CN112422638A - Data real-time stream processing method, system, computer device and storage medium - Google Patents
Data real-time stream processing method, system, computer device and storage medium Download PDFInfo
- Publication number
- CN112422638A CN112422638A CN202011171218.5A CN202011171218A CN112422638A CN 112422638 A CN112422638 A CN 112422638A CN 202011171218 A CN202011171218 A CN 202011171218A CN 112422638 A CN112422638 A CN 112422638A
- Authority
- CN
- China
- Prior art keywords
- rule
- data
- real
- early warning
- warning message
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000003672 processing method Methods 0.000 title abstract description 16
- 238000012545 processing Methods 0.000 claims abstract description 32
- 238000002955 isolation Methods 0.000 claims abstract description 5
- 238000000034 method Methods 0.000 claims description 23
- 238000007726 management method Methods 0.000 claims description 12
- 230000006870 function Effects 0.000 claims description 6
- 238000004458 analytical method Methods 0.000 claims description 5
- 238000004364 calculation method Methods 0.000 claims description 5
- 238000003032 molecular docking Methods 0.000 claims description 4
- 238000012544 monitoring process Methods 0.000 claims description 3
- 238000004806 packaging method and process Methods 0.000 claims description 2
- 238000011161 development Methods 0.000 abstract description 6
- 238000004590 computer program Methods 0.000 description 5
- 238000010586 diagram Methods 0.000 description 5
- 230000008569 process Effects 0.000 description 4
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 4
- 230000000694 effects Effects 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 3
- 238000007654 immersion Methods 0.000 description 2
- 210000001503 joint Anatomy 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000012800 visualization Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 239000000470 constituent Substances 0.000 description 1
- 238000013500 data storage Methods 0.000 description 1
- 238000003384 imaging method Methods 0.000 description 1
- 230000014759 maintenance of location Effects 0.000 description 1
- 239000002245 particle Substances 0.000 description 1
- 230000002085 persistent effect Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
Images
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/50—Network services
- H04L67/55—Push-based network services
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L65/00—Network arrangements, protocols or services for supporting real-time applications in data packet communication
- H04L65/40—Support for services or applications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L69/00—Network arrangements, protocols or services independent of the application payload and not provided for in the other groups of this subclass
- H04L69/22—Parsing or analysis of headers
Landscapes
- Engineering & Computer Science (AREA)
- Computer Networks & Wireless Communication (AREA)
- Signal Processing (AREA)
- Multimedia (AREA)
- Computer Security & Cryptography (AREA)
- Debugging And Monitoring (AREA)
Abstract
The invention discloses a data real-time stream processing method, a system, a computer device and a storage medium, wherein the data real-time stream processing method comprises the steps of developing a rule model, injecting associated threshold parameters into the rule model, combining a plurality of rule models into an operation rule, matching service data with the operation rule to generate an early warning message, pushing the early warning message and the like. The real-time stream processing method and the real-time stream processing system can realize the abstraction of the rules and combine the real-time stream processing and the rule engine, thereby effectively decoupling the rules and the parameters, enabling the rules to be multiplexed and recombined according to the business requirements and reducing the repeated work. In addition, due to the isolation of computing resources, different operations can be effectively segmented, mutual influence is avoided, a service worker can be effectively helped to separate service rules from application program codes, and the development complexity and development cost of the early warning model are reduced. The invention is widely applied to the technical field of data stream processing.
Description
Technical Field
The present invention relates to the field of data stream processing technologies, and in particular, to a method, a system, a computer device, and a storage medium for processing a real-time data stream.
Background
At present, the service digitization of enterprises and public institutions is basically complete, so the generated data volume and data types are exponentially increased, and meanwhile, the rules in the actual service are frequently changed, so that the real-time stream processing technology based on a rule engine is required to process the data generated in the daily service. The currently used data real-time stream processing technologies such as flink, storm, rules engine drools, complex event processing CEP, etc. have the following disadvantages: the business rules and the application program codes thereof are tightly combined and difficult to separate, so the development complexity and the development cost of the early warning model are higher, and the application of the real-time data stream processing in enterprises and public institutions is not facilitated.
Disclosure of Invention
In view of at least one of the above technical problems, it is an object of the present invention to provide a data real-time stream processing method, system, computer device and storage medium.
In one aspect, an embodiment of the present invention includes a method for processing a real-time data stream, including:
developing a rule model;
determining the association relation between the rule model and the corresponding threshold parameter;
injecting the associated threshold parameter into the rule model;
combining the plurality of rule models into an operation rule;
acquiring service data, and matching the service data with the analyzed operation rule so as to generate an early warning message;
and pushing the early warning message.
Further, the determining the association relationship between the rule model and the corresponding threshold parameter includes:
selecting a data source of service data;
associating an event date field in the data source; the event date field is used as a rule alarm time;
compiling a judgment rule;
exposing a threshold parameter corresponding to the judgment rule;
the threshold parameter is declared.
Further, the combining the plurality of rule models into the operation rule includes:
pulling a plurality of rule models;
associating each rule model in an OR mode; the result of the association is the job rule.
Further, the acquiring the service data and matching the service data with the analyzed operation rule to generate an early warning message includes:
generating a computing node set according to the computing environment configuration of the operation rule;
executing data source registration and user self-defined function registration;
executing computing resource docking, and distributing the operation rule to the computing nodes in the computing node set for computing;
acquiring service data from a data source;
matching the service data with the operation rule in real time;
and if the service data and the operation rule accord with a matching rule, packaging the service data into an early warning message.
On the other hand, the embodiment of the present invention further includes a data real-time stream processing system, including:
a data source for developing a rule model;
the rule analysis layer is used for determining the incidence relation between the rule model and the corresponding threshold parameter, injecting the associated threshold parameter into the rule model, and combining a plurality of rule models into an operation rule;
the calculation layer is used for acquiring service data and matching the service data with the analyzed operation rule so as to generate an early warning message;
and the middleware is used for pushing the early warning message.
Further, the data real-time stream processing system further comprises:
and the application layer is used for receiving the early warning message pushed by the middleware, and visualizing the early warning message or forwarding the early warning message to the outside.
Further, the data real-time stream processing system further comprises:
and the operation management layer is used for monitoring, submitting and recording the operation rules.
Further, computing resource isolation is set among the data source, the rule parsing layer, the computing layer, the middleware, the application layer and the job management layer.
In another aspect, an embodiment of the present invention further includes a computer apparatus, including a memory and a processor, where the memory is used to store at least one program, and the processor is used to load the at least one program to perform the method of the embodiment.
In another aspect, the present invention further includes a storage medium in which a program executable by a processor is stored, and the program executable by the processor is used to execute the data real-time stream processing method in the embodiment when executed by the processor.
The invention has the beneficial effects that: the real-time stream processing method and the real-time stream processing system in the embodiment can realize the abstraction of the rules and the combination of the real-time stream processing and the rule engine, thereby effectively decoupling the rules and the parameters, enabling the rules to be multiplexed and recombined according to the business requirements and reducing the repeated work. In addition, due to the isolation of computing resources, different operations can be effectively segmented, mutual influence is avoided, a service worker can be effectively helped to separate service rules from application program codes, and the development complexity and development cost of the early warning model are reduced.
Drawings
FIG. 1 is a flow chart of a method for processing a real-time stream of data according to an embodiment;
FIG. 2 is a schematic structural diagram of a data real-time stream processing system in an embodiment;
FIG. 3 is a schematic diagram of the step of determining the association relationship between the rule model and the corresponding threshold parameter in the embodiment;
FIG. 4 is a schematic diagram illustrating a step of combining a plurality of rule models into a job rule according to an embodiment;
fig. 5 is a schematic diagram illustrating a step of acquiring service data and matching the service data with the parsed operation rule to generate an early warning message in the embodiment;
FIG. 6 is a schematic diagram illustrating a method for performing real-time streaming of data on two rule models according to an embodiment.
Detailed Description
In this embodiment, referring to fig. 1, the data real-time stream processing method includes the following steps:
s1, developing a rule model;
s2, determining the incidence relation between the rule model and the corresponding threshold parameter;
s3, injecting the associated threshold parameters into the rule model;
s4, combining the rule models into an operation rule;
s5, acquiring service data, and matching the service data with the analyzed operation rule to generate an early warning message;
and S6, pushing the early warning message.
In the present embodiment, the data real-time stream processing method may be performed using the data real-time stream processing system as shown in fig. 2, i.e., the steps S1-S6 are performed using the data real-time stream processing system.
Referring to fig. 2, the data real-time stream processing system includes an application layer, a job management layer, a rule parsing layer, a calculation layer, early warning message middleware, and a data source. The application layer is responsible for data source management, rule model management, operation management, early warning pushing, early warning visualization and early warning tracing, and is in butt joint with the operation management layer, the rule analysis layer and early warning message middleware; the operation management layer is responsible for operation monitoring, operation log and operation submission and is in butt joint with the computing layer; the rule analysis layer is responsible for associating the rule model with the data source, injecting threshold parameters into the rule model, and combining the rule models to finally form operation; the computing layer is realized based on an open source real-time computing framework flink, is responsible for receiving the service data of the data source, matches the analyzed operation rule in real time, and generates an early warning message and writes the early warning message into an early warning message middleware when the service data is matched with the rule; the early warning message middleware is responsible for carrying out persistent cache on the early warning message written in the computing layer to avoid loss, pushing the early warning message to the application layer in real time, and carrying out visualization by the application layer or forwarding the early warning message to an external system; the data source refers to a service data storage location, and the embodiment supports multiple data sources, including a message middleware kafka, a distributed key value pair database hbase, a relational database pgsql, a relational database mysql, a relational database oracle, and a full-text search engine elastic search.
In this embodiment, step S1 may be executed by the data source, that is, the step of developing the rule model is executed; executing steps S2-S4 by the rule analysis layer, namely determining the incidence relation between the rule model and the corresponding threshold parameter, injecting the associated threshold parameter into the rule model, combining a plurality of rule models into an operation rule and the like; executing step S5 by the computing layer, that is, acquiring the service data, and matching the service data with the parsed operation rule, thereby generating an early warning message; step S6, i.e., the step of pushing the warning message, is performed by the middleware.
In this embodiment, the middleware executes step S6 to push the warning message to the application layer. The application layer receives the early warning message pushed by the middleware, can visualize the early warning message, or forwards the early warning message to the outside.
In this embodiment, the data source, the rule parsing layer, the computing layer, the middleware, the application layer, and the job management layer are isolated by setting computing resources, so that each job runs in a completely isolated computing environment, and mutual influence is avoided. The method has the advantages of isolating computing resources and running logs, and is convenient for subsequent positioning problems.
In this embodiment, step S2, namely, the step of determining the association relationship between the rule model and the corresponding threshold parameter, includes:
s201, selecting a data source of service data;
s202, associating an event date field in a data source, wherein the event date field is used as rule alarm time;
s203, compiling a judgment rule;
s204, exposing threshold parameters corresponding to the judgment rules;
s205, declaring a threshold parameter.
The principle of steps S201-S205 is shown in fig. 3. Firstly, selecting a data source of business data, associating an event date field in the data source for being used as rule alarm time subsequently, writing a judgment rule and exposing a corresponding threshold parameter, and finally declaring the rule threshold parameter for injecting an actual threshold parameter value during the generation of subsequent operation.
In this embodiment, step S3, namely the step of combining a plurality of rule models into a job rule, includes:
s301, pulling a plurality of rule models;
s302, associating the rule models in an OR mode, wherein the association result is an operation rule.
The principle of steps S301-S302 is as shown in fig. 4, first, a developed rule model is injected with specific parameter values according to its registered threshold parameters; and then judging whether other rule models need to be added according to business needs, if so, pulling other rule models and associating with the existing rule models, wherein the association mode comprises a sum and/or, and means that the two rule models are only established when both the two rule models meet the condition, or means that the two rule models are established as long as one of the two rule models meets the condition. And finally, configuring the operation, wherein the operation comprises the resource quota (such as CPU core number, memory number and the like) of the operation and an early warning pushing end, and the early warning pushing end is used for pushing early warning information to a corresponding external system when the subsequent operation is performed.
In this embodiment, step S5, namely, acquiring the service data, and matching the service data with the parsed operation rule, so as to generate the warning message, includes:
s501, generating a computing node set according to the computing environment configuration of the operation rule;
s502, performing data source registration and user-defined function registration;
s503, executing computing resource docking, and distributing the operation rule to the computing nodes in the computing node set for computing;
s504, acquiring service data from a data source;
s505, matching the service data with the operation rule in real time;
s506, if the business data and the operation rule accord with the matching rule, the business data is packaged into early warning information.
When the data real-time stream processing method is executed using the data real-time stream processing system, the principle of steps S501 to S506 is as shown in fig. 5. Referring to fig. 5, a job management layer generates a corresponding computing node set according to the computing environment configuration of a job, then calls a job submission module, and the job submission module performs data source registration, user-defined function registration, and final and actual computing resource docking and distribution of the job to the corresponding node for computing. The operation receives the data of the service data source, matches the data with the rule in real time, and encapsulates the data according with the matching rule into an early warning message which is sent to an early warning push end in real time.
In this embodiment, the principles of the data real-time stream processing method and the data real-time stream processing system are described with reference to specific examples. Referring to fig. 6, in the present embodiment, two regular models are selected, where a first model is a water immersion point early warning model, a parameter value of the first model is that a water level is greater than or equal to 10 centimeters, and a water immersion point is a xxx tunnel; the second model is a traffic jam model, the parameter value of the traffic jam model is that the jam index is greater than or equal to 3, and the jammed road section is a xxx tunnel. The combination relationship of the two models is yes. The whole operation shows that when rainfall high water level occurs in the xxx tunnel and road congestion is found, the information is pushed to an emergency department to process related problems.
In this embodiment, the real-time stream processing method and the real-time stream processing system can implement an atomic rule model, a custom rule model, a model combination, a model multiplexing, an operation alarm pushing, a real-time calculation, a state retention, a fault-tolerant processing, a concurrent calculation, and the like. The atomic rule model is an atomic model which provides various established service domain atomic models, such as waterlogging-prone point early warning in an emergency scene, safety helmet AI early warning in a building scene, atmospheric site early warning in an ecological scene and the like, and can be directly injected into a threshold value for use; the self-defined rule model is an atomic rule model which is already defined and also supports the user to define the rule model. Selecting a data source, configuring a judgment rule and exposing a threshold parameter, and flexibly compiling a service rule by simply combining and defining condition factors; the model combination refers to that if there are associable general attributes such as people, events, places, objects, organizations and the like in a plurality of different models, the models are combined with each other to form a new special model; the model multiplexing refers to that the created model supports parallel execution of a plurality of jobs with different parameters, and the jobs are completely isolated and do not influence each other; the operation alarm pushing refers to supporting an operation alarm subscription pushing function, judging alarms in real time and pushing the alarms to a subscription end in real time. One job supports a plurality of subscribers, and the same alarm data is issued to different subscribers at the same time.
The real-time stream processing method and the real-time stream processing system in the embodiment can realize the abstraction of the rules and combine the real-time stream processing and the rule engine. The abstraction of the rule means that the rule is divided into a rule model and a rule operation, wherein the rule model is used for declaring a threshold parameter, and the rule operation is used for injecting a specific threshold parameter and realizing the combination of a plurality of models. Based on the description, the rules and the parameters can be effectively decoupled, the rules can be multiplexed and recombined according to the service requirements, and the repeated work is reduced. In addition, the isolation of computing resources can effectively divide different operations, avoid mutual influence and effectively help the service staff to separate the service rules from the application program codes.
In this embodiment, a computer apparatus includes a memory and a processor, where the memory is used to store at least one program, and the processor is used to load the at least one program to execute the data real-time stream processing method in the embodiment, so as to achieve the same technical effects as those described in the embodiment.
In the present embodiment, a storage medium in which a processor-executable program is stored, the processor-executable program being configured to execute the data real-time stream processing method in the embodiments when executed by a processor, achieves the same technical effects as described in the embodiments.
It should be noted that, unless otherwise specified, when a feature is referred to as being "fixed" or "connected" to another feature, it may be directly fixed or connected to the other feature or indirectly fixed or connected to the other feature. Furthermore, the descriptions of upper, lower, left, right, etc. used in the present disclosure are only relative to the mutual positional relationship of the constituent parts of the present disclosure in the drawings. As used in this disclosure, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. In addition, unless defined otherwise, all technical and scientific terms used in this example have the same meaning as commonly understood by one of ordinary skill in the art. The terminology used in the description of the embodiments herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in this embodiment, the term "and/or" includes any combination of one or more of the associated listed items.
It will be understood that, although the terms first, second, third, etc. may be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element of the same type from another. For example, a first element could be termed a second element, and, similarly, a second element could be termed a first element, without departing from the scope of the present disclosure. The use of any and all examples, or exemplary language ("e.g.," such as "or the like") provided with this embodiment is intended merely to better illuminate embodiments of the invention and does not pose a limitation on the scope of the invention unless otherwise claimed.
It should be recognized that embodiments of the present invention can be realized and implemented by computer hardware, a combination of hardware and software, or by computer instructions stored in a non-transitory computer readable memory. The methods may be implemented in a computer program using standard programming techniques, including a non-transitory computer-readable storage medium configured with the computer program, where the storage medium so configured causes a computer to operate in a specific and predefined manner, according to the methods and figures described in the detailed description. Each program may be implemented in a high level procedural or object oriented programming language to communicate with a computer system. However, the program(s) can be implemented in assembly or machine language, if desired. In any case, the language may be a compiled or interpreted language. Furthermore, the program can be run on a programmed application specific integrated circuit for this purpose.
Further, operations of processes described in this embodiment can be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. The processes described in this embodiment (or variations and/or combinations thereof) may be performed under the control of one or more computer systems configured with executable instructions, and may be implemented as code (e.g., executable instructions, one or more computer programs, or one or more applications) collectively executed on one or more processors, by hardware, or combinations thereof. The computer program includes a plurality of instructions executable by one or more processors.
Further, the method may be implemented in any type of computing platform operatively connected to a suitable interface, including but not limited to a personal computer, mini computer, mainframe, workstation, networked or distributed computing environment, separate or integrated computer platform, or in communication with a charged particle tool or other imaging device, and the like. Aspects of the invention may be embodied in machine-readable code stored on a non-transitory storage medium or device, whether removable or integrated into a computing platform, such as a hard disk, optically read and/or write storage medium, RAM, ROM, or the like, such that it may be read by a programmable computer, which when read by the storage medium or device, is operative to configure and operate the computer to perform the procedures described herein. Further, the machine-readable code, or portions thereof, may be transmitted over a wired or wireless network. The invention described in this embodiment includes these and other different types of non-transitory computer-readable storage media when such media include instructions or programs that implement the steps described above in conjunction with a microprocessor or other data processor. The invention also includes the computer itself when programmed according to the methods and techniques described herein.
A computer program can be applied to input data to perform the functions described in the present embodiment to convert the input data to generate output data that is stored to a non-volatile memory. The output information may also be applied to one or more output devices, such as a display. In a preferred embodiment of the invention, the transformed data represents physical and tangible objects, including particular visual depictions of physical and tangible objects produced on a display.
The above description is only a preferred embodiment of the present invention, and the present invention is not limited to the above embodiment, and any modifications, equivalent substitutions, improvements, etc. within the spirit and principle of the present invention should be included in the protection scope of the present invention as long as the technical effects of the present invention are achieved by the same means. The invention is capable of other modifications and variations in its technical solution and/or its implementation, within the scope of protection of the invention.
Claims (10)
1. A method for processing a real-time stream of data, comprising:
developing a rule model;
determining the association relation between the rule model and the corresponding threshold parameter;
injecting the associated threshold parameter into the rule model;
combining the plurality of rule models into an operation rule;
acquiring service data, and matching the service data with the analyzed operation rule so as to generate an early warning message; and pushing the early warning message.
2. The method for processing the data real-time stream according to claim 1, wherein the determining the association relationship between the rule model and the corresponding threshold parameter comprises:
selecting a data source of service data;
associating an event date field in the data source; the event date field is used as a rule alarm time;
compiling a judgment rule;
exposing a threshold parameter corresponding to the judgment rule;
the threshold parameter is declared.
3. The method according to claim 1, wherein the combining the plurality of rule models into the operation rule comprises:
pulling a plurality of rule models;
associating each rule model in an OR mode; the result of the association is the job rule.
4. The method for processing the data real-time stream according to claim 1, wherein the acquiring the service data and matching the service data with the parsed operation rule to generate the warning message comprises:
generating a computing node set according to the computing environment configuration of the operation rule;
executing data source registration and user self-defined function registration;
executing computing resource docking, and distributing the operation rule to the computing nodes in the computing node set for computing;
acquiring service data from a data source;
matching the service data with the operation rule in real time;
and if the service data and the operation rule accord with a matching rule, packaging the service data into an early warning message.
5. A system for real-time streaming of data, comprising:
a data source for developing a rule model;
the rule analysis layer is used for determining the incidence relation between the rule model and the corresponding threshold parameter, injecting the associated threshold parameter into the rule model, and combining a plurality of rule models into an operation rule;
the calculation layer is used for acquiring service data and matching the service data with the analyzed operation rule so as to generate an early warning message;
and the middleware is used for pushing the early warning message.
6. The real-time streaming data processing system of claim 5, further comprising:
and the application layer is used for receiving the early warning message pushed by the middleware, and visualizing the early warning message or forwarding the early warning message to the outside.
7. The real-time streaming data processing system of claim 6, further comprising:
and the operation management layer is used for monitoring, submitting and recording the operation rules.
8. The real-time streaming data processing system of claim 7, wherein computing resource isolation is provided between the data source, the rule parsing layer, the computing layer, the middleware, the application layer, and the job management layer.
9. A computer apparatus comprising a memory for storing at least one program and a processor for loading the at least one program to perform the method of any one of claims 1 to 4.
10. A storage medium having stored therein a program executable by a processor, wherein the program executable by the processor is adapted to perform the method of any one of claims 1-4 when executed by the processor.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011171218.5A CN112422638A (en) | 2020-10-28 | 2020-10-28 | Data real-time stream processing method, system, computer device and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011171218.5A CN112422638A (en) | 2020-10-28 | 2020-10-28 | Data real-time stream processing method, system, computer device and storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN112422638A true CN112422638A (en) | 2021-02-26 |
Family
ID=74841011
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011171218.5A Pending CN112422638A (en) | 2020-10-28 | 2020-10-28 | Data real-time stream processing method, system, computer device and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112422638A (en) |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112966002A (en) * | 2021-02-28 | 2021-06-15 | 新华三信息安全技术有限公司 | Security management method, device, equipment and machine readable storage medium |
| CN113034056A (en) * | 2021-05-06 | 2021-06-25 | 广东鹰视能效科技有限公司 | Early warning identification method and system |
| CN113205666A (en) * | 2021-05-06 | 2021-08-03 | 广东鹰视能效科技有限公司 | Early warning method |
| CN114022279A (en) * | 2021-11-05 | 2022-02-08 | 税友软件集团股份有限公司 | Service data error correction method, device, equipment and readable storage medium |
| CN114629954A (en) * | 2022-03-16 | 2022-06-14 | 上海哔哩哔哩科技有限公司 | Content distribution method and system |
| CN115527354A (en) * | 2022-08-30 | 2022-12-27 | 国核信息科技有限公司 | Dynamic configuration method and system for early warning rules of wind turbine generator |
| CN116166701A (en) * | 2023-03-17 | 2023-05-26 | 湖北坤盈数字科技有限公司 | Service data real-time early warning method, device, equipment and storage medium |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20090150319A1 (en) * | 2007-12-05 | 2009-06-11 | Sybase,Inc. | Analytic Model and Systems for Business Activity Monitoring |
| CN105005525A (en) * | 2015-08-17 | 2015-10-28 | 深圳中兴网信科技有限公司 | Middleware based service data monitoring method and system |
| CN109257200A (en) * | 2017-07-14 | 2019-01-22 | 北京京东尚科信息技术有限公司 | The method and apparatus of big data platform monitoring |
| CN111190794A (en) * | 2019-12-30 | 2020-05-22 | 天津浪淘科技股份有限公司 | Operation and maintenance monitoring and management system |
| CN111325463A (en) * | 2020-02-18 | 2020-06-23 | 深圳前海微众银行股份有限公司 | Data quality detection method, device, equipment and computer readable storage medium |
-
2020
- 2020-10-28 CN CN202011171218.5A patent/CN112422638A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20090150319A1 (en) * | 2007-12-05 | 2009-06-11 | Sybase,Inc. | Analytic Model and Systems for Business Activity Monitoring |
| CN105005525A (en) * | 2015-08-17 | 2015-10-28 | 深圳中兴网信科技有限公司 | Middleware based service data monitoring method and system |
| CN109257200A (en) * | 2017-07-14 | 2019-01-22 | 北京京东尚科信息技术有限公司 | The method and apparatus of big data platform monitoring |
| CN111190794A (en) * | 2019-12-30 | 2020-05-22 | 天津浪淘科技股份有限公司 | Operation and maintenance monitoring and management system |
| CN111325463A (en) * | 2020-02-18 | 2020-06-23 | 深圳前海微众银行股份有限公司 | Data quality detection method, device, equipment and computer readable storage medium |
Non-Patent Citations (1)
| Title |
|---|
| 张小翠;: "监控阈值模型及报警事件关联规则研究", 中国金融电脑 * |
Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112966002A (en) * | 2021-02-28 | 2021-06-15 | 新华三信息安全技术有限公司 | Security management method, device, equipment and machine readable storage medium |
| CN112966002B (en) * | 2021-02-28 | 2023-04-18 | 新华三信息安全技术有限公司 | Security management method, device, equipment and machine readable storage medium |
| CN113034056A (en) * | 2021-05-06 | 2021-06-25 | 广东鹰视能效科技有限公司 | Early warning identification method and system |
| CN113205666A (en) * | 2021-05-06 | 2021-08-03 | 广东鹰视能效科技有限公司 | Early warning method |
| CN113205666B (en) * | 2021-05-06 | 2022-06-17 | 广东鹰视能效科技有限公司 | Early warning method |
| CN114022279A (en) * | 2021-11-05 | 2022-02-08 | 税友软件集团股份有限公司 | Service data error correction method, device, equipment and readable storage medium |
| CN114629954A (en) * | 2022-03-16 | 2022-06-14 | 上海哔哩哔哩科技有限公司 | Content distribution method and system |
| CN115527354A (en) * | 2022-08-30 | 2022-12-27 | 国核信息科技有限公司 | Dynamic configuration method and system for early warning rules of wind turbine generator |
| CN116166701A (en) * | 2023-03-17 | 2023-05-26 | 湖北坤盈数字科技有限公司 | Service data real-time early warning method, device, equipment and storage medium |
| CN116166701B (en) * | 2023-03-17 | 2023-07-25 | 湖北坤盈数字科技有限公司 | Service data real-time early warning method, device, equipment and storage medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112422638A (en) | Data real-time stream processing method, system, computer device and storage medium | |
| CA3111422C (en) | User interface for automated flows within a cloud based developmental platform | |
| US10956013B2 (en) | User interface for automated flows within a cloud based developmental platform | |
| US10101972B1 (en) | Data modelling and flow engine for building automated flows within a cloud based developmental platform | |
| US20200090052A1 (en) | Decision tables and enterprise rules for object linking within an application platform as a service environment | |
| US11137732B2 (en) | Flow engine for building automated flows within a cloud based developmental platform | |
| CN100492290C (en) | Method for acquiring and converting the state of component in the time of operation during software dynamic evolvement | |
| CN111026779A (en) | Data processing method, device and storage medium based on Flink SQL | |
| CN103559118A (en) | Security auditing method based on aspect oriented programming (AOP) and annotation information system | |
| CN105474171A (en) | Model-based approach to intelligent automation in a computing domain | |
| AU2018229432B1 (en) | Data modelling and flow engine for building automated flows within a cloud based developmental platform | |
| WO2015138568A1 (en) | Analyzing components related to a software application in a software development environment | |
| US10592304B2 (en) | Suggesting application programming interfaces based on feature and context analysis | |
| CN105893055A (en) | Method for triggering process engine platformization | |
| Ameedeen et al. | A model driven approach to represent sequence diagrams as free choice Petri nets | |
| Katta et al. | Requirements management in a combined process for safety and security assessments | |
| Chiș | A modeling method for model-driven API management | |
| CN111966718A (en) | System and method for data propagation tracking of application systems | |
| US10970055B2 (en) | Identifying software and hardware bottlenecks | |
| Sun et al. | Contract-aware slicing of UML class models | |
| US9021418B2 (en) | Systems and/or methods for creation of metamodels | |
| US20130138690A1 (en) | Automatically identifying reused model artifacts in business process models | |
| US11119760B2 (en) | Identifying and deleting unnecessary code lines | |
| CN111399806B (en) | Component management method and device, electronic equipment and storage medium | |
| US9274762B2 (en) | System and method for developing an object-oriented system |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20210226 |
|
| RJ01 | Rejection of invention patent application after publication |