CN116704735A - Hydropower station intelligent alarm method, system, terminal and storage medium - Google Patents
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Abstract
The application relates to the technical field of computer monitoring of hydropower stations, in particular to an intelligent hydropower station alarming method, a system, a terminal and a storage medium, wherein the method comprises the following steps: acquiring historical data; acquiring an operation condition based on the historical data; acquiring the data distribution condition of historical data based on the operation condition and the preset dimension; generating data models under different operation conditions based on the data distribution conditions; acquiring real-time data and a target working condition to which the real-time data belongs; based on the target working condition, selecting a data model matched with the target working condition, and judging whether the real-time data meets a preset standard or not; and if the real-time data does not meet the preset standard, alarming. The application is beneficial to improving the accuracy of the alarm system.
Description
Technical Field
The application relates to the technical field of computer monitoring of hydropower stations, in particular to an intelligent hydropower station alarming method, system, terminal and storage medium.
Background
The hydropower station is a comprehensive engineering facility for converting water energy into electric energy, is a place for generating electricity by utilizing water energy resources, and is a complex of water, machinery and electricity. Hydropower stations generally comprise reservoirs formed by water retaining and draining buildings, hydropower station diversion systems, power generation plants, electromechanical equipment and the like. The high-water level water of the reservoir flows into a factory building through a water diversion system to push a water-turbine generator set to generate electric energy, and then is input into a power grid through a step-up transformer, a switching station and a power transmission line.
With the development of electric power industry, hydroelectric power generation is also increasingly important, in order to maintain the safe and healthy work of a hydropower station, local data are required to be monitored and analyzed, the local data are acquired, calculated and synthesized in the related art, and an alarm is generated by configuring an analog quantity out-of-limit and a switching value, and out-of-limit values in the related art are generally manually set by staff according to the existing fixed standard or experience and the like, however, even if corresponding standard or experience values exist, the corresponding standard or experience values often do not meet the actual conditions due to the fact that the field conditions are quite different, so that the accuracy of an alarm system is not high.
Disclosure of Invention
In order to help to improve the accuracy of an alarm system, the application provides an intelligent hydropower station alarm method, an intelligent hydropower station alarm system, an intelligent hydropower station terminal and a storage medium.
In a first aspect, the intelligent hydropower station alarming method provided by the application adopts the following technical scheme:
an intelligent alarming method for a hydropower station comprises the following steps:
acquiring historical data;
acquiring an operation condition based on the historical data;
acquiring the data distribution condition of the historical data based on the operation condition and a preset dimension;
generating data models under different operation conditions based on the data distribution conditions;
acquiring real-time data and a target working condition to which the real-time data belong;
based on the target working condition, selecting the data model matched with the target working condition, and judging whether the real-time data meets a preset standard or not;
if the real-time data does not meet the preset standard, alarming is carried out;
the data model is an algorithm model under each operation condition and each monitoring dimension.
By adopting the technical scheme, the historical data of the normal operation of the hydropower station is firstly obtained, the historical data is analyzed, the operation working conditions are obtained from the historical data, then the data distribution conditions of the historical data in each dimension under each working condition are analyzed and calculated according to the preset dimension, and the data models under different operation working conditions are generated according to the data distribution conditions; acquiring real-time data and a target working condition to which the real-time data belong, finally selecting a data model corresponding to the target working condition to judge the real-time data, and giving an alarm if the real-time data does not meet a preset standard, namely the real-time data is abnormal; the data models with different data dimensionalities under different operation conditions are set through the historical data, then the corresponding data model is selected according to the target condition to which the real-time data belongs to judge whether the real-time data is abnormal or not, the actual condition of data monitoring is more met, the limit value is not required to be set manually according to the fixed standard and experience, and the accuracy of an alarm system is improved.
Optionally, the specific step of acquiring the operation condition based on the historical data includes:
acquiring working condition parameters corresponding to the working condition fields based on the historical data;
and acquiring the operation working condition based on the working condition parameters.
By adopting the technical scheme, working condition parameters are obtained according to the historical data, and the operation working conditions are set according to the working condition parameters; according to different operation conditions, corresponding working condition parameters are set, whether current data are abnormal or not can be judged according to different operation conditions, the actual condition of data monitoring is more met, the limit value is not required to be set manually according to fixed standards and experience, and accuracy of an alarm system can be improved.
Optionally, the specific step of obtaining the data distribution condition of the historical data based on the operation condition and the preset dimension includes:
acquiring data types of the historical data based on the operation conditions and the preset dimension, wherein the data types comprise transient data and steady-state data;
if the data type is the transient data, deleting the transient data;
and if the data type is the steady-state data, acquiring the data distribution condition of the historical data.
By adopting the technical scheme, the data type of the historical data is obtained, if the data type is transient data, the transient data is filtered, and if the data type is steady-state data, the data distribution state of the historical data under different working conditions is obtained; the transient data has larger fluctuation, so that the transient data is removed, the steady state data is reserved, and the data distribution condition of the steady state data under different working conditions is acquired, thereby being beneficial to improving the accuracy of the data and the accuracy of an alarm system.
Optionally, the method further comprises:
acquiring a history alarm record;
based on the historical alarm record, acquiring the alarm number corresponding to the target working condition as a first alarm number;
analyzing the first alarm quantity and generating an analysis result;
judging whether the analysis result meets a preset early warning requirement or not;
and if yes, early warning is carried out based on the analysis result.
By adopting the technical scheme, the historical alarm records are firstly obtained, then the first alarm quantity corresponding to the target working condition is obtained according to the historical alarm records, the first alarm quantity is analyzed, an analysis result is generated, whether the analysis result meets the preset early warning requirement is judged, if yes, the first alarm quantity is more and exceeds the normal quantity range set by the target working condition, and therefore early warning is carried out according to the analysis result; the first alarm quantity of target working conditions is known according to the historical alarm records, when the analysis result meets the preset early warning requirement, and when the target working conditions are reused, early warning can be carried out to remind workers that the abnormal conditions occur under the target working conditions are more, so that the workers can pay attention to early and timely make corresponding treatment, and the safety of the hydropower station is improved.
Optionally, the analysis result includes a first analysis result and a second analysis result; the specific steps of analyzing the first alarm quantity and generating an analysis result comprise:
if the first alarm number exceeds a preset first number threshold, generating the first analysis result based on the target working condition and the first alarm number;
if the first alarm number exceeds a preset second number threshold and does not exceed the preset first number threshold, acquiring an alarm type corresponding to a target working condition and target numbers corresponding to different alarm types based on the historical alarm record;
judging whether the target number exceeds a target number threshold corresponding to the alarm type;
and if the target number exceeds the target number threshold, generating a second analysis result based on the target working condition, the target number and the alarm type.
By adopting the technical scheme, if the first alarm number exceeds a preset first number threshold value, the first alarm number is indicated to be beyond the normal alarm number range under the target working condition, so that a first analysis result is generated according to the target working condition and the first alarm number, if the first alarm number exceeds a preset second number threshold value and does not exceed the preset first number threshold value, the first alarm number is indicated to be beyond the normal alarm number range under the target working condition, and if the first alarm number is not beyond the preset second number threshold value, the first analysis result is indicated to be about to exceed the normal alarm number range under the target working condition, so that whether the data abnormal risk exists under the target working condition, the target number corresponding to different alarm types and different alarm types under the target working condition are obtained according to the historical alarm record, if the target number exceeds the target number threshold value, the total alarm number under the target working condition is indicated to not to be beyond the normal alarm number range, but the target number corresponding to a certain alarm type is beyond the range of the normal alarm number of the type, and the second analysis result is generated according to the target working condition, the target number and the alarm type;
setting double judgment conditions, and when the total number of historical alarms corresponding to target working conditions, namely the first alarm number, exceeds a preset first threshold value or when the first alarm number is between the preset first threshold value and a preset second threshold value, but when the target number corresponding to the alarm type exceeds the target number threshold value, early warning is carried out, so that staff can pay attention to potential risks early, corresponding treatment is timely carried out, and further safety of the hydropower station is improved.
Optionally, the analysis result includes a third analysis result; after the history alarm record is obtained, the method further comprises the following steps:
acquiring alarm time based on the historical alarm record;
acquiring the alarm number corresponding to a target time node as a second alarm number;
judging whether the second alarm number exceeds a preset third number threshold value or not;
if the second alarm number exceeds the preset third number threshold, marking the target time node as an abnormal multi-time node;
acquiring a current time node, and judging whether the current time node is matched with the abnormal multiple time node or not based on a preset matching rule;
and if the first alarm number is matched with the second alarm number, generating a third analysis result based on the second alarm number.
By adopting the technical scheme, the second alarm number corresponding to the target time node is obtained, whether the second alarm number exceeds a preset third number threshold value is judged, if so, the situation that the alarm number in the renamed bar time node is more and exceeds the normal alarm number range is indicated, therefore, the target time node is marked as an abnormal multiple time node, the current time node is obtained, whether the current time node is matched with the abnormal multiple time node is judged according to a preset matching rule, if so, the situation that more abnormal data possibly occur in the current time node is indicated, and a third analysis result is generated according to the second alarm number; if the current time node is matched with the abnormal multiple time node, early warning is carried out, so that staff can pay attention to potential risks early, corresponding treatment is timely carried out, and further safety of the hydropower station is improved.
Optionally, if the first alarm number matches, the specific step of generating the third analysis result based on the second alarm number includes:
if the node types are matched, the alarm type corresponding to the target time node is obtained and used as the node alarm type;
and generating a third analysis result based on the node alarm type and the second alarm number.
Through adopting above-mentioned technical scheme, if the phase-match, indicate that more abnormal data can appear in the current time node, consequently acquire node alarm type to according to node alarm type and the generation of second alarm quantity third analysis result, increase node alarm type again on the basis of second alarm quantity, what kind of alarm type corresponds in unusual multiple time node is more to the understanding that help the staff is more clear, thereby help the staff to know the potential risk place more accurately, consequently can in time make corresponding processing, and then help improving the security of hydroelectric power station.
In a second aspect, the application also discloses an intelligent alarm system of the hydropower station, which adopts the following technical scheme:
an intelligent warning system for a hydropower station, comprising:
the first acquisition module is used for acquiring historical data;
the second acquisition module is used for acquiring the operation working condition based on the historical data;
the third acquisition module is used for acquiring the data distribution condition of the historical data based on the operation condition and a preset dimension;
the generation module is used for generating data models under different operation conditions based on the data distribution conditions;
the fourth acquisition module is used for acquiring real-time data and target working conditions to which the real-time data belong;
the judging module is used for selecting the data model matched with the target working condition based on the target working condition and judging whether the real-time data meets a preset standard or not;
the alarm module is used for alarming if the real-time data does not meet the preset standard;
the data model is an algorithm model under each operation condition and each monitoring dimension.
By adopting the technical scheme, the historical data of the normal operation of the hydropower station is firstly obtained, the historical data is analyzed, the operation working conditions are obtained from the historical data, then the data distribution conditions of the historical data in each dimension under each working condition are analyzed and calculated according to the preset dimension, and the data models under different operation working conditions are generated according to the data distribution conditions; acquiring real-time data and a target working condition to which the real-time data belong, finally selecting a data model corresponding to the target working condition to judge the real-time data, and giving an alarm if the real-time data does not meet a preset standard, namely the real-time data is abnormal; the data models with different data dimensionalities under different operation conditions are set through the historical data, then the corresponding data model is selected according to the target condition to which the real-time data belongs to judge whether the real-time data is abnormal or not, the actual condition of data monitoring is more met, the limit value is not required to be set manually according to the fixed standard and experience, and the accuracy of an alarm system is improved.
In a third aspect, the present application provides an intelligent terminal, which adopts the following technical scheme:
an intelligent terminal comprising a memory, a processor, wherein the memory is configured to store a computer program capable of running on the processor, and the processor, when loaded with the computer program, performs the method of the first aspect.
By adopting the technical scheme, the computer program is generated based on the method of the first aspect and is stored in the memory to be loaded and executed by the processor, so that the intelligent terminal is manufactured according to the memory and the processor, and the intelligent terminal is convenient for a user to use.
In a fourth aspect, the present application provides a computer readable storage medium, which adopts the following technical scheme:
a computer readable storage medium having stored therein a computer program which, when loaded by a processor, performs the method of the first aspect.
By adopting the technical scheme, the method based on the first aspect generates the computer program, and stores the computer program in the computer readable storage medium to be loaded and executed by the processor, and the computer program is convenient to read and store through the computer readable storage medium.
In summary, the application has the following beneficial technical effects:
the data models with different data dimensionalities under different operation conditions are set through the historical data, then the corresponding data model is selected according to the target condition to which the real-time data belongs to judge whether the real-time data is abnormal or not, the actual condition of data monitoring is more met, the limit value is not required to be set manually according to the fixed standard and experience, and the accuracy of an alarm system is improved.
Drawings
FIG. 1 is a main flow chart of an intelligent alarming method of a hydropower station according to an embodiment of the application;
fig. 2 is a step flowchart of steps S201 to S202;
fig. 3 is a step flowchart of steps S301 to S303;
fig. 4 is a step flowchart of steps S401 to S405;
fig. 5 is a step flowchart of steps S501 to S504;
fig. 6 is a step flowchart of steps S601 to S606;
fig. 7 is a step flowchart of steps S701 to S702;
FIG. 8 is a block diagram of a hydropower station intelligent alarm system according to an embodiment of the application.
Reference numerals illustrate:
1. a first acquisition module; 2. a second acquisition module; 3. a third acquisition module; 4. a generating module; 5. a fourth acquisition module; 6. a judging module; 7. and an alarm module.
Detailed Description
In a first aspect, the application discloses an intelligent hydropower station alarming method.
Referring to fig. 1, an intelligent alarming method for a hydropower station includes steps S101 to S107:
step S101: historical data is obtained.
Specifically, the history data is related data when the device operates normally in the specified period of time, and in this embodiment, the history data may be one year or all data from the beginning of the device to date.
In this embodiment, the historical data includes water level, vane opening, active power, reactive power, generator outlet circuit breaker, excitation voltage, excitation current, power factor, unit frequency, unit rotational speed, a-phase current, B-phase current, C-phase current, unit voltage Uab, unit voltage Uac, unit voltage Ubc, water guide bush temperature, thrust bush temperature, stator core temperature, stator coil temperature, upper guide bush temperature, lower guide bush temperature, governor oil level, vibration ferry data, and the like. The historical data is stored in a database, which may be a relational database, a time series database, noSQL, newSQL or other data access container.
Step S102: based on the historical data, an operating condition is obtained.
Specifically, in this embodiment, the operation condition is the operation condition of the device during operation.
Step S103: and acquiring the data distribution condition of the historical data based on the operation condition and the preset dimension.
Specifically, the preset dimension and the preset monitoring dimension, in this embodiment, the monitoring dimension includes, but is not limited to, a univariate dimension, a univariate time dimension, a same-dimension multivariate, a multivariate related dimension, and an abnormal algorithm corresponding to the change dimension, and different processing modes corresponding to different monitoring dimensions are also different; the data distribution status is the distribution status of the historical data under different operation conditions.
The single variable dimension is that only selected single variables are processed, such as the rotating speed of a unit, and a statistical test 3-Sigma method is used in the embodiment; the single variable time dimension is that the selected single variable plus the time dimension are processed together, in which case a list of data is automatically added to represent the time of operation under the operating condition, and in this embodiment, the ARIMA algorithm is used; the same-dimension multivariable means different measurement points of the same dimension, such as a unit voltage Uab, a unit voltage Uac and a unit voltage Ubc, and in this case, the mean value and the variance are automatically generated, and in this embodiment, a neural network self-coding algorithm is used; the multivariate relevant dimension is to process a plurality of selected variables, such as vibration ferry data, in this embodiment, a neural network self-coding algorithm is adopted, and other algorithms such as regression and the like and local outlier factor detection methods can also be selected.
Step S104: based on the data distribution conditions, data models under different operating conditions are generated.
Specifically, in this embodiment, the data model is an algorithm model of each monitoring dimension under each working condition.
Step S105: and acquiring the real-time data and the target working condition to which the real-time data belongs.
Specifically, in this embodiment, the real-time data, that is, the latest data collected currently, may obtain the real-time data, the target working condition, and the operation working condition corresponding to the real-time data through an API or other modes.
Step S106: and selecting a data model matched with the target working condition based on the target working condition, and judging whether the real-time data meets the preset standard.
Specifically, the preset standard is a preset judgment standard for judging whether the real-time data has an abnormality, and in this embodiment, a data model matched with the target working condition is selected, that is, a data model with the same working condition as the target working condition is selected.
Step S107: and if the real-time data does not meet the preset standard, alarming.
Specifically, in this embodiment, if the real-time data does not meet the preset standard, it indicates that the real-time data is abnormal, so that an alarm is given.
According to the intelligent hydropower station alarming method provided by the embodiment, firstly, historical data of normal operation of a hydropower station is obtained, the historical data is analyzed, operation conditions are obtained from the historical data, then, according to preset dimensions, the data distribution conditions of the historical data in each dimension under each condition are analyzed and calculated, and according to the data distribution conditions, data models under different operation conditions are generated; acquiring real-time data and a target working condition to which the real-time data belong, finally selecting a data model corresponding to the target working condition to judge the real-time data, and giving an alarm if the real-time data does not meet a preset standard, namely the real-time data is abnormal; the data models with different data dimensionalities under different operation conditions are set through the historical data, then the corresponding data model is selected according to the target condition to which the real-time data belongs to judge whether the real-time data is abnormal or not, the actual condition of data monitoring is more met, the limit value is not required to be set manually according to the fixed standard and experience, and the accuracy of an alarm system is improved.
Referring to fig. 2, in one implementation manner of the present embodiment, step S102 includes steps S201 to S202 of acquiring an operation condition based on the history data:
step S201: based on the historical data, working condition parameters corresponding to the working condition fields are obtained.
Specifically, the working condition parameters include a working condition field, a steady state minimum duration time and a working condition interval number, where the working condition field is a field for representing an operation working condition, such as a water level, a guide vane opening, active power, reactive power, and the like, and in this embodiment, the hydropower station selects the water level, the guide vane opening, and the active power as working condition fields; the history data includes a steady state and a tentative state, and the steady state minimum duration is a criterion for judging that the history data is a distinction between the history data and the steady state or the tentative state, and in this embodiment, the steady state minimum duration may be set to 5 minutes; the number of operating mode intervals may be 10.
Step S202: and acquiring an operation condition based on the condition parameter.
Specifically, in this embodiment, according to the working condition parameters, it may be obtained: water level = (maximum water level-minimum water level)/10, = (maximum vane opening-minimum vane opening)/10, = (maximum active power-minimum active power)/10, whereby 1000 (10 x 10) different operating conditions can be obtained; wherein, condition 1 is ((minimum water level, minimum water level +, (minimum vane opening, minimum vane opening +, (minimum active power +, (minimum water level, minimum water level +2 x) water level), (minimum vane opening, minimum vane opening +, (minimum active power + active power)), condition 1000 is ((minimum water level, minimum water level +10 x) water level), (minimum vane opening, minimum vane opening +10 x) vane opening, (minimum active power +10 x) active power)), condition 2 is ((minimum water level, minimum vane opening +2 x) water level, (minimum active power +10 x) active power).
According to the intelligent alarming method for the hydropower station, working condition parameters are obtained according to historical data, and operation working conditions are set according to the working condition parameters; according to different operation conditions, corresponding working condition parameters are set, whether current data are abnormal or not can be judged according to different operation conditions, the actual condition of data monitoring is more met, the limit value is not required to be set manually according to fixed standards and experience, and accuracy of an alarm system can be improved.
Referring to fig. 3, in one implementation manner of the present embodiment, step S103 includes the specific steps of obtaining the data distribution status of the history data based on the operation condition and the preset dimension, including steps S301 to S303:
step S301: based on the operation condition and the preset dimension, acquiring the data type of the historical data, wherein the data type comprises transient data and steady-state data.
Specifically, in this embodiment, the data type is the type of the history data, and in this embodiment, the transient data is the data in the tentative state, and the steady data is the data in the steady state.
Step S302: and if the data type is transient data, deleting the transient data.
Specifically, in this embodiment, the transient data has a large fluctuation, and therefore, in this embodiment, the transient data is deleted.
Step S303: and if the data type is steady-state data, acquiring the data distribution condition of the historical data.
Specifically, in this embodiment, if the data type is steady-state data, the steady-state data of the same operation condition are stored together.
According to the hydropower station intelligent alarm method provided by the embodiment, the data type of the historical data is obtained, if the data type is transient data, the transient data is filtered, and if the data type is steady-state data, the data distribution state of the historical data under different working conditions is obtained; the transient data has larger fluctuation, so that the transient data is removed, the steady state data is reserved, and the data distribution condition of the steady state data under different working conditions is acquired, thereby being beneficial to improving the accuracy of the data and the accuracy of an alarm system.
Referring to fig. 4, in one implementation manner of the present embodiment, step S401 to step S405 are further included:
step S401: and acquiring a historical alarm record.
Specifically, in this embodiment, the history alarm record is an alarm record in a specified period, and in this embodiment, the history alarm record may be one year or all alarm records from the beginning of the equipment to the date.
Step S402: based on the historical alarm record, the alarm number corresponding to the target working condition is obtained and used as the first alarm number.
Specifically, in this embodiment, the first alarm number is the sum of the alarm numbers corresponding to the target working condition, for example, the sum of the alarm numbers corresponding to the working condition 1 is 100 times, and then the first alarm number is 100.
Step S403: and analyzing the first alarm quantity and generating an analysis result.
Specifically, in this embodiment, the first alarm number is compared with a preset number threshold to generate a comparison result, and an analysis result is generated according to the comparison result and related data, where in this embodiment, the preset number threshold is a preset judgment standard for judging whether the first alarm number is an alarm number in a normal range corresponding to a target working condition, and the judgment standard includes a preset first number threshold, a preset second number threshold and a preset third early warning requirement; the analysis results include a first analysis result, a second analysis result, and a third analysis result.
Step S404: judging whether the analysis result meets the preset early warning requirement.
Specifically, the preset early warning requirement is preset, and is used for judging whether the analysis result meets the judgment standard of the early warning condition, in this embodiment, the first analysis result, the second analysis result and the third analysis result all meet the preset early warning requirement.
Step S405: and if yes, early warning is carried out based on the analysis result.
Specifically, in this embodiment, if the analysis result meets the early warning condition, the early warning is performed according to the analysis result.
According to the intelligent alarming method for the hydropower station, the historical alarming records are acquired firstly, then the first alarming quantity corresponding to the target working condition is acquired according to the historical alarming records, the first alarming quantity is analyzed, an analysis result is generated, whether the analysis result meets the preset early-warning requirement is judged, if yes, the fact that the first alarming quantity is more than the normal quantity range set by the target working condition is indicated, and therefore early warning is conducted according to the analysis result; the first alarm quantity of target working conditions is known according to the historical alarm records, when the analysis result meets the preset early warning requirement, and when the target working conditions are reused, early warning can be carried out to remind workers that the abnormal conditions occur under the target working conditions are more, so that the workers can pay attention to early and timely make corresponding treatment, and the safety of the hydropower station is improved.
Referring to fig. 5, in one implementation manner of the present embodiment, step S403 includes steps S501 to S504 of analyzing the first alarm number and generating an analysis result:
step S501: and if the first alarm number exceeds a preset first number threshold, generating a first analysis result based on the target working condition and the first alarm number.
Specifically, the first analysis result includes the target working condition and the first alarm number, so that the target working condition and the first alarm number can be clearly known from the first analysis result, and in this embodiment, the preset first number threshold may be 20 times or other values.
Step S502: if the first alarm number exceeds the preset second number threshold and does not exceed the preset first number threshold, acquiring the alarm type corresponding to the target working condition and the target number corresponding to different alarm types based on the historical alarm record.
Specifically, the alarm type is the abnormal data type, and the alarm type, such as too low water level or insufficient opening of the guide vane, can be automatically reported at the same time when the alarm is performed; the target number is the number of alarms corresponding to different alarm types under the target working condition, for example, the first number of alarms corresponding to the working condition 1 is 20 times, and the number of alarms caused by insufficient opening of the guide vane is 10 times, and the target number corresponding to insufficient opening of the guide vane is 10 times under the working condition 1; in addition, in this embodiment, the preset second number threshold may be set to 10 times or other values.
Step S503: and judging whether the target number exceeds a target number threshold corresponding to the alarm type.
Specifically, the target number threshold and the judgment standard for judging whether the target number exceeds the normal alarm number range under a certain operation condition may be set to 5 times or other values in this embodiment.
Step S504: and if the target number exceeds the target number threshold, generating a second analysis result based on the target working condition, the target number and the alarm type.
Specifically, the second analysis result is an analysis result generated according to the target number and the alarm type.
According to the intelligent hydropower station alarming method provided by the embodiment, if the first alarming quantity exceeds the preset first quantity threshold value, the first alarming quantity is indicated to be beyond the normal alarming quantity range under the target working condition, so that a first analysis result is generated according to the target working condition and the first alarming quantity, if the first alarming quantity exceeds the preset second quantity threshold value and does not exceed the preset first quantity threshold value, the first alarming quantity is indicated to be beyond the normal alarming quantity range under the target working condition, if so, the first alarming quantity is not beyond the normal alarming quantity range under the target working condition, and in order to further judge whether the abnormal data risk exists under the target working condition, the target quantity corresponding to different alarming types and different alarming types under the target working condition is obtained according to the historical alarming record, if the target quantity exceeds the target quantity threshold value, the total alarming quantity under the target working condition is indicated to not exceed the normal alarming quantity range, but the target quantity corresponding to a certain alarming type is beyond the normal alarming quantity range of the type, and the abnormal data risk exists, and therefore, the second analysis result is generated according to the target working condition, the target quantity and the alarming type.
Setting double judgment conditions, and when the total number of historical alarms corresponding to target working conditions, namely the first alarm number, exceeds a preset first threshold value or when the first alarm number is between the preset first threshold value and a preset second threshold value, but when the target number corresponding to the alarm type exceeds the target number threshold value, early warning is carried out, so that staff can pay attention to potential risks early, corresponding treatment is timely carried out, and further safety of the hydropower station is improved.
Referring to fig. 6, in one implementation manner of the present embodiment, after step S401, step S601 to step S606 are further included:
step S601: based on the historical alarm record, the alarm time is obtained.
Specifically, the alarm time is the time when the system alarms when the data is abnormal, and in this embodiment, the alarm time is automatically saved when the system alarms.
Step S602: and acquiring the target time node and the alarm number corresponding to the target time node as a second alarm number.
Specifically, in this embodiment, the target event node is a time interval designated by the user, for example, 22 to 24 hours per day, for example, 7 months 1 to 8 months 1 each year, and the second alarm number is the total number of alarms in the target time node, for example, 10 total alarms in the target time node, and the second alarm number is 10 total alarms.
Step S603: judging whether the second alarm number exceeds a preset third number threshold.
Specifically, in this embodiment, the preset third number threshold may be set to a fixed value, for example, 10 times, or may be set according to the time length of the target event node, for example, the preset third number threshold is set to 200% or other percentages of the days included in the target time node, that is, when the target time node is 7 months 1 to 8 months 1, for 31 days, the preset third number threshold is (31×200%) 62 times.
Step S604: and if the second alarm number exceeds a preset third number threshold, marking the target time node as an abnormal multi-time node.
Specifically, in this embodiment, the abnormal multiple time node is a target time node with a large number of abnormal times of data occurrence.
Step S605: and acquiring the current time node, and judging whether the current time node is matched with the abnormal multiple time node or not based on a preset matching rule.
Specifically, the current time node, i.e. the current time, in this embodiment, the current time node may be a certain time point, for example, 7 hours and 0 minutes, or may be a certain time interval, for example, a certain day or a certain month.
In this embodiment, if the current time node is a specific time node from 7 months 1 to 8 months 1 and the current time node is a specific time node from 7 months 1 to 8 months 1, then whether the current time node matches the specific time node is determined.
Step S606: and if the first alarm number is matched with the second alarm number, generating a third analysis result based on the second alarm number.
Specifically, the third analysis result is an analysis result generated according to the second alarm number.
According to the hydropower station intelligent alarm method provided by the embodiment, the second alarm quantity corresponding to the target time node is obtained, whether the second alarm quantity exceeds a preset third quantity threshold value is judged, if yes, the situation that the alarm quantity in the rename bar time node is more and exceeds the normal alarm quantity range is indicated, therefore, the target time node is marked as an abnormal multiple time node, the current time node is obtained, whether the current time node is matched with the abnormal multiple time node is judged according to a preset matching rule, if yes, the situation that more abnormal data possibly occur in the current time node is indicated, and therefore a third analysis result is generated according to the second alarm quantity; if the current time node is matched with the abnormal multiple time node, early warning is carried out, so that staff can pay attention to potential risks early, corresponding treatment is timely carried out, and further safety of the hydropower station is improved.
Referring to fig. 7, in one implementation of the present embodiment, step S606: if the first alarm number matches, the specific step of generating the third analysis result based on the second alarm number includes steps S701 to S702:
step S701: if the node alarm types are matched, the alarm type corresponding to the target time node is obtained and used as the node alarm type.
Specifically, in this embodiment, the node alarm type is the alarm type of the alarm corresponding to the target time node.
Step S702: and generating a third analysis result based on the node alarm type and the second alarm number.
According to the intelligent hydropower station alarm method, if the intelligent hydropower station alarm method is matched with the intelligent hydropower station alarm device, more abnormal data possibly appear in the current time node, so that node alarm types are obtained, a third analysis result is generated according to the node alarm types and the second alarm quantity, the node alarm types are increased on the basis of the second alarm quantity, workers can know more clearly which alarm types correspond to more types in the abnormal multi-time node, workers can know the potential risk more accurately, corresponding processing can be timely performed, and safety of the hydropower station can be improved.
In a second aspect, the application also discloses an intelligent alarm system for the hydropower station.
Referring to fig. 8, a hydropower station intelligent warning system, comprising:
a first acquisition module 1 for acquiring history data;
the second acquisition module 2 is used for acquiring the operation working condition based on the historical data;
the third acquisition module 3 is used for acquiring the data distribution condition of the historical data based on the operation condition and the preset dimension;
the generation module 4 is used for generating data models under different operation conditions based on the data distribution conditions;
the fourth acquisition module 5 is used for acquiring the real-time data and the target working condition to which the real-time data belongs;
the judging module 6 is used for selecting a data model matched with the target working condition based on the target working condition and judging whether the real-time data meets the preset standard or not;
the alarm module 7 is used for giving an alarm if the real-time data does not meet the preset standard;
the data model is an algorithm model under each operation condition and each monitoring dimension.
In a third aspect, an embodiment of the present application discloses an intelligent terminal, including a memory, and a processor, where the memory is configured to store a computer program capable of running on the processor, and when the processor loads the computer program, the processor executes an intelligent alarming method for a hydropower station according to the foregoing embodiment.
In a fourth aspect, an embodiment of the present application discloses a computer readable storage medium, and a computer program is stored in the computer readable storage medium, where the computer program, when loaded by a processor, executes a hydropower station intelligent alarm method of the above embodiment.
The above embodiments are not intended to limit the scope of the present application, so: all equivalent changes in structure, shape and principle of the application should be covered in the scope of protection of the application.
Claims (10)
1. An intelligent alarming method for a hydropower station is characterized by comprising the following steps:
acquiring historical data;
acquiring an operation condition based on the historical data;
acquiring the data distribution condition of the historical data based on the operation condition and a preset dimension;
generating data models under different operation conditions based on the data distribution conditions;
acquiring real-time data and a target working condition to which the real-time data belong;
based on the target working condition, selecting the data model matched with the target working condition, and judging whether the real-time data meets a preset standard or not;
if the real-time data does not meet the preset standard, alarming is carried out;
the data model is an algorithm model under each operation condition and each monitoring dimension.
2. The intelligent hydropower station alarm method according to claim 1, wherein the specific step of acquiring the operation condition based on the history data comprises:
acquiring working condition parameters corresponding to the working condition fields based on the historical data;
and acquiring the operation working condition based on the working condition parameters.
3. The intelligent hydropower station alarm method according to claim 2, wherein the specific step of obtaining the data distribution condition of the historical data based on the operation condition and the preset dimension includes:
acquiring data types of the historical data based on the operation conditions and the preset dimension, wherein the data types comprise transient data and steady-state data;
if the data type is the transient data, deleting the transient data;
and if the data type is the steady-state data, acquiring the data distribution condition of the historical data.
4. The intelligent hydropower station alarm method according to claim 1, further comprising:
acquiring a history alarm record;
based on the historical alarm record, acquiring the alarm number corresponding to the target working condition as a first alarm number;
analyzing the first alarm quantity and generating an analysis result;
judging whether the analysis result meets a preset early warning requirement or not;
and if yes, early warning is carried out based on the analysis result.
5. The intelligent hydropower station alarm method according to claim 4, wherein the analysis results comprise a first analysis result and a second analysis result; the specific steps of analyzing the first alarm quantity and generating an analysis result comprise:
if the first alarm number exceeds a preset first number threshold, generating the first analysis result based on the target working condition and the first alarm number;
if the first alarm number exceeds a preset second number threshold and does not exceed the preset first number threshold, acquiring an alarm type corresponding to a target working condition and target numbers corresponding to different alarm types based on the historical alarm record;
judging whether the target number exceeds a target number threshold corresponding to the alarm type;
and if the target number exceeds the target number threshold, generating a second analysis result based on the target working condition, the target number and the alarm type.
6. The intelligent hydropower station alarm method according to claim 4, wherein the analysis result comprises a third analysis result; after the history alarm record is obtained, the method further comprises the following steps:
acquiring alarm time based on the historical alarm record;
acquiring the alarm number corresponding to a target time node as a second alarm number;
judging whether the second alarm number exceeds a preset third number threshold value or not;
if the second alarm number exceeds the preset third number threshold, marking the target time node as an abnormal multi-time node;
acquiring a current time node, and judging whether the current time node is matched with the abnormal multiple time node or not based on a preset matching rule;
and if the first alarm number is matched with the second alarm number, generating a third analysis result based on the second alarm number.
7. The intelligent hydropower station alarm method according to claim 6, wherein the specific step of generating the third analysis result based on the second alarm number if the first alarm number is matched with the second alarm number comprises:
if the node types are matched, the alarm type corresponding to the target time node is obtained and used as the node alarm type;
and generating a third analysis result based on the node alarm type and the second alarm number.
8. An intelligent warning system for a hydropower station, comprising:
a first acquisition module (1) for acquiring history data;
the second acquisition module (2) is used for acquiring the operation working condition based on the historical data;
the third acquisition module (3) is used for acquiring the data distribution condition of the historical data based on the operation working condition and a preset dimension;
a generation module (4) for generating data models under different operating conditions based on the data distribution conditions;
a fourth acquisition module (5) for acquiring real-time data and a target working condition to which the real-time data belong;
the judging module (6) is used for selecting the data model matched with the target working condition based on the target working condition and judging whether the real-time data meets a preset standard or not;
the alarm module (7) is used for giving an alarm if the real-time data does not meet the preset standard;
the data model is an algorithm model under each operation condition and each monitoring dimension.
9. A smart terminal comprising a memory, a processor, wherein the memory is adapted to store a computer program capable of running on the processor, and wherein the processor, when loaded with the computer program, performs the method of any of claims 1 to 7.
10. A computer readable storage medium having a computer program stored therein, characterized in that the computer program, when loaded by a processor, performs the method of any of claims 1 to 7.
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