CN116859838B - Early warning system for monitoring equipment operation condition - Google Patents
Early warning system for monitoring equipment operation condition Download PDFInfo
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- 230000002159 abnormal effect Effects 0.000 claims abstract description 41
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- 238000007781 pre-processing Methods 0.000 claims abstract description 9
- 238000005070 sampling Methods 0.000 claims description 101
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- 238000004458 analytical method Methods 0.000 claims description 29
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- 238000012549 training Methods 0.000 claims description 13
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- G—PHYSICS
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- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
- G05B19/4183—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM] characterised by data acquisition, e.g. workpiece identification
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- G—PHYSICS
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- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
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- G05B2219/31—From computer integrated manufacturing till monitoring
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Abstract
The invention provides an early warning system for monitoring the running condition of equipment, which comprises the following components: collecting working data of the appointed equipment, preprocessing the working data to obtain target data, acquiring data characteristics of the target data, inputting the data characteristics into a preset equipment monitoring model for state monitoring, generating a monitoring report, analyzing the monitoring report to obtain the running state of the appointed equipment, generating early warning information when the running state of the appointed equipment is abnormal and transmitting the early warning information to the appointed terminal for display so as to analyze the running state of the equipment, and generating early warning information when the appointed equipment is abnormal, wherein in the process, manual measurement and monitoring are not needed, the cost is reduced, and the accuracy of early warning work is improved.
Description
Technical Field
The invention relates to the technical field of equipment working early warning, in particular to an early warning system for monitoring equipment operation conditions.
Background
In modern production operations, monitoring of the operation of various equipment and machines is very important. Equipment failure or downtime can lead to production interruptions, reduced efficiency, and even unpredictable hazards. Therefore, in order to improve the production efficiency and the equipment reliability, an early warning system for monitoring the running condition of the equipment is required to be introduced. Therefore, the state and performance index of the specific equipment can be monitored in real time, and the running abnormality of the equipment can be early warned and tracked in time so as to repair and maintain in time. For some critical equipment, intelligent early warning and maintenance can be carried out, and the probability of fault occurrence is predicted through technologies such as machine learning, so that more efficient and more accurate early warning and maintenance are realized. Development of sensor technology: with the continuous development of sensor technology, various aspects of the running process of the equipment, such as temperature, power consumption, amplitude, sound and the like, can be monitored in real time. This opens new possibilities for fault diagnosis and prevention of the device.
Application of machine learning and artificial intelligence techniques: the machine learning and artificial intelligence technology can process and analyze a large amount of data rapidly and accurately, can realize automatic monitoring, accurate positioning and diagnosis of equipment faults, and provides effective early warning and prediction functions.
Popularization of the Internet and Internet of things technologies: the internet and the internet of things technology provide technical support for data sharing and unified management between devices, and provide possibility for centralized monitoring and fault analysis of the devices.
Requirements for device stability and reliability: stability and reliability of the equipment are very critical issues in modern production and manufacturing. Many factors such as temperature, power consumption, amplitude, sound and the like of the operation of the equipment have great influence on the health state of the equipment, so that fault diagnosis and prevention by these factors are currently important trends.
In addition, the rapid development and wide application of modern computer technology also greatly promote the industrial application of temperature, power consumption, amplitude and voiceprint analysis and diagnosis algorithms. The method has the advantages that the method is more and more supported and focused in the directions of hardware equipment and algorithm optimization, practical application, intelligent management and the like, and a solid foundation is laid for further popularization and application of the technology.
However, existing machine operation monitoring devices have a number of problems, such as: the traditional diagnosis method needs a large amount of manual measurement and monitoring, has the problems of insufficient personnel resources, low measurement precision, low working efficiency, unstable diagnosis precision, more common misjudgment or missed judgment and the like besides high cost,
therefore, the invention provides an early warning system for monitoring the running condition of equipment.
Disclosure of Invention
According to the early warning system for monitoring the running condition of the equipment, the running condition of the equipment can be analyzed by collecting the working data of the appointed equipment and then carrying out characteristic analysis on the working data and carrying out state monitoring on the generated characteristics by utilizing the preset monitoring model, early warning information is generated when the appointed equipment is abnormal, and in the process, manual measurement and monitoring are not needed, so that the cost is reduced, and the accuracy of early warning work is improved.
The invention provides an early warning system for monitoring the running condition of equipment, which comprises the following components:
the acquisition module is used for acquiring working data of the appointed equipment;
the processing module is used for preprocessing the working data to obtain target data and obtaining data characteristics of the target data;
the modeling module is used for inputting the data characteristics into a preset equipment monitoring model to perform state monitoring and generating a monitoring report;
and the execution module is used for analyzing the monitoring report to obtain the running state of the appointed equipment, and generating early warning information to be transmitted to the appointed terminal for display when the running state of the appointed equipment is abnormal.
In one embodiment of the present invention, in one possible implementation,
the acquisition module comprises:
the first acquisition unit is used for acquiring temperature data in the working process of the appointed equipment;
the second acquisition unit is used for acquiring power consumption data in the working process of the appointed equipment;
the fourth acquisition unit is used for acquiring amplitude data in the working process of the appointed equipment;
the fifth acquisition unit is used for acquiring sound data in the working process of the appointed equipment;
and the data synchronization unit is used for carrying out time synchronization on the temperature data, the power consumption data, the amplitude data and the sound data according to the corresponding acquisition time of each acquisition unit, and establishing working data.
In one embodiment of the present invention, in one possible implementation,
the processing module comprises:
the preprocessing unit is used for carrying out noise reduction and filtering processing on the working data to generate target data;
the sampling unit is used for sampling the target data to obtain a plurality of sampling points, and sampling sub-data corresponding to each sampling point is respectively obtained;
the analysis unit is used for respectively carrying out data division on each sampling sub-data to obtain a plurality of regional sub-data, and respectively obtaining data sub-features corresponding to each regional sub-data;
the integration unit is used for acquiring characteristic influence parameters among different data sub-characteristics, respectively correcting each data sub-characteristic according to the characteristic influence parameters to obtain a characteristic range corresponding to each data sub-characteristic, establishing the data characteristic of the target data according to a plurality of data sub-characteristics corresponding to the same sampling point, and establishing a target characteristic range according to the characteristic range corresponding to each data sub-characteristic.
In one embodiment of the present invention, in one possible implementation,
the processing module further comprises:
and the comparison unit is used for judging whether the data characteristics are in the target characteristic range, and if the data characteristics are not in the target characteristic range, the abnormal operation of the designated equipment is not determined, and abnormal information is generated and transmitted to the designated terminal for display.
In one embodiment of the present invention, in one possible implementation,
the modeling module includes:
the preparation unit is used for acquiring the historical data of the appointed equipment, acquiring the historical characteristics of the appointed equipment and acquiring a preset equipment monitoring model corresponding to the appointed equipment;
the correction unit is used for respectively acquiring the historical time corresponding to each historical feature, establishing a historical feature sequence according to the sequence of the historical time, and sequentially correcting a preset equipment monitoring model by utilizing each historical feature in the historical feature sequence to obtain a current equipment monitoring model;
the training unit is used for carrying out state training on the data characteristic data in the current equipment monitoring model to obtain a training model;
the monitoring unit is used for analyzing the training model to obtain a plurality of model features, obtaining a plurality of state data of the appointed equipment according to the model features, and building a monitoring report according to the state data.
In one embodiment of the present invention, in one possible implementation,
the modeling module further includes:
the evaluation unit is used for respectively acquiring a correction result corresponding to each correction in the process of correcting the preset equipment monitoring model by using the historical characteristic sequence and establishing a correction result sequence;
analyzing the correction result sequence to obtain correction amounts between adjacent correction results, and evaluating the loss amount of the designated equipment according to the correction amounts to obtain the loss trend of the designated equipment;
and evaluating the residual service life of the appointed equipment according to the loss trend, and generating feedback information to be transmitted to the appointed terminal for display when the residual service life is smaller than the preset service life.
In one embodiment of the present invention, in one possible implementation,
the execution module comprises:
the execution unit is used for obtaining the running state of the appointed equipment according to the monitoring report;
the judging unit is used for judging whether the running state belongs to an abnormal state or not;
the early warning unit is used for acquiring the state difference between the running state and the normal state when the running state belongs to the abnormal state, and establishing early warning information by feeling the state difference;
and the transmission unit is used for transmitting the early warning information to the appointed designation for display.
In one embodiment of the present invention, in one possible implementation,
the analysis unit includes:
the first analysis subunit is used for searching data nodes contained in each sampling sub-data respectively, and dividing the corresponding sampling sub-data into a plurality of regional sub-data according to the positions of the data nodes in the sampling sub-data;
the second analysis subunit is used for respectively acquiring the data attribute corresponding to each region sub-data corresponding to the same data sampling sub-data, and recording the target region sub-data with the same data attribute corresponding to each sampling sub-data as the same data class;
the third analysis subunit is used for respectively carrying out logic analysis on each data class to obtain logic relations among the sub-data in different areas in the same data class, and constraint information among the sub-data in different areas in the same data class is obtained according to the logic relations;
and the fourth analysis subunit is used for respectively acquiring the data quantity corresponding to each region sub-data and combining the constraint information to obtain the data sub-feature corresponding to each region sub-data.
In one embodiment of the present invention, in one possible implementation,
the sampling unit includes:
the first sampling subunit is used for sampling the target data and determining a plurality of sampling points;
the second sampling subunit is used for respectively acquiring first data corresponding to each sampling point, respectively establishing a corresponding data clock for each first data, and establishing a clock tree according to the data clock;
the third sampling subunit is used for respectively analyzing each clock tree, extracting clock empty branches on each clock tree, acquiring abnormal sampling points corresponding to the clock empty branches, acquiring adjacent sampling points corresponding to the abnormal sampling points, and resampling in the range of the adjacent sampling points to obtain corresponding second data;
and the fourth sampling subunit is used for replacing the corresponding first data by the second data to obtain sampling sub-data corresponding to each sampling point.
In one embodiment of the present invention, in one possible implementation,
further comprises:
when the number of the abnormal sampling points in the target data is larger than the preset number, determining that the appointed equipment is in an abnormal working state, generating early warning information and transmitting the early warning information to the appointed terminal for display.
The invention has the beneficial effects that: the method comprises the steps of collecting working data of the appointed equipment, preprocessing the working data, then carrying out feature extraction, and further inputting data features into a preset equipment model for state monitoring, so that a monitoring report can be generated, the running state of the appointed equipment can be clearly judged, early warning information is generated and transmitted to the appointed terminal for display when the running state of the appointed equipment is abnormal in order to achieve the purpose of early warning, serious adverse effects caused by the fact that the appointed equipment is in an abnormal working state for a long time can be effectively avoided. The method has wide technical application fields such as mechanical equipment state monitoring, power system state monitoring, fault diagnosis, power equipment state monitoring, automatic monitoring, safety control and the like, and has the advantages of rapidness, accuracy, real time and the like, thus having very wide application prospect.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims thereof as well as the appended drawings.
The technical scheme of the invention is further described in detail through the drawings and the embodiments.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention. In the drawings:
FIG. 1 is a schematic diagram of a system for monitoring the operation of a device according to an embodiment of the present invention;
FIG. 2 is a schematic diagram illustrating the processing modules of an early warning system for monitoring the operation of a device according to an embodiment of the present invention;
fig. 3 is a schematic diagram of modeling modules of an early warning system for monitoring equipment operation conditions in an embodiment of the invention.
Detailed Description
The preferred embodiments of the present invention will be described below with reference to the accompanying drawings, it being understood that the preferred embodiments described herein are for illustration and explanation of the present invention only, and are not intended to limit the present invention.
Example 1
The embodiment provides an early warning system for monitoring equipment operation conditions, as shown in fig. 1, including:
the acquisition module is used for acquiring working data of the appointed equipment;
the processing module is used for preprocessing the working data to obtain target data and obtaining data characteristics of the target data;
the modeling module is used for inputting the data characteristics into a preset equipment monitoring model to perform state monitoring and generating a monitoring report;
and the execution module is used for analyzing the monitoring report to obtain the running state of the appointed equipment, and generating early warning information to be transmitted to the appointed terminal for display when the running state of the appointed equipment is abnormal.
In this example, the operation data represents data generated when the specified device is in an operation state;
in this example, the data features represent features corresponding to the target data at the current time, that is, features of the target data at different current times can be distinguished;
in this example, the preset device model represents the product of representing a specified device in the virtual space;
in this example, the monitoring report includes the working state of the designated device and the working information of different devices of the designated device;
in this example, the operation states of the designated device include two types, normal operation and abnormal operation;
in this example, the format of the early warning information may be: charts or reports;
in this example, the mode of transmitting the early warning information may be: short messages or mails;
in this example, the designated terminal may be a mobile phone of the relevant person or a display operated by the relevant person.
The working principle of the technical scheme has the beneficial effects that: the method comprises the steps of collecting working data of the appointed equipment, preprocessing the working data, then carrying out feature extraction, and further inputting data features into a preset equipment model for state monitoring, so that a monitoring report can be generated, the running state of the appointed equipment can be clearly judged, early warning information is generated and transmitted to the appointed terminal for display when the running state of the appointed equipment is abnormal in order to achieve the purpose of early warning, serious adverse effects caused by the fact that the appointed equipment is in an abnormal working state for a long time can be effectively avoided. The method has wide technical application fields such as mechanical equipment state monitoring, power system state monitoring, fault diagnosis, power equipment state monitoring, automatic monitoring, safety control and the like, and has the advantages of rapidness, accuracy, real time and the like, thus having very wide application prospect.
Example 2
On the basis of embodiment 1, the early warning system for monitoring the operation condition of the device, the acquisition module includes:
the first acquisition unit is used for acquiring temperature data in the working process of the appointed equipment;
the second acquisition unit is used for acquiring power consumption data in the working process of the appointed equipment;
the fourth acquisition unit is used for acquiring amplitude data in the working process of the appointed equipment;
the fifth acquisition unit is used for acquiring sound data in the working process of the appointed equipment;
and the data synchronization unit is used for carrying out time synchronization on the temperature data, the power consumption data, the amplitude data and the sound data according to the corresponding acquisition time of each acquisition unit, and establishing working data.
The working principle of the technical scheme has the beneficial effects that: working data are established by collecting data of different angles of the designated equipment in the working process, multi-angle analysis can be realized, and the monitoring effectiveness is improved.
Example 3
On the basis of embodiment 1, the early warning system for monitoring the operation condition of the device, as shown in fig. 2, the processing module includes:
the preprocessing unit is used for carrying out noise reduction and filtering processing on the working data to generate target data;
the sampling unit is used for sampling the target data to obtain a plurality of sampling points, and sampling sub-data corresponding to each sampling point is respectively obtained;
the analysis unit is used for respectively carrying out data division on each sampling sub-data to obtain a plurality of regional sub-data, and respectively obtaining data sub-features corresponding to each regional sub-data;
the integration unit is used for acquiring characteristic influence parameters among different data sub-characteristics, respectively correcting each data sub-characteristic according to the characteristic influence parameters to obtain a characteristic range corresponding to each data sub-characteristic, establishing the data characteristic of the target data according to a plurality of data sub-characteristics corresponding to the same sampling point, and establishing a target characteristic range according to the characteristic range corresponding to each data sub-characteristic.
In this example, the method for performing noise reduction filtering processing on the working data may be: a wavelet transform method;
in this example, one sampling sub-data corresponding to one sampling point, and a part of the target data is sampled when the sub-data is sampled;
in this example, the number of region sub-data is one, respectively: temperature sub-data, power consumption sub-data, amplitude sub-data, sound sub-data;
in this example, the data sub-feature represents a feature that is used to distinguish between different region sub-data;
in this example, the characteristic influencing parameter represents a parameter that influences each other between different data sub-characteristics;
in this example, the feature range represents the range of feature fluctuations that the data sub-feature exhibits under the influence of different feature influencing parameters.
The working principle of the technical scheme has the beneficial effects that: in order to improve the effectiveness of the monitoring result, the influence of interference data on the monitoring result is avoided, noise reduction and filtering processing is carried out on working data before sampling is carried out, target data is generated, then the target data is sampled, a plurality of sampled sub-data are obtained, influence parameters among different data sub-features are analyzed, and a feature range of the data sub-features is established, so that the data features of the target data and the target feature range of the target data are established, and a foundation is laid for the follow-up monitoring work.
Example 4
On the basis of embodiment 1, the early warning system for monitoring the operation condition of the device, the processing module further includes:
and the comparison unit is used for judging whether the data characteristics are in the target characteristic range, and if the data characteristics are not in the target characteristic range, the abnormal operation of the designated equipment is not determined, and abnormal information is generated and transmitted to the designated terminal for display.
The working principle of the technology and the beneficial effects are that: in order to accelerate the monitoring progress, when the data characteristics are abnormal, the abnormal operation of the designated equipment is directly determined, and abnormal information is generated and transmitted to the designated terminal for display.
Example 5
On the basis of embodiment 1, the early warning system for monitoring the operation condition of the device, as shown in fig. 3, the modeling module includes:
the preparation unit is used for acquiring the historical data of the appointed equipment, acquiring the historical characteristics of the appointed equipment and acquiring a preset equipment monitoring model corresponding to the appointed equipment;
the correction unit is used for respectively acquiring the historical time corresponding to each historical feature, establishing a historical feature sequence according to the sequence of the historical time, and sequentially correcting a preset equipment monitoring model by utilizing each historical feature in the historical feature sequence to obtain a current equipment monitoring model;
the training unit is used for carrying out state training on the data characteristic data in the current equipment monitoring model to obtain a training model;
the monitoring unit is used for analyzing the training model to obtain a plurality of model features, obtaining a plurality of state data of the appointed equipment according to the model features, and building a monitoring report according to the state data.
In this example, the history data represents work data generated by the designated device over a history time;
in this example, the history feature represents a feature of the history data;
in this example, a preset device monitoring model corresponding to a specific device;
in this example, the history time indicates the time at which the history feature is generated, and one history feature corresponds to one history time;
in this example, the history feature sequence represents a sequence obtained by sorting history features corresponding to different history times.
The working principle of the technical scheme has the beneficial effects that: in order to further analyze the working state of the appointed equipment, before monitoring by using a preset equipment monitoring model, historical data and historical characteristics of the appointed equipment are acquired, then the preset equipment monitoring model is corrected by using a plurality of historical characteristics, so that a current equipment monitoring model which is most consistent with the current situation can be obtained, the data characteristics are input into the current equipment monitoring model for training, a plurality of model characteristics can be obtained, finally, a monitoring report is established according to the model characteristic establishment state data, the follow-up monitoring result can be more persuasive by correcting the preset equipment model by using the historical characteristics in the process, and the equipment health state model is established by combining the historical data for analysis and modeling, so that the stability and reliability of the equipment are improved.
Example 6
On the basis of embodiment 5, the early warning system for monitoring the operation condition of the device, the modeling module further includes:
the evaluation unit is used for respectively acquiring a correction result corresponding to each correction in the process of correcting the preset equipment monitoring model by using the historical characteristic sequence and establishing a correction result sequence;
analyzing the correction result sequence to obtain correction amounts between adjacent correction results, and evaluating the loss amount of the designated equipment according to the correction amounts to obtain the loss trend of the designated equipment;
and evaluating the residual service life of the appointed equipment according to the loss trend, and generating feedback information to be transmitted to the appointed terminal for display when the residual service life is smaller than the preset service life.
In the example, the correction result includes a correction amount corresponding to each correction and a model parameter of a corrected preset equipment monitoring model;
in this example, the feedback information represents information that will feedback the remaining service life of the specified device;
in this example, the preset lifetime may be 5% of the total lifetime.
The working principle of the technical scheme has the beneficial effects that: in order to further monitor the specified equipment, a correction result corresponding to each correction is acquired in the process of correcting a preset equipment monitoring model by using a historical characteristic sequence, then the loss of the specified equipment after one-time work is estimated according to the correction result, and then the loss trend of the specified equipment is established according to the loss, so that the residual service life of the specified equipment can be estimated, corresponding feedback is made, and the specified equipment is subjected to life supervision in mind and body in such a way, so that misjudgment and misdiagnosis caused by errors and subjective factors can be reduced.
Example 7
On the basis of embodiment 1, the early warning system for monitoring the running condition of the device, the execution module includes:
the execution unit is used for obtaining the running state of the appointed equipment according to the monitoring report;
the judging unit is used for judging whether the running state belongs to an abnormal state or not;
the early warning unit is used for acquiring the state difference between the running state and the normal state when the running state belongs to the abnormal state, and establishing early warning information by feeling the state difference;
and the transmission unit is used for transmitting the early warning information to the appointed designation for display.
In this example, the state difference represents the difference between the running state and the normal state, and is a vector;
in this example, the early warning information includes a state difference and an early warning word.
The working principle of the technical scheme has the beneficial effects that: the operation state of the appointed equipment is obtained by analyzing the monitoring report, whether the early warning is needed or not is determined by judging the abnormality of the operation state, and the early warning is carried out under the necessary condition, so that the operation state of the appointed equipment can be monitored at any time and the corresponding early warning can be carried out, and the operation state of the equipment can be tracked and monitored by engineers and maintainers conveniently.
Example 8
On the basis of embodiment 3, the early warning system for monitoring the operation condition of the device, the analysis unit includes:
the first analysis subunit is used for searching data nodes contained in each sampling sub-data respectively, and dividing the corresponding sampling sub-data into a plurality of regional sub-data according to the positions of the data nodes in the sampling sub-data;
the second analysis subunit is used for respectively acquiring the data attribute corresponding to each region sub-data corresponding to the same data sampling sub-data, and recording the target region sub-data with the same data attribute corresponding to each sampling sub-data as the same data class;
the third analysis subunit is used for respectively carrying out logic analysis on each data class to obtain logic relations among the sub-data in different areas in the same data class, and constraint information among the sub-data in different areas in the same data class is obtained according to the logic relations;
and the fourth analysis subunit is used for respectively acquiring the data quantity corresponding to each region sub-data and combining the constraint information to obtain the data sub-feature corresponding to each region sub-data.
In this example, the data node represents a junction of starting points of different data in the sampled sub-data;
in this example, the region sub-data represents one part of data in the sampling sub-data, and the region sub-data may be one of temperature data, power consumption data, amplitude data, and sound data;
in this example, the data attributes may be: temperature data attributes, power consumption data attributes, amplitude data attributes, sound data attributes;
in this example, the attributes of the target region sub-data in the same data class are consistent;
in this example, constraint information represents the effect of one region sub-data in the same class of data on another region data, such as: the working temperature at 0 point is 25 degrees celsius, and if the temperature at 1 point is to be increased to 60 degrees celsius, the temperature needs to be heated to 35 degrees celsius, and if the temperature at 1 point is to be increased to 30 degrees celsius, the temperature needs to be heated to only 5 degrees celsius.
The working principle of the technical scheme has the beneficial effects that: because each data sub-feature is not an independent feature and needs to be synchronously analyzed with other features of the same type to monitor the designated equipment, sampling sub-data is divided into a plurality of regional sub-data according to data nodes, the regional sub-data is classified according to data attributes to obtain a plurality of data classes, the data in the same data class is logically analyzed to obtain corresponding logic relations, constraint information among different regional sub-data is determined according to the logic relations, and accordingly the corresponding data sub-features are built by combining data volumes of the data sub-features, in this way, in the process of building the data sub-features, not only the data volume of the data per se, but also the constraint information among different data are considered, and therefore the multi-angle effective data sub-features are built.
Example 9
On the basis of embodiment 3, the early warning system for monitoring the operation condition of the device, the sampling unit includes:
the first sampling subunit is used for sampling the target data and determining a plurality of sampling points;
the second sampling subunit is used for respectively acquiring first data corresponding to each sampling point, respectively establishing a corresponding data clock for each first data, and establishing a clock tree according to the data clock;
the third sampling subunit is used for respectively analyzing each clock tree, extracting clock empty branches on each clock tree, acquiring abnormal sampling points corresponding to the clock empty branches, acquiring adjacent sampling points corresponding to the abnormal sampling points, and resampling in the range of the adjacent sampling points to obtain corresponding second data;
and the fourth sampling subunit is used for replacing the corresponding first data by the second data to obtain sampling sub-data corresponding to each sampling point.
In this example, the first data represents data acquired at a sampling point when the first sampling is performed;
in this example, the data clock indicates a manner in which data in the first data is presented in a number by means of a circular chart;
in this example, the clock tree represents a presentation manner in which the data clocks of all the first data are ordered according to the binary tree format;
in this example, a clock empty branch represents a branch in the clock tree that does not contain data;
in this example, the abnormal sampling point represents a sampling point of sampling failure;
in this example, the second data represents data collected at the sampling point only when the second sampling is performed.
The working principle of the technical scheme has the beneficial effects that: in order to ensure the validity of the sampling sub-data, the first writing corresponding to the sampling point is firstly subjected to clock analysis, the first data is subjected to integral analysis by using a clock tree, then the validity of the first data is determined according to an analysis result, the sampling point is selected again for sampling when the first data is invalid, then the collected second data is replaced with the corresponding first data, finally the sampling sub-data is obtained, the data condition on each branch can be intuitively displayed in a clock tree mode, clock empty branches can be rapidly positioned, the supplementary sampling speed is accelerated, the validity of the sampling sub-data is ensured, and the persuasion of the monitoring result is improved.
Example 10
On the basis of embodiment 9, the early warning system for monitoring the operation condition of the device further includes:
when the number of the abnormal sampling points in the target data is larger than the preset number, determining that the appointed equipment is in an abnormal working state, generating early warning information and transmitting the early warning information to the appointed terminal for display.
The working principle of the technical scheme has the beneficial effects that: when the number of abnormal sampling points in the target data is excessive, the fact that most devices in the designated equipment are in an abnormal working state is indicated, and in order to quickly inform related personnel and avoid delay time, early warning information is directly generated and transmitted to the designated terminal for display, and the related personnel are reminded of timely performing fault investigation.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, it is intended that the present invention also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
Claims (8)
1. An early warning system for monitoring the operation condition of equipment, which is characterized by comprising:
the acquisition module is used for acquiring working data of the appointed equipment;
the processing module is used for preprocessing the working data to obtain target data and obtaining data characteristics of the target data;
the modeling module is used for inputting the data characteristics into a preset equipment monitoring model to perform state monitoring and generating a monitoring report;
the execution module is used for analyzing the monitoring report to obtain the running state of the appointed equipment, and generating early warning information to be transmitted to the appointed terminal for display when the running state of the appointed equipment is abnormal;
the processing module comprises:
the preprocessing unit is used for carrying out noise reduction and filtering processing on the working data to generate target data;
the sampling unit is used for sampling the target data to obtain a plurality of sampling points, and sampling sub-data corresponding to each sampling point is respectively obtained;
the analysis unit is used for respectively carrying out data division on each sampling sub-data to obtain a plurality of regional sub-data, and respectively obtaining data sub-features corresponding to each regional sub-data;
the integration unit is used for acquiring characteristic influence parameters among different data sub-characteristics, respectively correcting each data sub-characteristic according to the characteristic influence parameters to obtain a characteristic range corresponding to each data sub-characteristic, establishing the data characteristic of the target data according to a plurality of data sub-characteristics corresponding to the same sampling point, and establishing a target characteristic range according to the characteristic range corresponding to each data sub-characteristic;
the analysis unit includes:
the first analysis subunit is used for searching data nodes contained in each sampling sub-data respectively, and dividing the corresponding sampling sub-data into a plurality of regional sub-data according to the positions of the data nodes in the sampling sub-data;
the second analysis subunit is used for respectively acquiring the data attribute corresponding to each region sub-data corresponding to the same data sampling sub-data, and recording the target region sub-data with the same data attribute corresponding to each sampling sub-data as the same data class;
the third analysis subunit is used for respectively carrying out logic analysis on each data class to obtain logic relations among the sub-data in different areas in the same data class, and constraint information among the sub-data in different areas in the same data class is obtained according to the logic relations;
and the fourth analysis subunit is used for respectively acquiring the data quantity corresponding to each region sub-data and combining the constraint information to obtain the data sub-feature corresponding to each region sub-data.
2. The system of claim 1, wherein the acquisition module comprises:
the first acquisition unit is used for acquiring temperature data in the working process of the appointed equipment;
the second acquisition unit is used for acquiring power consumption data in the working process of the appointed equipment;
the fourth acquisition unit is used for acquiring amplitude data in the working process of the appointed equipment;
the fifth acquisition unit is used for acquiring sound data in the working process of the appointed equipment;
and the data synchronization unit is used for carrying out time synchronization on the temperature data, the power consumption data, the amplitude data and the sound data according to the corresponding acquisition time of each acquisition unit, and establishing working data.
3. The system for early warning of equipment operation monitoring of claim 1, wherein the processing module further comprises:
and the comparison unit is used for judging whether the data characteristics are in the target characteristic range, and if the data characteristics are not in the target characteristic range, the abnormal operation of the designated equipment is not determined, and abnormal information is generated and transmitted to the designated terminal for display.
4. The system of claim 1, wherein the modeling module comprises:
the preparation unit is used for acquiring the historical data of the appointed equipment, acquiring the historical characteristics of the appointed equipment and acquiring a preset equipment monitoring model corresponding to the appointed equipment;
the correction unit is used for respectively acquiring the historical time corresponding to each historical feature, establishing a historical feature sequence according to the sequence of the historical time, and sequentially correcting a preset equipment monitoring model by utilizing each historical feature in the historical feature sequence to obtain a current equipment monitoring model;
the training unit is used for carrying out state training on the data characteristic data in the current equipment monitoring model to obtain a training model;
the monitoring unit is used for analyzing the training model to obtain a plurality of model features, obtaining a plurality of state data of the appointed equipment according to the model features, and building a monitoring report according to the state data.
5. The system for early warning of equipment operation monitoring of claim 4, wherein the modeling module further comprises:
the evaluation unit is used for respectively acquiring a correction result corresponding to each correction in the process of correcting the preset equipment monitoring model by using the historical characteristic sequence and establishing a correction result sequence;
analyzing the correction result sequence to obtain correction amounts between adjacent correction results, and evaluating the loss amount of the designated equipment according to the correction amounts to obtain the loss trend of the designated equipment;
and evaluating the residual service life of the appointed equipment according to the loss trend, and generating feedback information to be transmitted to the appointed terminal for display when the residual service life is smaller than the preset service life.
6. The system of claim 1, wherein the execution module comprises:
the execution unit is used for obtaining the running state of the appointed equipment according to the monitoring report;
the judging unit is used for judging whether the running state belongs to an abnormal state or not;
the early warning unit is used for acquiring the state difference between the running state and the normal state when the running state belongs to the abnormal state, and establishing early warning information according to the state difference;
and the transmission unit is used for transmitting the early warning information to a designated terminal for display.
7. The system for early warning of equipment operation monitoring according to claim 1, wherein the sampling unit comprises:
the first sampling subunit is used for sampling the target data and determining a plurality of sampling points;
the second sampling subunit is used for respectively acquiring first data corresponding to each sampling point, respectively establishing a corresponding data clock for each first data, and establishing a clock tree according to the data clock;
the third sampling subunit is used for respectively analyzing each clock tree, extracting clock empty branches on each clock tree, acquiring abnormal sampling points corresponding to the clock empty branches, acquiring adjacent sampling points corresponding to the abnormal sampling points, and resampling in the range of the adjacent sampling points to obtain corresponding second data;
and the fourth sampling subunit is used for replacing the corresponding first data by the second data to obtain sampling sub-data corresponding to each sampling point.
8. The system for early warning of equipment operation monitoring of claim 7, further comprising:
when the number of the abnormal sampling points in the target data is larger than the preset number, determining that the appointed equipment is in an abnormal working state, generating early warning information and transmitting the early warning information to the appointed terminal for display.
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