CN113034056A - Early warning identification method and system - Google Patents
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Abstract
An early warning identification method and system, comprising: collecting and backing up binary data of an environment or equipment to be early-warned; adding acquisition items, and adding global standardized early warning rules and/or customized early warning rules; converting the binary data into data conforming to a json protocol; pushing json data to a RabbitMq queue; monitoring a RabbitMq queue in real time, and performing data verification on json data to obtain correct data; storing correct data to a Redis database to backup the data and storing the correct data to an Ehcache cache frame to run the data; acquiring correct data from an Ehcache cache frame, matching an early warning rule according to an acquisition item, identifying a combination and upgrade mechanism of early warning, and explaining the early warning rule to perform early warning; triggering early warning, generating an early warning log, and reporting an early warning condition; and (5) removing the abnormity, triggering an early warning recovery mechanism, and generating an early warning recovery log. The invention realizes the effects of finding the abnormity in time, efficiently and flexibly accumulating abnormity identification experience, dynamically improving the abnormity grade according to the environment and improving the abnormity processing efficiency.
Description
Technical Field
The invention relates to the technical field of early warning identification, in particular to an early warning identification method and system.
Background
In the prior art, the early warning system has various types and different functions, is used for forest fire prevention, public security management, health management and the like. The key safety parameters in the natural and production processes are generally monitored, and when the critical safety parameters reach a limit value, a strong prompt alarm is generated. However, the common alarm mode is single, the content of the configured rule is not friendly, the simple threshold configuration can be performed only according to the existing collected data model, some early warning logics which are suitable for local conditions and some early warning logics which are calculated by complex formulas are needed, the user is often required to calculate the threshold result by himself and then configure the threshold into the rule, and the early warning logics cannot be really solidified in the rule.
Disclosure of Invention
The invention aims to provide an early warning identification method and an early warning identification system aiming at the defects in the background art, wherein early warning logic is solidified in rules through preset global standardized early warning rules and customized client early warning rules defined by a user based on an early warning combination mechanism and an early warning upgrading mechanism, early warning and analysis are carried out on abnormal dynamic change conditions of monitored values in time, abnormal identification experiences are efficiently and flexibly accumulated, abnormal grades are dynamically improved according to the environment, and the efficiency of abnormal processing is improved.
In order to achieve the purpose, the invention adopts the following technical scheme:
an early warning identification method comprises the following steps:
collecting binary data of an environment or equipment to be early-warned, and temporarily backing up the binary data;
adding a collection item to be early-warned based on the collected binary data, and adding a global standardized early-warning rule and/or a customized early-warning rule based on the collection item;
converting the binary data into data conforming to a json protocol;
the converted json data are pushed to a RabbitMq queue concurrently;
monitoring a RabbitMq queue in real time, carrying out data verification on json data, and eliminating problem data to obtain correct data;
storing correct data into a Redis database to perform primary caching to backup the data and storing the correct data into an Ehcache cache frame to perform secondary caching to run the data;
acquiring correct data from an Ehcache cache frame, matching an early warning rule according to an acquisition item, identifying a combination and upgrade mechanism of early warning, and concurrently interpreting the early warning rule based on a thread pool to perform early warning;
triggering early warning, generating an early warning log, and reporting an early warning condition;
and (5) removing the abnormity, triggering an early warning recovery mechanism, and generating an early warning recovery log.
Preferably, the early warning combination mechanism reuses the existing early warning rules to perform complex early warning, and specifically includes:
newly establishing an early warning rule;
and associating the newly-built early warning rule with other existing early warning rules to perform linkage early warning.
Preferably, the early warning upgrading mechanism configures a plurality of early warning rules to perform upgrading linkage early warning, and specifically includes:
configuring a plurality of early warning rules, and carrying out grade division on the early warning rules;
based on the divided grades, the lower-level early warning rule triggers the upper-level early warning rule to carry out early warning, and so on until the highest-level early warning rule is triggered to carry out early warning.
Preferably, each acquisition item can be added with a plurality of early warning rules;
the global standardized early warning rule is a preset early warning rule;
the customized early warning rule is an early warning rule needing to be defined by user;
the early warning rule comprises rule content, duration and early warning level;
the rule content comprises a trigger early warning condition formed by setting judgment logic according to binary data of the acquisition items;
the duration time comprises that when the rule content is judged to be effective, early warning can be triggered after the preset duration time;
the early warning level comprises the division of early warning grades according to severity.
Preferably, the data verification includes:
checking whether the acquired data is negative, if so, carrying out negative elimination warning and eliminating problem data;
and checking whether the self-carried acquisition time of the acquired data exceeds the actual system time, if so, performing time exclusion alarm and eliminating problem data.
An early warning identification system comprises a memory, a processor, a cloud acquisition server and a cloud early warning server;
the processor is used for executing the binary data of the acquisition environment or the equipment to be early-warned and transmitting the binary data to the memory and the cloud acquisition server;
the memory is used for temporarily backing up binary data;
the cloud acquisition server is used for converting binary data into data conforming to a json protocol and pushing the json data to a RabbitMq queue;
the cloud early warning server comprises an adding submodule, a monitoring submodule, a checking submodule, a matching identification submodule, an early warning submodule and an early warning recovery submodule;
the adding submodule is used for adding an acquisition item to be early-warned based on the acquired binary data, and adding a global standardized early warning rule and/or a customized early warning rule based on the acquisition item;
the monitoring submodule is used for monitoring a RabbitMq queue to obtain json data;
the checking submodule is used for checking data and eliminating problems to obtain correct data, storing the correct data into a Redis database to perform primary caching to backup the data and storing the correct data into an Ehcache cache frame to perform secondary caching to run the data;
the matching identification submodule is used for identifying the combination and upgrading mechanism of the early warning according to the acquisition item matching early warning rule;
the early warning sub-module is used for carrying out early warning based on the concurrent interpretation of the early warning rules of the thread pool, generating an early warning log and reporting an early warning condition;
and the early warning recovery submodule is used for triggering an early warning recovery mechanism to generate an early warning recovery log when abnormal contact occurs.
Preferably, the matching identification submodule comprises an early warning combination subunit;
the early warning combination subunit is used for creating an early warning rule to associate with other existing early warning rules for linkage early warning.
Preferably, the matching identification subunit comprises an early warning upgrading subunit;
the early warning upgrading subunit is used for configuring a plurality of early warning rules, carrying out grade division on the early warning rules, triggering the upper early warning rule to carry out early warning by the lower early warning rule based on the divided grade, and analogizing until the highest early warning rule is triggered to carry out early warning.
Preferably, the check submodule comprises a first check subunit and a second check subunit;
the first checking subunit is used for checking whether the acquired data is negative, and if so, negative elimination warning is carried out to eliminate problem data;
the second checking subunit is used for checking whether the self-carried acquisition time of the acquired data exceeds the actual system time, and if so, time exclusion warning is carried out to exclude the problem data.
Preferably, the adding submodule is further configured to perform an operation of adding a preset global standardized early warning rule and/or an operation of executing a custom early warning rule.
Compared with the prior art, the invention has the following beneficial effects:
according to the invention, through a preset global standardized early warning rule and a customized early warning rule defined by a user, based on an early warning combination mechanism and an early warning upgrading mechanism, early warning logic is solidified in the rule, early warning and analysis are carried out on abnormal dynamic change conditions of the monitored values in time, abnormal recognition experience is efficiently and flexibly accumulated, the abnormal grade is dynamically improved according to the environment, and the efficiency of abnormal processing is improved.
Drawings
Fig. 1 is a timing diagram of an early warning identification process of one embodiment of the invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are illustrative only for the purpose of explaining the present invention and are not to be construed as limiting the invention;
any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process, and alternate implementations are included within the scope of the preferred embodiment of the present invention in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the present invention.
The technical scheme of the invention is further explained by the specific implementation mode in combination with the attached drawings.
In the prior art, the early warning mode is single, the content of the configured rule is not friendly, the simple threshold configuration can be performed only according to the existing collected data model, some early warning logics which need to be adapted to local conditions and some early warning logics which need to be calculated by a complex formula often need to be calculated by a user, then the threshold is configured into the rule, and the early warning logics cannot be really solidified in the rule.
Collecting binary data of an environment or equipment to be early-warned, and temporarily backing up the binary data;
in the embodiment, a processor is used for collecting binary data of an environment or equipment, and a memory is used for temporarily backing up the binary data so as to prevent the collected data from being lost;
adding a collection item to be early-warned based on the collected binary data, and adding a global standardized early-warning rule and/or a customized early-warning rule based on the collection item;
in this embodiment, the acquisition items may include items such as current, voltage, harmonic, temperature, and the like, and the specific acquisition items are determined according to actual production and according to the acquisition capability of the processor; the addition and deletion of the acquisition items can dynamically react to the early warning;
preferably, each acquisition item can be added with a plurality of early warning rules;
the global standardized early warning rule is a preset early warning rule, and it needs to be explained that the preset early warning rule is an early warning rule carried by the system and does not need to be set by a user;
the customized early warning rule is an early warning rule needing to be customized, and it needs to be explained that the customized early warning rule needs a user to set the early warning rule by himself and then is added;
the global standardized early warning rule and the customized early warning rule can be automatically switched, namely, after a user selects the global standardized early warning rule, the global standardized early warning rule can be converted into the customized early warning rule, and the conversion of the early warning rule can dynamically react to early warning;
the early warning rule comprises rule content, duration and early warning level;
the rule content comprises a trigger early warning condition formed by setting judgment logic according to binary data of the acquisition items;
for example, in this embodiment, the warning rule is based on a grammar close to natural language, and the logic is determined by a record or voice control input according to actual business requirements, for example: (corning. actual value > (corning. ratinga. 1.07-10)) & (corning. measurepcontact name. equals ("xxx collection point") | corning. measurepcontact area. contact ("xxx city"));
the early warning rule means that the actual value of the collected data is greater than a rated value x 1.07-10, and the name of the collection point is a xxx collection point or the area of the collection point is a xxx city, the judgment is valid; except individual keywords, the grammar of the content of the early warning rule is the same as that of natural language, so that the early warning rule is convenient to write;
meanwhile, the system also provides another mode for generating rule contents: setting a simple threshold value, and generating the content of the complaint rule according to the set threshold value;
the duration time comprises that when the rule content is judged to be effective, early warning can be triggered after the preset duration time; that is, after the logic in the rule content is triggered, the logic needs to last for a period of time, so that the early warning can be triggered.
The early warning level comprises the division of early warning grades according to severity.
If the color is divided into blue (general), yellow (heavy) and red (serious) according to the severity, the early warning grades are divided according to different colors;
converting the binary data into data conforming to a json protocol;
the converted json data are pushed to a RabbitMq queue concurrently;
the cloud acquisition server interprets the binary data into data conforming to a json protocol after receiving the binary data sent by the processor, and pushes the json data to a RabbitMq queue by fully utilizing a multi-core processor of the cloud server by using a java thread pool;
monitoring a RabbitMq queue in real time, carrying out data verification on json data, and eliminating problem data to obtain correct data;
the monitoring submodule acquires json data by monitoring a RabbitMq queue, and concurrently performs data verification on the acquired data by using a java thread pool to eliminate problem data;
specifically, the data verification includes:
checking whether the acquired data is negative, if so, carrying out negative elimination warning and eliminating problem data;
checking whether the self-carried acquisition time of the acquired data exceeds the actual system time, if so, performing time exclusion alarm and excluding problem data;
the early warning result can be effectively prevented from being interfered by error data through checking the data.
Storing correct data into a Redis database to perform primary caching to backup the data and storing the correct data into an Ehcache cache frame to perform secondary caching to run the data;
the checking submodule further comprises a Redis database, the Redis database performs a first-level cache function, the correct data are backed up, loss of the correct data is prevented, meanwhile, the correct data are stored in an Ehcache cache frame, the Ehcache frame performs a second-level cache function, data are obtained from the Ehcache frame according to a follow-up matching early warning rule, and the condition that when a large amount of data are applied, the system is crashed or runs slowly due to overlarge data amount can be effectively avoided by setting two-level storage.
Acquiring correct data from an Ehcache cache frame, matching an early warning rule according to an acquisition item, identifying a combination and upgrade mechanism of early warning, and concurrently interpreting the early warning rule based on a thread pool to perform early warning;
the matching identification submodule selects a corresponding early warning rule from the selected global standardized early warning rule or the customized early warning rule for matching according to different acquisition items;
preferably, the early warning combination mechanism reuses the existing early warning rules to perform complex early warning, and specifically includes:
newly establishing an early warning rule;
and associating the newly-built early warning rule with other existing early warning rules to perform linkage early warning.
Preferably, the early warning upgrading mechanism configures a plurality of early warning rules to perform upgrading linkage early warning, and specifically includes:
configuring a plurality of early warning rules, and carrying out grade division on the early warning rules;
based on the divided grades, the lower-level early warning rule triggers the upper-level early warning rule to carry out early warning, and so on until the highest-level early warning rule is triggered to carry out early warning.
The early warning combination subunit associates other existing early warning rules to perform linkage early warning by newly establishing an early warning rule, for example, the following scenarios:
the fire alarm needs to be warned, and the current rules are as follows:
A. the carbon monoxide gas concentration is ultrahigh, and the early warning rule (the duration is one minute, and the early warning level is particularly serious);
B. the air temperature is ultrahigh, and the early warning rule (the duration is one minute, and the early warning level is particularly serious);
C. the dust concentration is ultrahigh, and the early warning rule (the duration is one minute, and the early warning level is particularly serious);
and (3) actual operation after combination: the client adds a fire alarm early warning rule to be associated with the three early warning rules, when two of the three early warning rules take effect, the fire alarm can be triggered to be early warned, the existing early warning rules are reused to carry out complex early warning by newly building an early warning rule, no acquisition equipment is required to be newly added, no repeated early warning logic is required to be designed, and the cost is saved to the maximum extent;
further, the early warning upgrading subunit configures upgrading linkage of multiple early warning rules according to service requirements, so as to realize upgrading of early warning, for example, in the following scenarios:
the requirement carries out early warning to thunderstorm weather, has had the rule at present:
A. the heavy rain early warning rainfall is 25-50 mm in early warning rule (the duration is 24 hours, and the early warning level is serious);
B. the rainstorm rainfall is 50-100 mm in early warning rule (the duration is 24 hours, and the early warning level is serious);
C. the heavy rainstorm rainfall is 100-250 mm in early warning rule (the duration is 24 hours, and the early warning level is serious);
D. the early warning rule (the duration is 24 hours, the early warning level is serious) of the ultra-large heavy rain whose rainfall is more than 250 mm;
actual operation after upgrading: and adding one thunderstorm weather early warning rule to be associated with the 4 early warning rules, triggering the higher early warning to carry out early warning when the lower early warning takes effect, triggering the higher early warning again when the higher early warning takes effect, and repeating the steps until the highest early warning is achieved.
Triggering early warning, generating an early warning log, and reporting an early warning condition;
the report early warning condition can be notified by sending short messages, e-mails, WeChat messages, voice messages and other forms;
and (5) removing the abnormity, triggering an early warning recovery mechanism, and generating an early warning recovery log.
After the abnormity is relieved, the early warning recovery mechanism is triggered to recover to normal state, the condition of continuous early warning is prevented, and the relevant information of the early warning is stored by generating an early warning log and generating an early warning recovery log so as to facilitate subsequent checking.
An early warning identification system comprises a memory, a processor, a cloud acquisition server and a cloud early warning server;
the processor is used for executing the binary data of the acquisition environment or the equipment to be early-warned and transmitting the binary data to the memory and the cloud acquisition server;
the memory is used for temporarily backing up binary data;
the cloud acquisition server is used for converting binary data into data conforming to a json protocol and pushing the json data to a RabbitMq queue;
the cloud early warning server comprises an adding submodule, a monitoring submodule, a checking submodule, a matching identification submodule, an early warning submodule and an early warning recovery submodule;
the adding submodule is used for adding an acquisition item to be early-warned based on the acquired binary data, and adding a global standardized early warning rule and/or a customized early warning rule based on the acquisition item;
the monitoring submodule is used for monitoring a RabbitMq queue to obtain json data;
the checking submodule is used for checking data and eliminating problems to obtain correct data, storing the correct data into a Redis database to perform primary caching to backup the data and storing the correct data into an Ehcache cache frame to perform secondary caching to run the data;
the matching identification submodule is used for identifying the combination and upgrading mechanism of the early warning according to the acquisition item matching early warning rule;
the early warning sub-module is used for carrying out early warning based on the concurrent interpretation of the early warning rules of the thread pool, generating an early warning log and reporting an early warning condition;
and the early warning recovery submodule is used for triggering an early warning recovery mechanism to generate an early warning recovery log when abnormal contact occurs.
Preferably, the matching identification submodule comprises an early warning combination subunit;
the early warning combination subunit is used for creating an early warning rule to associate with other existing early warning rules for linkage early warning.
Preferably, the matching identification subunit comprises an early warning upgrading subunit;
the early warning upgrading subunit is used for configuring a plurality of early warning rules, carrying out grade division on the early warning rules, triggering the upper early warning rule to carry out early warning by the lower early warning rule based on the divided grade, and analogizing until the highest early warning rule is triggered to carry out early warning.
Preferably, the check submodule comprises a first check subunit and a second check subunit;
the first checking subunit is used for checking whether the acquired data is negative, and if so, negative elimination warning is carried out to eliminate problem data;
the second checking subunit is used for checking whether the self-carried acquisition time of the acquired data exceeds the actual system time, and if so, time exclusion warning is carried out to exclude the problem data.
Preferably, the adding submodule is further configured to perform an operation of adding a preset global standardized early warning rule and/or an operation of executing a custom early warning rule.
The technical principle of the present invention is described above in connection with specific embodiments. The description is only intended to explain the principles of the invention and should not be interpreted in any way as limiting the scope of the invention. Based on the explanations herein, those skilled in the art will be able to conceive of other embodiments of the present invention without inventive effort, which would fall within the scope of the present invention.
Claims (10)
1. An early warning identification method is characterized in that: the method comprises the following steps:
collecting binary data of an environment or equipment to be early-warned, and temporarily backing up the binary data;
adding a collection item to be early-warned based on the collected binary data, and adding a global standardized early-warning rule and/or a customized early-warning rule based on the collection item;
converting the binary data into data conforming to a json protocol;
the converted json data are pushed to a RabbitMq queue concurrently;
monitoring a RabbitMq queue in real time, carrying out data verification on json data, and eliminating problem data to obtain correct data;
storing correct data into a Redis database to perform primary caching to backup the data and storing the correct data into an Ehcache cache frame to perform secondary caching to run the data;
acquiring correct data from an Ehcache cache frame, matching an early warning rule according to an acquisition item, identifying a combination and upgrade mechanism of early warning, and concurrently interpreting the early warning rule based on a thread pool to perform early warning;
triggering early warning, generating an early warning log, and reporting an early warning condition;
and (5) removing the abnormity, triggering an early warning recovery mechanism, and generating an early warning recovery log.
2. The early warning identification method according to claim 1, wherein:
the early warning combination mechanism reuses the existing early warning rules to perform complex early warning, and specifically comprises the following steps:
newly establishing an early warning rule;
and associating the newly-built early warning rule with other existing early warning rules to perform linkage early warning.
3. The early warning identification method according to claim 1, wherein:
the upgrading linkage early warning is carried out by configuring a plurality of early warning rules by an upgrading mechanism of the early warning, and the method specifically comprises the following steps:
configuring a plurality of early warning rules, and carrying out grade division on the early warning rules;
based on the divided grades, the lower-level early warning rule triggers the upper-level early warning rule to carry out early warning, and so on until the highest-level early warning rule is triggered to carry out early warning.
4. The early warning identification method according to claim 1, wherein:
each acquisition item can be added with a plurality of early warning rules;
the global standardized early warning rule is a preset early warning rule;
the customized early warning rule is an early warning rule needing to be defined by user;
the early warning rule comprises rule content, duration and early warning level;
the rule content comprises a trigger early warning condition formed by setting judgment logic according to binary data of the acquisition items;
the duration time comprises that when the rule content is judged to be effective, early warning can be triggered after the preset duration time;
the early warning level comprises the division of early warning grades according to severity.
5. The early warning identification method according to claim 1, wherein:
the data verification comprises the following steps:
checking whether the acquired data is negative, if so, carrying out negative elimination warning and eliminating problem data;
and checking whether the self-carried acquisition time of the acquired data exceeds the actual system time, if so, performing time exclusion alarm and eliminating problem data.
6. An early warning identification system, characterized by: the system comprises a memory, a processor, a cloud acquisition server and a cloud early warning server;
the processor is used for executing the binary data of the acquisition environment or the equipment to be early-warned and transmitting the binary data to the memory and the cloud acquisition server;
the memory is used for temporarily backing up binary data;
the cloud acquisition server is used for converting binary data into data conforming to a json protocol and pushing the json data to a RabbitMq queue;
the cloud early warning server comprises an adding submodule, a monitoring submodule, a checking submodule, a matching identification submodule, an early warning submodule and an early warning recovery submodule;
the adding submodule is used for adding an acquisition item to be early-warned based on the acquired binary data, and adding a global standardized early warning rule and/or a customized early warning rule based on the acquisition item;
the monitoring submodule is used for monitoring a RabbitMq queue to obtain json data;
the checking submodule is used for checking data and eliminating problems to obtain correct data, storing the correct data into a Redis database to perform primary caching to backup the data and storing the correct data into an Ehcache cache frame to perform secondary caching to run the data;
the matching identification submodule is used for identifying the combination and upgrading mechanism of the early warning according to the acquisition item matching early warning rule;
the early warning sub-module is used for carrying out early warning based on the concurrent interpretation of the early warning rules of the thread pool, generating an early warning log and reporting an early warning condition;
and the early warning recovery submodule is used for triggering an early warning recovery mechanism to generate an early warning recovery log when abnormal contact occurs.
7. The warning identification system of claim 6, wherein:
the matching identification submodule comprises an early warning combination subunit;
the early warning combination subunit is used for creating an early warning rule to associate with other existing early warning rules for linkage early warning.
8. The warning identification system of claim 6, wherein:
the matching identification submodule comprises an early warning upgrading subunit;
the early warning upgrading subunit is used for configuring a plurality of early warning rules, carrying out grade division on the early warning rules, triggering the upper early warning rule to carry out early warning by the lower early warning rule based on the divided grade, and analogizing until the highest early warning rule is triggered to carry out early warning.
9. The warning identification system of claim 6, wherein:
the check submodule comprises a first check subunit and a second check subunit;
the first checking subunit is used for checking whether the acquired data is negative, and if so, negative elimination warning is carried out to eliminate problem data;
the second checking subunit is used for checking whether the self-carried acquisition time of the acquired data exceeds the actual system time, and if so, time exclusion warning is carried out to exclude the problem data.
10. The warning identification system of claim 6, wherein:
the adding submodule is also used for executing operation of adding the preset global standardized early warning rule and/or executing operation of self-defining early warning rule.
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