponding to the Date recue / Date received 2021-12-17 business indicator;
generating monitoring rule data according to the monitoring configuration data; and determining a transmission mode of the monitoring rule data according to the statistical frequency and statistical data demand volume to which the business indicator corresponds.
8. A device for monitoring business data, characterized in comprising:
a rule data obtaining module, for obtaining, by a business node in a cluster, monitoring rule data based on a transmission mode of the monitoring rule data determined by an alarm platform, wherein the monitoring rule data includes: a preconfigured business indicator and a statistical dimension corresponding to the business indicator;
a combining module, for combining the business indicator with the statistical dimension corresponding thereto to form an indicator combination;
a business data obtaining module, for receiving business flow data in real time, and obtaining business data; and a monitoring module, for making statistics on the business data according to the indicator combination, and monitoring and analyzing the business data.
a rule data obtaining module, for obtaining, by a business node in a cluster, monitoring rule data based on a transmission mode of the monitoring rule data determined by an alarm platform, wherein the monitoring rule data includes: a preconfigured business indicator and a statistical dimension corresponding to the business indicator;
a combining module, for combining the business indicator with the statistical dimension corresponding thereto to form an indicator combination;
a business data obtaining module, for receiving business flow data in real time, and obtaining business data; and a monitoring module, for making statistics on the business data according to the indicator combination, and monitoring and analyzing the business data.
9. A device for generating monitoring rule data, characterized in comprising:
a configuring module, for obtaining monitoring configuration data, wherein the monitoring configuration data includes: a business indicator, and statistical frequency and statistical dimension corresponding to the business indicator;
a rule generating module, for generating monitoring rule data according to the monitoring configuration data; and a transmission mode determining module, for determining a transmission mode of the monitoring rule data according to the statistical frequency and statistical data demand volume to which the business indicator corresponds.
a configuring module, for obtaining monitoring configuration data, wherein the monitoring configuration data includes: a business indicator, and statistical frequency and statistical dimension corresponding to the business indicator;
a rule generating module, for generating monitoring rule data according to the monitoring configuration data; and a transmission mode determining module, for determining a transmission mode of the monitoring rule data according to the statistical frequency and statistical data demand volume to which the business indicator corresponds.
10. A computer system, characterized in comprising:
one or more processor(s); and Date recue / Date received 2021-12-17 a memory, associated with the one or more processor(s), wherein the memory is employed to store a program instruction, and the program instruction performs the method according to anyone of Claims 1 to 6 when it is read and executed by the one or more processor(s).
Date recue / Date received 2021-12-17
one or more processor(s); and Date recue / Date received 2021-12-17 a memory, associated with the one or more processor(s), wherein the memory is employed to store a program instruction, and the program instruction performs the method according to anyone of Claims 1 to 6 when it is read and executed by the one or more processor(s).
Date recue / Date received 2021-12-17
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| CN115098485A (en) * | 2022-07-13 | 2022-09-23 | 南威软件股份有限公司 | Intelligent data reconciliation method based on grouping statistics |
| CN115331440A (en) * | 2022-08-09 | 2022-11-11 | 山东旗帜信息有限公司 | High-adaptation early warning method and system based on monitoring threshold information |
| CN115913886A (en) * | 2022-11-15 | 2023-04-04 | 浪潮云信息技术股份公司 | Alarm method and system based on sliding window in cloud native environment |
| CN116112385A (en) * | 2023-02-15 | 2023-05-12 | 贵州北盘江电力股份有限公司光照分公司 | Network communication data analysis and display method and system for power monitoring system |
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| CN113920698B (en) * | 2021-11-25 | 2023-08-04 | 杭州安恒信息技术股份有限公司 | Early warning method, device, equipment and medium for interface abnormal call |
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| CN104461830B (en) * | 2014-12-19 | 2017-09-22 | 北京奇虎科技有限公司 | The method and apparatus of monitoring process |
| CN105471671A (en) * | 2015-11-10 | 2016-04-06 | 国云科技股份有限公司 | Method for customizing monitoring rules of cloud platform resources |
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| CN109688188B (en) * | 2018-09-07 | 2022-08-19 | 平安科技（深圳）有限公司 | Monitoring alarm method, device, equipment and computer readable storage medium |
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2020
- 2020-12-17 CN CN202011492921.6A patent/CN112463543B/en active Active
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2021
- 2021-12-17 CA CA3142771A patent/CA3142771A1/en active Pending
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| CN115098485A (en) * | 2022-07-13 | 2022-09-23 | 南威软件股份有限公司 | Intelligent data reconciliation method based on grouping statistics |
| CN115331440A (en) * | 2022-08-09 | 2022-11-11 | 山东旗帜信息有限公司 | High-adaptation early warning method and system based on monitoring threshold information |
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| CN116112385B (en) * | 2023-02-15 | 2023-09-19 | 贵州北盘江电力股份有限公司光照分公司 | Network communication data analysis and display method and system for power monitoring system |
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| Publication number | Publication date |
|---|---|
| CN112463543A (en) | 2021-03-09 |
| CN112463543B (en) | 2024-08-23 |
| CA3230388A1 (en) | 2022-06-17 |
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