CN113205666A - Early warning method - Google Patents
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- CN113205666A CN113205666A CN202110492217.9A CN202110492217A CN113205666A CN 113205666 A CN113205666 A CN 113205666A CN 202110492217 A CN202110492217 A CN 202110492217A CN 113205666 A CN113205666 A CN 113205666A
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Abstract
The early warning method comprises the steps of performing early warning exception handling before early warning identification, wherein the early warning identification comprises data acquisition and backup; adding acquisition items, and adding global standardized early warning rules and/or customized early warning rules; converting the data into json data; pushing json data to a RabbitMq queue; monitoring a RabbitMq queue, and performing data verification to obtain correct data; storing correct data to a Redis database and an Ehcache cache frame; acquiring correct data from an Ehcache cache frame, matching an early warning rule, identifying a combination and upgrade mechanism of the early warning, and explaining the early warning rule to carry out early warning; triggering early warning, generating an early warning log, and reporting an early warning condition; and triggering an early warning recovery mechanism, removing the abnormity and generating an early warning recovery log. The invention realizes the effects of timely discovering the abnormity, efficiently and flexibly accumulating the abnormity identification experience, dynamically improving the abnormity grade according to the environment and improving the abnormity processing efficiency, accurately judging whether to remove the abnormity early warning state by utilizing the recovery coefficient and the recovery condition, and avoiding frequent early warning recovery and misinformation.
Description
Technical Field
The invention relates to the technical field of early warning, in particular to an early warning method.
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 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, the threshold result is configured into the rule, the early warning logics can not be really solidified in the rule, the early warning is easily sent out or removed, and the real control can not be realized.
Disclosure of Invention
The invention aims to provide an early warning method and an early warning system aiming at the defects in the background art, wherein early warning logic is solidified in a rule 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 and analysis are carried out on abnormal dynamic change conditions of a monitored value in time, abnormal recognition experience is efficiently and flexibly accumulated, abnormal grades are dynamically improved according to the environment, and the efficiency of abnormal processing is improved;
by means of an early warning recovery mechanism, recovery coefficients and recovery conditions are utilized to accurately judge whether an abnormal early warning state is relieved or not, and frequent early warning recovery and false alarm are avoided.
In order to achieve the purpose, the invention adopts the following technical scheme:
the early warning method comprises the steps of carrying out early warning exception handling before early warning identification;
the early warning identification 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 triggering an early warning recovery mechanism, removing the abnormity and generating an early warning recovery log.
The early warning recovery mechanism comprises:
and when the collected binary data is identified to be restored to the normal range, executing restoration judgment operation according to the restoration coefficient and the restoration condition, wherein the restoration judgment operation comprises judging whether the binary data is just restored to the critical value touching the normal range, if so, judging the restoration to be abnormal restoration, and if not, judging the restoration to be normal restoration.
Preferably, the early warning recovery mechanism specifically includes:
if the early warning is the upper limit early warning, the recovery judging operation comprises the step of judging whether the detection value is reduced to a set low-point value in a normal range;
if the early warning is the lower limit early warning, the recovery judgment operation comprises the step of judging whether the detection value rises to a set high-point value in a normal range;
the low point value and the high point value do not belong to the critical value of the normal range.
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.
Preferably, the early warning log comprises logs recording all full early warning states after the early warning takes effect;
the early warning recovery log comprises a log recording recovery time.
Preferably, the early warning exception handling includes the following steps:
step S1: acquiring original data of an environment or equipment to be early-warned, and judging whether an early-warning rule used in the early-warning identification process is a global standard early-warning rule or a customized early-warning rule based on the original data;
if the pre-warning rule is the customized pre-warning rule, executing step S3;
if the global standardized early warning rule is adopted, executing step S2;
step S2: setting a global standardized early warning rule;
step S3: performing pre-operation early warning based on the selected early warning rule, generating early warning records, and not reporting early warning information;
step S4: operating early warning judgment according to the early warning record, generating an early warning judgment operation result, triggering an early warning self-correcting mechanism to judge whether the early warning rule has a problem, and if so, suspending the early warning function of the early warning rule;
step S5: and judging whether the test run early warning is triggered according to the early warning judgment operation result, if so, judging that the test run early warning is abnormal, otherwise, judging that the test run early warning is abnormal, and repairing the test run early warning.
Preferably, the method for judging whether the early warning rule has a problem by the early warning self-correcting mechanism comprises the following steps:
judging whether the current early warning rule exceeds an error correction coefficient, if so, judging that the current early warning rule has a problem;
and suspending the early warning function of the early warning rule with the problem and repairing the early warning function.
Preferably, when the pre-warning of the test run is not triggered, whether the abnormality of the pre-warning is determined to be processed within a preset time is judged, and if the abnormality of the pre-warning is determined, the pre-warning is repaired; if not, an abnormal alarm is sent out.
Compared with the prior art, the invention has the following beneficial effects:
1. the method carries out early warning abnormity processing before early warning identification, judges whether the current early warning system is abnormal or not by carrying out trial operation early warning according to different early warning rules and judges whether the current early warning rule has problems or not by an early warning self-error correction mechanism, thereby realizing the self-checking of the early warning rules and improving the correctness of the early warning system.
2. 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 a monitored value 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;
3. according to the method, through an early warning recovery mechanism, whether the abnormal early warning state is relieved or not is accurately judged by utilizing the recovery coefficient and the recovery condition, and frequent early warning recovery and false alarm are avoided;
4. the invention provides a manual auditing function and early warning record for traceability analysis, ensures reliable and safe production operation, and informs related parties in advance to carry out preventive protection before danger occurs.
Drawings
FIG. 1 is a flow diagram of early warning exception handling according to one embodiment of the invention;
fig. 2 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.
The early warning method in the prior art is single, is unfriendly when rule contents are configured, can be simply configured according to an existing acquired data model, needs early warning logics according to local conditions and needs complex formula calculation, often needs a user to calculate a threshold value result by himself/herself and then configures the threshold value into a rule, and cannot really solidify the early warning logics in the rule, and in order to solve the problems, the early warning method is provided, and comprises the steps of performing early warning abnormal processing before early warning identification, as shown in fig. 2;
the early warning identification comprises the following steps:
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;
it should be noted that, when configuring the early warning rule, the user may be allowed to pass after performing manual review, if the user needs manual review, the user needs to initiate an application and needs to manually confirm within a certain time, and if the user does not manually confirm within a certain time, the rule is defaulted to be invalid, and the early warning cannot be triggered.
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;
acquiring json data by monitoring a RabbitMq queue, using a java thread pool, concurrently performing data verification on the acquired data, and eliminating 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 method comprises the steps of storing correct data into a Redis database, wherein the Redis data has a first-level cache function, backing up the correct data and preventing the correct data from being lost, and meanwhile, storing the correct data into an Ehcache cache frame, wherein the Ehcache cache frame has a second-level cache function, and acquiring data from the Ehcache cache frame according to a subsequent matching early warning rule.
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;
specifically, according to different acquisition items, matching is carried out by selecting a corresponding early warning rule from the selected global standardized early warning rules or customized early warning rules;
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 comprises the following steps of associating other existing early warning rules for linkage early warning by newly establishing an early warning rule, for example, the following scenes:
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 upgrade configures upgrade linkage of a plurality of early warning rules according to business requirements, so that the upgrade of early warning is realized, for example, in the following scenes:
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 triggering an early warning recovery mechanism, removing the abnormity 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.
The early warning recovery mechanism comprises:
and when the collected binary data is identified to be restored to the normal range, executing restoration judgment operation according to the restoration coefficient and the restoration condition, wherein the restoration judgment operation comprises judging whether the binary data is just restored to the critical value touching the normal range, if so, judging the restoration to be abnormal restoration, and if not, judging the restoration to be normal restoration.
Preferably, the early warning recovery mechanism specifically includes:
if the early warning is the upper limit early warning, the recovery judging operation comprises the step of judging whether the detection value is reduced to a set low-point value in a normal range;
if the early warning is the lower limit early warning, the recovery judgment operation comprises the step of judging whether the detection value rises to a set high-point value in a normal range;
the low point value and the high point value do not belong to the critical value of the normal range.
When the change of the collected data is recognized, in this embodiment, it can be understood that the data value is restored to the normal range value, at this time, the early warning state is not immediately released, but the judgment is performed according to the recovery coefficient and the restoration condition, when the detection value is judged, that is, the collected data falls to the low point value in the normal range or rises to the high point value in the normal range, the abnormal state is released, and it should be noted that the operation of releasing the abnormal state is not triggered when the detection value touches the critical value in the normal range.
There are two ways of setting the recovery coefficient and the recovery condition:
1. user-defined settings (two ways): a. specific numerical values can be filled in; b. filling in recovery content;
2. performing machine learning and artificial intelligent automatic judgment according to multiple times of early warning validation and recovery of the monitoring point or similar monitoring points;
preferably, the early warning log comprises logs recording all full early warning states after the early warning takes effect;
the early warning recovery log comprises a log recording recovery time.
In case that the triggering condition and the early warning process of the early warning system in the prior art are set, the triggering condition and the early warning process cannot be changed and checked under normal conditions, the situation that the early warning process and the triggering condition are not known by themselves due to the fact that the early warning process and the triggering condition are prone to causing problems can be easily caused, namely, the abnormity existing in the whole early warning system can not be identified and processed, so that the whole early warning system is prone to generating deviation when early warning is carried out, in order to solve the problems, the early warning abnormity processing is carried out before the early warning identification is carried out, and the early warning abnormity processing comprises the following steps:
step S1: acquiring original data of an environment or equipment to be early-warned, and judging whether an early-warning rule used in the early-warning identification process is a global standard early-warning rule or a customized early-warning rule based on the original data;
if the pre-warning rule is the customized pre-warning rule, executing step S3;
if the global standardized early warning rule is adopted, executing step S2;
step S2: setting a global standardized early warning rule;
preferably, the setting of the global standardized early warning rule comprises setting rule contents, upper and lower limit values, early warning recovery conditions and early warning recovery coefficients;
the early warning judgment comprises the judgment of rule content, upper and lower limit values, early warning recovery conditions, early warning recovery coefficients, duration and early warning levels.
In this embodiment, as shown in the global standardized early warning rule table, taking early warning of the power distribution network as an example, setting a global standardized early warning rule, including setting an early warning rule name, early warning rule contents, an upper limit value, a lower limit value, a recovery coefficient, a recovery condition, and the like, when setting, if the upper and lower limit values are empty, early warning is performed according to the early warning rule contents, otherwise, the early warning is performed according to the upper and lower limit values;
global standardized early warning rule table:
step S3: performing pre-operation early warning based on the selected early warning rule, generating early warning records, and not reporting early warning information;
step S4: operating early warning judgment according to the early warning record, generating an early warning judgment operation result, triggering an early warning self-correcting mechanism to judge whether the early warning rule has a problem, and if so, suspending the early warning function of the early warning rule;
preferably, the method for judging whether the early warning rule has a problem by the early warning self-correcting mechanism comprises the following steps:
judging whether the current early warning rule exceeds an error correction coefficient, if so, judging that the current early warning rule has a problem;
and suspending the early warning function of the early warning rule with the problem and repairing the early warning function.
Step S5: and judging whether the test run early warning is triggered according to the early warning judgment operation result, if so, judging that the test run early warning is abnormal, otherwise, judging that the test run early warning is abnormal, and repairing the test run early warning.
Preferably, when the pre-warning of the test run is not triggered, whether the abnormality of the pre-warning is determined to be processed within a preset time is judged, and if the abnormality of the pre-warning is determined, the pre-warning is repaired; if not, an abnormal alarm is sent out.
In the embodiment, it is very necessary to identify and process the possible abnormality of the early warning system, so that the problem that the early warning system may have the abnormal early warning process is placed before the step of identifying the equipment or the environment by the early warning system, so as to ensure that the equipment or the environment cannot be deviated during early warning identification; specifically, the early warning identification is carried out based on two early warning rules, namely a global standardized early warning rule and a customized early warning rule, the global standardized early warning rule is a self-contained default early warning rule of an early warning system, and the customized early warning rule is a customer-defined early warning rule, so that judgment is needed firstly;
further, the pre-run warning generates a warning record, but does not send warning information to the outside, it should be noted that the pre-run warning needs to be continued for a period of time, such as 2 hours of pre-run warning shown in fig. 1, so as to ensure the effectiveness of the pre-run warning, the pre-run warning can generate a warning judgment operation result, whether the pre-run warning is triggered or not can be judged based on the result, if the pre-run warning is triggered, if the early warning of the early warning system is not abnormal, the early warning of the early warning system is abnormal, and if the early warning of the early warning system is abnormal, waiting for confirmation within a period of time, such as 2 hours as shown in fig. 1, this time confirmation being a manual confirmation, confirming whether to proceed with the process, if so, if not, the abnormal condition is repaired, the early warning of the trial operation is abnormal but can not be known, and a corresponding notification mode is selected to send out the repairing strategy;
further, when the pre-run pre-warning is carried out, a pre-warning self-correction mechanism is triggered, wherein the pre-warning self-correction mechanism can be understood as that if an early-warning rule is generated in an over-constant amount (error correction coefficient), the generation of the rule is firstly suspended, then a relevant person in charge is informed to carry out processing, after the relevant person in charge processes the rule, no problem is confirmed, and then the rule pre-warning work is resumed; the error correction coefficient can be set to alarm at the same time for more than 30% of users according to an early warning rule in fig. 1 or generate 50 early warning messages within 15 minutes.
The error correction coefficient setting mode has two types:
1. user-defined settings (including two ways): a. specific numerical values can be filled in; b. filling error correction content;
2. the system performs machine learning and artificial intelligence automatic judgment according to the effect and recovery of multiple previous early warnings of the monitoring point or similar monitoring points;
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 method is characterized in that: performing early warning exception handling before early warning identification;
the early warning identification 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;
triggering an early warning recovery mechanism, removing the abnormity and generating an early warning recovery log;
the early warning recovery mechanism comprises:
and when the collected binary data is identified to be restored to the normal range, executing restoration judgment operation according to the restoration coefficient and the restoration condition, wherein the restoration judgment operation comprises judging whether the binary data is just restored to the critical value touching the normal range, if so, judging the restoration to be abnormal restoration, and if not, judging the restoration to be normal restoration.
2. The warning method as claimed in claim 1, wherein:
the early warning recovery mechanism specifically comprises:
if the early warning is the upper limit early warning, the recovery judging operation comprises the step of judging whether the detection value is reduced to a set low-point value in a normal range;
if the early warning is the lower limit early warning, the recovery judgment operation comprises the step of judging whether the detection value rises to a set high-point value in a normal range;
the low point value and the high point value do not belong to the critical value of the normal range.
3. The warning method as claimed in 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.
4. The warning method as claimed in 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.
5. The warning method as claimed in 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.
6. The warning method as claimed in 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.
7. The warning method as claimed in claim 1, wherein:
the early warning logs comprise logs recording all full early warning states after early warning takes effect;
the early warning recovery log comprises a log recording recovery time.
8. The warning method as claimed in claim 1, wherein:
the early warning exception handling method comprises the following steps:
step S1: acquiring original data of an environment or equipment to be early-warned, and judging whether an early-warning rule used in the early-warning identification process is a global standard early-warning rule or a customized early-warning rule based on the original data;
if the pre-warning rule is the customized pre-warning rule, executing step S3;
if the global standardized early warning rule is adopted, executing step S2;
step S2: setting a global standardized early warning rule;
step S3: performing pre-operation early warning based on the selected early warning rule, generating early warning records, and not reporting early warning information;
step S4: operating early warning judgment according to the early warning record, generating an early warning judgment operation result, triggering an early warning self-correcting mechanism to judge whether the early warning rule has a problem, and if so, suspending the early warning function of the early warning rule;
step S5: and judging whether the test run early warning is triggered according to the early warning judgment operation result, if so, judging that the test run early warning is abnormal, otherwise, judging that the test run early warning is abnormal, and repairing the test run early warning.
9. The warning method as claimed in claim 7, wherein:
the method for judging whether the early warning rule has a problem by the early warning self-correcting mechanism comprises the following steps:
judging whether the current early warning rule exceeds an error correction coefficient, if so, judging that the current early warning rule has a problem;
and suspending the early warning function of the early warning rule with the problem and repairing the early warning function.
10. The warning method as claimed in claim 7, wherein:
when the pre-warning of the trial operation is not triggered, judging whether to confirm that the pre-warning is abnormal within a preset time, and if so, repairing the pre-warning; if not, an abnormal alarm is sent out.
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