CN117318297A - Alarm threshold setting method, system, equipment and medium based on state monitoring - Google Patents
Alarm threshold setting method, system, equipment and medium based on state monitoring Download PDFInfo
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01D—MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE; ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVERED IN A SINGLE OTHER SUBCLASS; TARIFF METERING APPARATUS; MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR
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- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
- H02J13/00001—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by the display of information or by user interaction, e.g. supervisory control and data acquisition systems [SCADA] or graphical user interfaces [GUI]
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- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
- H02J13/00002—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by monitoring
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
- H02J13/00006—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by information or instructions transport means between the monitoring, controlling or managing units and monitored, controlled or operated power network element or electrical equipment
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Abstract
The invention discloses a state monitoring-based alarm threshold setting method, a state monitoring-based alarm threshold setting system, state monitoring-based alarm threshold setting equipment and a state monitoring-based alarm threshold medium, which belong to the field of power supply or power distribution circuit systems.
Description
Technical Field
The invention belongs to the technical field of power supply or distribution circuit systems, and particularly relates to a state monitoring-based alarm threshold setting method, a state monitoring-based alarm threshold setting system, state monitoring-based alarm threshold setting equipment and state monitoring-based alarm threshold setting media.
Background
In internet of things applications, data acquisition and processing are very important parts, especially in monitoring and control systems. An alarm mechanism is indispensable for ensuring the safety and reliability of the system. The alarm mechanism can be timely found and processed when the system has abnormal conditions, so that the normal operation of the system is ensured.
The application document with the application bulletin number of CN111176153A discloses a machine room alarm system, which is connected with a data acquisition module for acquiring detection data of a monitoring unit through a server, wherein the server is connected with a data processing unit for comprehensively processing the detection data acquired by the data acquisition module, and the data processing unit is connected with a data analysis module for analyzing and comparing the data comprehensively processed by the data processing unit; the technical scheme provided by the invention can effectively overcome the defect that the alarm information cannot be pushed to the manager in time in the prior art. The alarm threshold setting of such a system is typically done by way of a fixed value or manual setting. The method has the defects that the method can not well meet different requirements of different devices, and has different alarm thresholds for various devices and scenes, so that personalized setting is difficult. Meanwhile, because the number of the devices accessed by the Internet of things system is too large, time and effort are consumed in manually setting the alarm for each device, and errors possibly exist in the manually set alarm threshold, and the errors can cause that the devices cannot timely alarm for abnormal conditions, so that serious accidents are caused. Thus, there is a need for a more intelligent and flexible method to set multi-policy alarm thresholds.
In order to solve the problems, the application designs an alarm threshold setting method, an alarm threshold setting system, alarm threshold setting equipment and alarm threshold setting media based on state monitoring.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides an alarm threshold setting method, a system, equipment and a medium based on state monitoring.
In order to achieve the above purpose, the present invention provides the following technical solutions:
the alarm threshold setting method based on state monitoring comprises the following specific steps:
s1, acquiring data of equipment to be monitored, and determining required monitoring indexes according to the requirements and settings of the equipment;
s2, extracting historical data of monitoring, collecting and recording indexes, wherein the historical data comprise normal operation values and abnormal operation values;
s3, importing the historical data and the historical interference data into a data analysis strategy to output an abnormal rule;
s4, importing the detected real-time interference data and performance requirements into an alarm threshold calculation strategy to calculate an alarm threshold;
s5, outputting the alarm threshold according to the calculation result of the alarm threshold, and applying the set alarm threshold to the Internet of things system to perform real-time monitoring and alarm processing.
Specifically, the step S1 includes the following specific steps:
s11, determining a monitoring target: defining equipment and equipment requirements to be monitored, such as business operation, system performance and user behavior;
s12, defining key monitoring service indexes: determining key business indexes which directly reflect the realization condition of the monitoring target, and setting the indexes as key monitoring business indexes, such as the production condition of a product;
s13, decomposing monitoring indexes and subdivision dimensions: decomposing the key monitoring service index into monitoring indexes, and subdividing the dimensions of the indexes according to the service and the requirements, such as the production time, the production geographic position and the type of the produced product;
s14, determining a calculation mode of the monitoring index: determining a specific mode for calculating the monitoring index according to the definition of the monitoring index and the dimension, such as summation, averaging and proportion;
s15, selecting a data acquisition mode of a monitoring index: determining a data source for acquiring index data, and selecting a proper data acquisition mode, such as log monitoring, a data warehouse and an API interface;
s16, determining monitoring frequency and time window: the sampling frequency of the monitoring index is determined, as well as the time window of each sampling, e.g. every minute, hour, day.
Specifically, the specific steps of S2 are as follows:
s21, acquiring and monitoring real-time data of the index in real time, and archiving the real-time data according to a certain time interval or periodically to form historical data;
s22, setting historical data of damage to equipment production or workpiece production as an abnormal operation value, and setting historical data of damage not to equipment production or workpiece production as a normal operation value;
s23, identifying external interference data generating abnormal operation values and normal operation values, and setting the external interference data as historical interference data, wherein the external interference data comprises temperature T, humidity F, external vibration frequency F and external vibration amplitude d;
s24, setting the abnormal operation value and the corresponding historical interference data thereof as a first dimension vector, setting the normal operation value and the corresponding historical interference data thereof as a second dimension vector, and integrating and storing the first dimension vector and the second dimension vector.
Specifically, the specific steps of S3 are as follows:
s31, extracting the abnormal operation value and the corresponding historical interference data, the normal operation value and the corresponding historical interference data, and extracting the normal operation value sequence (lambda) of the same historical interference data 1 ,λ 2 ,...,λ n1 ) And abnormal operation number sequence (beta) 1 ,β 2 ,...,β n2 ) Extracting, wherein n1 is the number of normal operation values of the same historical interference data, n2 is the number of abnormal operation values of the same historical interference data, lambda i For the ith normal running value in the sequence of normal running values, i.e. (1, n 1), beta j For the j-th abnormal operation value in the abnormal operation value sequence, j epsilon (1, n 2), arranging to obtain a normal operation value of the historical interference data and a handover value of the abnormal operation value, wherein the handover value is obtained by the following steps: average value of two nearest values in the normal operation numerical value sequence and the abnormal operation numerical value sequence;
s32, extracting a crossover value, a monitoring index standard value and a historical interference data curve corresponding to the historical interference data, wherein the historical interference data curve comprises a temperature curve, a humidity curve, an external vibration frequency curve and an external vibration amplitude curve, and the historical interference data curve comprises the temperature curve, the humidity curve, the external vibration frequency curve, the external vibration amplitude curve, the crossover value and the monitoring index standard value corresponding to the historical interference data are led into a data analysis strategy to output abnormal rules.
Specifically, the specific steps of the data analysis strategy in S32 include the following:
s321, extracting a historical interference data curve including a temperature curve, a humidity curve, an external vibration frequency curve, an external vibration amplitude curve, a corresponding intersection value of the historical interference data and a standard value of a monitoring index;
s322, establishing an abnormal formula in an abnormal rule, wherein the initial formula of the abnormal formula is as follows:wherein k is jjzi For handover value, k bzz To monitor the standard value of index, a 1 Is the temperature duty ratio coefficient, a 2 Is the humidity duty ratio coefficient, a 3 A is the ratio coefficient of the external vibration frequency 4 For the external vibration amplitude duty ratio coefficient, t is a set safe temperature range value, t m To be the nearest value to T in the set safe temperature range value, F 1 To set the safe humidity range value, F m For the value closest to F in the set safe humidity range value, F 1 For setting the value of the safe external vibration frequency range, f m For the value closest to f in the set safe external vibration frequency range value, d 1 D is the set value of the range of the external vibration amplitude m For the value closest to d among the set safe outside vibration amplitude range values, 1= (a) 1 +a 2 +a 3 +a 4 ) Exp () is the power of e;
s323, substituting the historical interference data curves including a temperature curve, a humidity curve, an external vibration frequency curve, an external vibration amplitude curve, a corresponding intersection value of the historical interference data and a standard value of a monitoring index into an abnormal formula and into fitting software to perform fitting solution of parameters, and outputting an optimal abnormal formula meeting preset accuracy as an abnormal rule to be output.
Specifically, the specific steps of the alarm threshold calculation strategy in S4 are as follows:
s41, extracting detected real-time interference data and performance requirement data, wherein the real-time interference data comprise real-time temperature data, real-time humidity data, real-time external vibration frequency data and real-time external vibration amplitude data, and the performance requirement data are preset parameter monitoring index standard values;
s42, importing the extracted real-time interference data and performance requirement data into an abnormal rule, calculating and outputting a corresponding handover value, and setting the calculated handover value as an alarm threshold.
The alarm threshold setting system based on state monitoring is realized based on the alarm threshold setting method based on state monitoring, and specifically comprises the following steps: the system comprises an acquisition module, a monitoring index determining module, a historical data collecting module, an abnormal rule calculating module, an alarm threshold calculating module, a control module and an alarm threshold output module, wherein the acquisition module is used for acquiring data of equipment to be monitored, the monitoring index determining module is used for determining required monitoring indexes according to the requirements and the settings of the equipment, the historical data collecting module is used for extracting historical data of monitoring collecting and recording indexes, the abnormal rule calculating module is used for guiding the historical data and the historical interference data into a data analysis strategy to output abnormal rules, the alarm threshold calculating module is used for guiding detected real-time interference data and performance requirements into the alarm threshold calculating strategy to calculate an alarm threshold, the alarm threshold output module is used for outputting the alarm threshold according to the calculation result of the alarm threshold, the set alarm threshold is applied to an Internet of things system to perform real-time monitoring and alarm processing, and the control module is used for controlling the operation of the acquisition module, the monitoring index determining module, the historical data collecting module, the abnormal rule calculating module, the alarm threshold calculating module and the alarm threshold outputting module.
Specifically, an electronic device includes: a processor and a memory, wherein the memory stores a computer program for the processor to call;
the processor executes the alarm threshold setting method based on state monitoring by calling the computer program stored in the memory.
Specifically, a computer readable storage medium stores instructions that, when executed on a computer, cause the computer to perform the alarm threshold setting method based on state monitoring as described above.
Compared with the prior art, the invention has the beneficial effects that:
the invention collects the data of the equipment to be monitored, determines the required monitoring index according to the requirement and the setting of the equipment, extracts the historical data of the monitoring collection and recording index, wherein the historical data comprises a normal operation value and an abnormal operation value, guides the historical data and the historical interference data into a data analysis strategy to output abnormal rules, guides the detected real-time interference data and performance requirements into an alarm threshold calculation strategy to calculate an alarm threshold, outputs the alarm threshold according to the calculation result of the alarm threshold, applies the set alarm threshold into an Internet of things system to perform real-time monitoring and alarm processing, and can realize the automatic setting and automatic optimization of the alarm threshold through the algorithm prediction based on the historical data.
Drawings
FIG. 1 is a schematic flow chart of an alarm threshold setting method based on state monitoring;
FIG. 2 is a schematic diagram showing a specific flow of steps of an alarm threshold setting method S1 based on state monitoring;
FIG. 3 is a flowchart showing the steps of the method S2 for setting the alarm threshold based on state monitoring;
fig. 4 is a schematic diagram of an overall architecture of an alarm threshold setting system based on state monitoring according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments.
Example 1
Referring to fig. 1-3, an embodiment of the present invention is provided: the alarm threshold setting method based on state monitoring comprises the following specific steps:
s1, acquiring data of equipment to be monitored, and determining required monitoring indexes according to the requirements and settings of the equipment;
wherein, S1 comprises the following specific steps:
s11, determining a monitoring target: defining equipment and equipment requirements to be monitored, such as business operation, system performance and user behavior;
s12, defining key monitoring service indexes: determining key business indexes which directly reflect the realization condition of the monitoring target, and setting the indexes as key monitoring business indexes, such as the production condition of a product;
s13, decomposing monitoring indexes and subdivision dimensions: decomposing the key monitoring service index into monitoring indexes, and subdividing the dimensions of the indexes according to the service and the requirements, such as the production time, the production geographic position and the type of the produced product;
s14, determining a calculation mode of the monitoring index: determining a specific mode for calculating the monitoring index according to the definition of the monitoring index and the dimension, such as summation, averaging and proportion;
s15, selecting a data acquisition mode of a monitoring index: determining a data source for acquiring index data, and selecting a proper data acquisition mode, such as log monitoring, a data warehouse and an API interface;
s16, determining monitoring frequency and time window: determining a sampling frequency of the monitoring index and a time window of each sampling, such as every minute, hour, day;
in the implementation of the invention, according to the actual application situation, the batch setting of the alarm threshold values can be carried out for the equipment of the same type under the same scene. For example, for a temperature sensor device and a humidity sensor device, different alarm strategies may be designed to set different alarm thresholds due to the different sensitivities of the two devices to temperature and humidity; and then batch setting alarm threshold values are carried out for different types of equipment. For example, for a plurality of temperature sensors, an alarm strategy can be set and issued to the plurality of temperature sensors simultaneously; meanwhile, different alarm strategy schemes are designed according to different scenes and requirements. For example, in medical systems, different alarm thresholds need to be set for different wards and for different medical devices to ensure the life safety of the patient. For this purpose, different models can be designed to set different alarm threshold schemes;
s2, extracting historical data of monitoring, collecting and recording indexes, wherein the historical data comprise normal operation values and abnormal operation values;
the specific steps of S2 are as follows:
s21, acquiring and monitoring real-time data of the index in real time, and archiving the real-time data according to a certain time interval or periodically to form historical data;
s22, setting historical data of damage to equipment production or workpiece production as an abnormal operation value, and setting historical data of damage not to equipment production or workpiece production as a normal operation value;
s23, identifying external interference data generating abnormal operation values and normal operation values, and setting the external interference data as historical interference data, wherein the external interference data comprises temperature T, humidity F, external vibration frequency F and external vibration amplitude d;
s24, setting an abnormal operation value and corresponding historical interference data thereof as a first dimension vector, setting a normal operation value and corresponding historical interference data thereof as a second dimension vector, integrating the first dimension vector and the second dimension vector, and storing;
s3, importing the historical data and the historical interference data into a data analysis strategy to output an abnormal rule;
the specific steps of S3 are as follows:
s31, extracting the abnormal operation value and the corresponding historical interference data, the normal operation value and the corresponding historical interference data, and extracting the normal operation value sequence (lambda) of the same historical interference data 1 ,λ 2 ,...,λ n1 ) And abnormal operation number sequence (beta) 1 ,β 2 ,...,β n2 ) Extracting, wherein n1 is the number of normal operation values of the same historical interference data, n2 is the number of abnormal operation values of the same historical interference data, lambda i For the ith normal running value in the sequence of normal running values, i.e. (1, n 1), beta j For the j-th abnormal operation value in the abnormal operation value sequence, j epsilon (1, n 2), arranging to obtain a normal operation value of the historical interference data and a handover value of the abnormal operation value, wherein the handover value is obtained by the following steps: average value of two nearest values in the normal operation numerical value sequence and the abnormal operation numerical value sequence; here, for example, the normal operation value series is (12, 13, 15, 18, 28, 32), the abnormal operation value series is (35, 37, 38, 39), and the crossover value is (32+35)/2=33.5;
s32, extracting a crossover value, a monitoring index standard value and a historical interference data curve corresponding to the historical interference data, wherein the historical interference data curve comprises a temperature curve, a humidity curve, an external vibration frequency curve and an external vibration amplitude curve, and importing the historical interference data curve comprises the temperature curve, the humidity curve, the external vibration frequency curve, the external vibration amplitude curve, the crossover value and the monitoring index standard value corresponding to the historical interference data into a data analysis strategy to output abnormal rules;
the specific steps of the data analysis strategy in S32 include the following:
s321, extracting a historical interference data curve including a temperature curve, a humidity curve, an external vibration frequency curve, an external vibration amplitude curve, a corresponding intersection value of the historical interference data and a standard value of a monitoring index;
s322, establishing an abnormal formula in an abnormal rule, wherein the initial formula of the abnormal formula is as follows:wherein k is jjzi For handover value, k bzz To monitor the standard value of index, a 1 Is the temperature duty ratio coefficient, a 2 Is the humidity duty ratio coefficient, a 3 A is the ratio coefficient of the external vibration frequency 4 For the external vibration amplitude duty ratio coefficient, t is a set safe temperature range value, t m To be the nearest value to T in the set safe temperature range value, F 1 To set the safe humidity range value, F m For the value closest to F in the set safe humidity range value, F 1 For setting the value of the safe external vibration frequency range, f m For the value closest to f in the set safe external vibration frequency range value, d 1 D is the set value of the range of the external vibration amplitude m For the value closest to d among the set safe outside vibration amplitude range values, 1= (a) 1 +a 2 +a 3 +a 4 ) Exp () is the power of e;
s323, substituting the historical interference data curves including a temperature curve, a humidity curve, an external vibration frequency curve, an external vibration amplitude curve, a corresponding intersection value of the historical interference data and a standard value of a monitoring index into an abnormal formula and into fitting software to perform fitting solution of parameters, and outputting an optimal abnormal formula meeting preset accuracy as an abnormal rule to be output;
s4, importing the detected real-time interference data and performance requirements into an alarm threshold calculation strategy to calculate an alarm threshold;
the specific steps of the alarm threshold calculation strategy in the step S4 are as follows:
s41, extracting detected real-time interference data and performance requirement data, wherein the real-time interference data comprise real-time temperature data, real-time humidity data, real-time external vibration frequency data and real-time external vibration amplitude data, and the performance requirement data are preset parameter monitoring index standard values;
s42, importing the extracted real-time interference data and performance requirement data into an abnormal rule, calculating and outputting a corresponding handover value, and setting the calculated handover value as an alarm threshold;
s5, outputting the alarm threshold according to the calculation result of the alarm threshold, and applying the set alarm threshold to the Internet of things system to perform real-time monitoring and alarm processing.
The method comprises the steps of acquiring data of equipment to be monitored, determining required monitoring indexes according to the requirements and the settings of the equipment, extracting historical data of monitoring, collecting and recording indexes, wherein the historical data comprises normal operation values and abnormal operation values, guiding the historical data and the historical interference data into a data analysis strategy to output abnormal rules, guiding detected real-time interference data and performance requirements into an alarm threshold calculation strategy to calculate an alarm threshold, outputting the alarm threshold according to the calculation result of the alarm threshold, applying the set alarm threshold into an Internet of things system to perform real-time monitoring and alarm processing, and realizing automatic setting and automatic optimization of the alarm threshold through algorithm prediction based on the historical data;
the traditional alarm threshold setting method is often set once and used for a lifetime. The alarm threshold value setting process is complicated, so that the operation and maintenance personnel can not refine the threshold value or adjust the threshold value according to different scenes after setting the alarm threshold value when the system is started. In the actual operation of the internet of things system, the alarm threshold value needs to be adjusted in time according to different climates, business requirements and the like. In the invention, the alarm scheme can be set according to the scene, such as summer, winter, emergency guarantee, daily work and the like. The system dynamically identifies relevant equipment measuring points and threshold values to be adjusted in the scene by establishing an association relation between the scene and the monitoring equipment, and automatically adjusts the alarm threshold values according to a preset logic rule or a calculation formula; in the implementation process of the invention, the set alarm threshold and alarm level can be applied to the Internet of things system for real-time monitoring and alarm processing. When the system is abnormal, corresponding alarm and warning information can be sent out in time, so that measures can be taken in time to ensure the normal operation of the system. Meanwhile, the algorithm can judge the accuracy of manually setting the alarm threshold or the strategy according to the historical data in the actual application, and correct the error setting. In summary, the invention can ensure that the sensor equipment alarms abnormal situations in time, and avoid accidents.
Example 2
As shown in fig. 4, the alarm threshold setting system based on state monitoring is implemented based on the above alarm threshold setting method based on state monitoring, and specifically includes: the system comprises an acquisition module, a monitoring index determining module, a historical data collecting module, an abnormal rule calculating module, an alarm threshold calculating module, a control module and an alarm threshold output module, wherein the acquisition module is used for acquiring data of equipment to be monitored, the monitoring index determining module is used for determining required monitoring indexes according to the requirements and the settings of the equipment, the historical data collecting module is used for extracting historical data of monitoring, collecting and recording indexes, the abnormal rule calculating module is used for guiding the historical data and the historical interference data into a data analysis strategy to output abnormal rules, the alarm threshold calculating module is used for guiding detected real-time interference data and performance requirements into the alarm threshold calculating strategy to calculate an alarm threshold, the alarm threshold output module is used for outputting the alarm threshold according to the calculation result of the alarm threshold, the set alarm threshold is applied to an Internet of things system to perform real-time monitoring and alarm processing, and the control module is used for controlling the operation of the acquisition module, the monitoring index determining module, the historical data collecting module, the abnormal rule calculating module, the alarm threshold calculating module and the alarm threshold output module.
Example 3
The present embodiment provides an electronic device including: a processor and a memory, wherein the memory stores a computer program for the processor to call;
the processor executes the alarm threshold setting method based on the state monitoring described above by calling a computer program stored in the memory.
The electronic device may vary greatly in configuration or performance, and can include one or more processors (Central Processing Units, CPU) and one or more memories, where the memories store at least one computer program that is loaded and executed by the processors to implement the alarm threshold setting method based on condition monitoring provided by the above method embodiments. The electronic device can also include other components for implementing the functions of the device, for example, the electronic device can also have wired or wireless network interfaces, input-output interfaces, and the like, for inputting and outputting data. The present embodiment is not described herein.
Example 4
The present embodiment proposes a computer-readable storage medium having stored thereon an erasable computer program;
the computer program, when run on a computer device, causes the computer device to perform the alarm threshold setting method based on state monitoring described above.
For example, the computer readable storage medium can be Read-Only Memory (ROM), random access Memory (Random Access Memory, RAM), compact disk Read-Only Memory (Compact Disc Read-Only Memory, CD-ROM), magnetic tape, floppy disk, optical data storage device, etc.
It should be understood that, in various embodiments of the present application, the sequence numbers of the foregoing processes do not mean the order of execution, and the order of execution of the processes should be determined by the functions and internal logic thereof, and should not constitute any limitation on the implementation process of the embodiments of the present application.
It should be understood that determining B from a does not mean determining B from a alone, but can also determine B from a and/or other information.
The above embodiments may be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented in software, the above-described embodiments may be implemented in whole or in part in the form of a computer program product. The computer program product comprises one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the processes or functions in accordance with embodiments of the present invention are produced in whole or in part. The computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. The computer instructions may be stored in or transmitted from one computer-readable storage medium to another, for example, by way of wired or/and wireless networks from one website site, computer, server, or data center to another. Computer readable storage media can be any available media that can be accessed by a computer or data storage devices, such as servers, data centers, etc. that contain one or more collections of available media. The usable medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. The semiconductor medium may be a solid state disk.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, and are not repeated herein.
In the several embodiments provided by the present invention, it should be understood that the disclosed systems, devices, and methods may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of the units is merely one, and there may be additional divisions in actual implementation, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit.
In the description of the present specification, the descriptions of the terms "one embodiment," "example," "specific example," and the like, mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The preferred embodiments of the invention disclosed above are intended only to assist in the explanation of the invention. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise form disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best understand and utilize the invention. The invention is limited only by the claims and the full scope and equivalents thereof.
Claims (10)
1. The alarm threshold setting method based on state monitoring is characterized by comprising the following specific steps:
s1, acquiring data of equipment to be monitored, and determining required monitoring indexes according to the requirements and settings of the equipment;
s2, extracting historical data of monitoring, collecting and recording indexes, wherein the historical data comprise normal operation values and abnormal operation values;
s3, importing the historical data and the historical interference data into a data analysis strategy to output an abnormal rule;
s4, importing the detected real-time interference data and performance requirements into an alarm threshold calculation strategy to calculate an alarm threshold;
s5, outputting the alarm threshold according to the calculation result of the alarm threshold, and applying the set alarm threshold to the Internet of things system to perform real-time monitoring and alarm processing.
2. The alarm threshold setting method based on state monitoring as claimed in claim 1, wherein S1 comprises the following specific steps:
s11, determining a monitoring target: defining equipment and equipment requirements to be monitored;
s12, defining key monitoring service indexes: determining key service indexes which directly reflect the realization condition of the monitoring target, and setting the indexes as key monitoring service indexes;
s13, decomposing monitoring indexes and subdivision dimensions: decomposing the key monitoring service index into monitoring indexes, and subdividing the dimensionality of the indexes according to the service and the requirements;
s14, determining a calculation mode of the monitoring index: determining a specific mode for calculating the monitoring index according to the definition of the monitoring index and the dimension;
s15, selecting a data acquisition mode of a monitoring index: determining a data source for acquiring index data, and selecting a proper data acquisition mode;
s16, determining monitoring frequency and time window: the sampling frequency of the monitoring index is determined, as well as the time window of each sampling.
3. The alarm threshold setting method based on state monitoring as claimed in claim 2, wherein the specific steps of S2 are as follows:
s21, acquiring and monitoring real-time data of the index in real time, and archiving the real-time data according to a certain time interval or periodically to form historical data;
s22, setting historical data of damage to equipment production or workpiece production as an abnormal operation value, and setting historical data of damage not to equipment production or workpiece production as a normal operation value;
s23, identifying external interference data generating abnormal operation values and normal operation values, and setting the external interference data as historical interference data, wherein the external interference data comprises temperature T, humidity F, external vibration frequency F and external vibration amplitude d;
s24, setting the abnormal operation value and the corresponding historical interference data thereof as a first dimension vector, setting the normal operation value and the corresponding historical interference data thereof as a second dimension vector, and integrating and storing the first dimension vector and the second dimension vector.
4. The alarm threshold setting method based on state monitoring as claimed in claim 3, wherein the specific steps of S3 are as follows:
s31, extracting the abnormal operation value and the corresponding historical interference data, the normal operation value and the corresponding historical interference data, and extracting the normal operation value sequence (lambda) of the same historical interference data 1 ,λ 2 ,...,λ n1 ) And abnormal operation number sequence (beta) 1 ,β 2 ,...,β n2 ) Extracting, wherein n1 is the number of normal operation values of the same historical interference data, n2 is the number of abnormal operation values of the same historical interference data, lambda i For the ith normal running value in the sequence of normal running values, i.e. (1, n 1), beta j For the j-th abnormal operation value in the abnormal operation value sequence, j epsilon (1, n 2), arranging to obtain a normal operation value of the historical interference data and a handover value of the abnormal operation value, wherein the handover value is obtained by the following steps: average value of two nearest values in the normal operation numerical value sequence and the abnormal operation numerical value sequence;
s32, extracting a crossover value, a monitoring index standard value and a historical interference data curve corresponding to the historical interference data, wherein the historical interference data curve comprises a temperature curve, a humidity curve, an external vibration frequency curve and an external vibration amplitude curve, and the historical interference data curve comprises the temperature curve, the humidity curve, the external vibration frequency curve, the external vibration amplitude curve, the crossover value and the monitoring index standard value corresponding to the historical interference data are led into a data analysis strategy to output abnormal rules.
5. The alarm threshold setting method based on state monitoring as claimed in claim 4, wherein the specific steps of the data analysis strategy in S32 include the following:
s321, extracting a historical interference data curve including a temperature curve, a humidity curve, an external vibration frequency curve, an external vibration amplitude curve, a corresponding intersection value of the historical interference data and a standard value of a monitoring index;
s322, establishing an abnormal formula in an abnormal rule, wherein the initial formula of the abnormal formula is as follows:
，
wherein k is jjzi For handover value, k bzz To monitor the standard value of index, a 1 Is the temperature duty ratio coefficient, a 2 Is the humidity duty ratio coefficient, a 3 A is the ratio coefficient of the external vibration frequency 4 For the external vibration amplitude duty ratio coefficient, t is a set safe temperature range value, t m To be the nearest value to T in the set safe temperature range value, F 1 To set the safe humidity range value, F m For the value closest to F in the set safe humidity range value, F 1 For setting the value of the safe external vibration frequency range, f m For the value closest to f in the set safe external vibration frequency range value, d 1 D is the set value of the range of the external vibration amplitude m For the value closest to d among the set safe outside vibration amplitude range values, 1= (a) 1 +a 2 +a 3 +a 4 ) Exp () is the power of e;
s323, substituting the historical interference data curves including a temperature curve, a humidity curve, an external vibration frequency curve, an external vibration amplitude curve, a corresponding intersection value of the historical interference data and a standard value of a monitoring index into an abnormal formula and into fitting software to perform fitting solution of parameters, and outputting an optimal abnormal formula meeting preset accuracy as an abnormal rule to be output.
6. The alarm threshold setting method based on state monitoring as claimed in claim 5, wherein the specific steps of the alarm threshold calculation strategy in S4 are:
s41, extracting detected real-time interference data and performance requirement data, wherein the real-time interference data comprise real-time temperature data, real-time humidity data, real-time external vibration frequency data and real-time external vibration amplitude data, and the performance requirement data are preset parameter monitoring index standard values;
s42, importing the extracted real-time interference data and performance requirement data into an abnormal rule, calculating and outputting a corresponding handover value, and setting the calculated handover value as an alarm threshold.
7. An alarm threshold setting system based on state monitoring, which is implemented based on the alarm threshold setting method based on state monitoring as claimed in any one of claims 1 to 6, and is characterized in that it specifically comprises: the monitoring system comprises an acquisition module, a monitoring index determining module, a historical data collecting module, an abnormal rule calculating module, an alarm threshold calculating module, a control module and an alarm threshold output module, wherein the acquisition module is used for acquiring data of equipment to be monitored, the monitoring index determining module is used for determining required monitoring indexes according to the requirements and the settings of the equipment, and the historical data collecting module is used for extracting historical data of monitoring, collecting and recording indexes.
8. The state monitoring based alarm threshold setting system of claim 7, wherein the control module controls operation of the acquisition module, the monitoring indicator determination module, the historical data collection module, the anomaly rule calculation module, the alarm threshold calculation module, and the alarm threshold output module.
9. The alarm threshold setting system based on state monitoring as claimed in claim 8, wherein the alarm threshold output module is configured to output an alarm threshold according to a calculation result of the alarm threshold, and apply the set alarm threshold to the internet of things system for real-time monitoring and alarm processing.
10. The alarm threshold setting system based on state monitoring according to claim 9, wherein the abnormal rule calculation module is configured to import historical data and historical interference data into a data analysis policy to output abnormal rules, and the alarm threshold calculation module is configured to import detected real-time interference data and performance requirements into an alarm threshold calculation policy to calculate an alarm threshold.
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| CN117665574A (en) * | 2024-02-01 | 2024-03-08 | 深圳市思科诺达科技有限公司 | Constant temperature servo motor testing system and method based on data identification |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117665574A (en) * | 2024-02-01 | 2024-03-08 | 深圳市思科诺达科技有限公司 | Constant temperature servo motor testing system and method based on data identification |
| CN117665574B (en) * | 2024-02-01 | 2024-04-09 | 深圳市思科诺达科技有限公司 | Constant temperature servo motor testing system and method based on data identification |
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