CN117932972B - Visual modeling platform and method applied to equipment state algorithm model based on WEB - Google Patents
Visual modeling platform and method applied to equipment state algorithm model based on WEB Download PDFInfo
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Abstract
The invention discloses a visual modeling platform and a method applied to a device state algorithm model based on WEB, wherein the platform comprises the following components: the operator library is used for storing operators for performing the visual modeling module; and a visual modeling module: constructing a visual model of an equipment state algorithm through operator, flow lines and operator node configuration of an operator library; the model management module is used for managing the constructed visual model, editing, publishing and canceling the visual model; the model operation module is used for operating the model issued by the model management module, inputting the operation measurement data of the equipment into the model, acquiring the equipment state according to the model calculation result, and reporting the equipment state. The invention flexibly combines operators in the operator library, can quickly create a large number of state models conforming to specific equipment and services, reduces the difficulty and complexity of model creation, and improves the modeling efficiency.
Description
Technical Field
The invention relates to equipment maintenance technology, in particular to a visual modeling platform and method applied to an equipment state algorithm model based on WEB.
Background
With the development of industrial internet, big data and artificial intelligence, the data on the equipment is collected by related technologies, and the remote and intelligent operation and maintenance of the equipment are possible. And particularly, the collected data is modeled, and more information on the state of the equipment is found, so that the data has greater value. However, as the number of equipment models increases, the state of the same equipment also becomes complex and changeable along with the changes of factors such as service life, operation working conditions, environment and the like, and the state of the equipment is judged by a fixed model to be more and more difficult to realize. According to the invention, different models are flexibly established to judge the state of the equipment according to the conditions of different equipment, different states and the like in a visual modeling mode, so that the flexibility and the customizable characteristic are increased; meanwhile, the visual drag modeling greatly simplifies the creation difficulty of the model and reduces the time and labor cost.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a visual modeling platform and a visual modeling method applied to a device state algorithm model based on WEB.
The technical scheme adopted for solving the technical problems is as follows: a visual modeling method applied to a device state algorithm model based on WEB comprises the following steps:
1) Collecting basic information and operation measurement data of equipment, and judging the preliminary state of the equipment according to the basic information of the equipment; the basic information comprises the type of the equipment, the operation time of the equipment and the maintenance times of the equipment;
2) Configuring a visual model, and dragging operators usable for establishing the model to canvas according to the equipment and the preliminary state of the equipment;
The operator comprises: algorithm sets, model sets, rule operations and logic operations;
3) The operator nodes are configured, measuring points and corresponding characteristic values of equipment are selected from the running measurement data to serve as operator inputs, and other parameters of the operators are configured;
The measuring points of the equipment are preselected equipment operation measuring points, and the measuring points comprise equipment surfaces and selected equipment components; the characteristic values corresponding to the measuring points of the equipment are characteristic values extracted from vibration signals, temperature values and noise signals collected at the measuring points of the equipment;
4) Connecting a plurality of operators by using a flow line according to input and output and a calculation flow to form a complete visual equipment state detection model;
5) Checking the validity of the created equipment state detection model, and if the equipment state detection model is valid, storing the model; the method comprises the following steps:
acquiring all combinations of a plurality of operators, which are connected by using a flow line according to the input and output conditions and the calculation flow, to form complete visual equipment state detection models as candidate models, and performing index verification on each candidate model;
Generating a training sample according to basic information of equipment, historical operation measurement data and corresponding equipment states, generating an initial equipment state detection result based on the training sample by a candidate model, comparing the initial equipment state detection result with the historical data, if the comparison consistency index of the initial equipment state detection result and the historical data is detected to be not met with a preset requirement (consistency index is lower than 90%), training the model based on the historical operation measurement data of the training sample and the corresponding equipment states, updating model parameters of the candidate model to obtain a candidate model with optimized parameters, generating an equipment state detection result based on the training sample by the candidate model, if the comparison consistency index of the equipment state detection result and the historical data is detected to be met with the requirement (consistency index is up to 90%), judging the model to be an effective model, and storing the model, otherwise discarding the candidate model;
The consistency index is the same as the result of the equipment state, and the equipment state comprises a normal running state and a fault state;
6) Releasing a model;
7) And inputting the operation measurement data of the equipment into a model, acquiring the equipment state according to the calculation result of the model, reporting the equipment state, and storing the model into a model set.
According to the scheme, the algorithm set comprises: data preprocessing algorithms, signal processing algorithms, vibration mechanism failure algorithms, statistical algorithms, machine learning algorithms, deep learning algorithms, rules and knowledge based algorithms.
According to the scheme, the model set is a verified equipment state detection model.
A visual modeling platform for a device state algorithm model based on WEB applications, comprising:
An operator library for storing operators for performing a visual modeling module, the operators comprising: algorithm sets, model sets, rule operations and logic operations;
and a visual modeling module: constructing a visual model of an equipment state algorithm through operator, flow lines and operator node configuration of an operator library;
comprising the following steps:
The visual model sub-module is configured and used for dragging operators which can be used for establishing the model to canvas according to the equipment and the preliminary state of the equipment;
The operator node configuration submodule is used for selecting a measuring point of the equipment and a corresponding characteristic value as operator input in running measurement data, and configuring other parameters of the operator;
The model building sub-module is used for connecting a plurality of operators by using flow lines according to the input and output conditions and the calculation flow to form a complete visual equipment state detection model;
the model management module is used for managing the constructed visual model, editing, publishing and canceling the visual model;
the model operation module is used for operating the model issued by the model management module, inputting the operation measurement data of the equipment into the model, acquiring the equipment state according to the model calculation result, and reporting the equipment state.
According to the scheme, the algorithm set comprises: data preprocessing algorithm, signal processing algorithm, vibration mechanism fault algorithm, statistical algorithm, machine learning algorithm, deep learning algorithm, algorithm based on rules and knowledge;
according to the scheme, the model set is a verified equipment state detection model.
The invention has the beneficial effects that:
According to the invention, operators in the operator library are flexibly combined, a large number of state models conforming to specific equipment and services can be quickly created, and the visual modeling of the dragging operators reduces the difficulty and complexity of model creation and improves the modeling efficiency; the accuracy of the model is improved through the verification function, and some basic errors of the model are avoided; the model creation difficulty is greatly simplified, and the time and labor cost are reduced.
Drawings
The invention will be further described with reference to the accompanying drawings and examples, in which:
FIG. 1 is a flow chart of a method of an embodiment of the present invention;
Fig. 2 is a schematic diagram of the platform operation of an embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the following examples in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
As shown in fig. 1, a visual modeling method applied to a device state algorithm model based on WEB includes:
1) Collecting basic information and operation measurement data of equipment, and judging the preliminary state of the equipment according to the basic information of the equipment; the basic information comprises the type of the equipment, the operation time of the equipment and the maintenance times of the equipment;
2) Configuring a visual model, and dragging operators usable for establishing the model to canvas according to the equipment and the preliminary state of the equipment;
The operator comprises: algorithm sets, model sets, rule operations and logic operations;
the algorithm set includes: data preprocessing algorithm, signal processing algorithm, vibration mechanism fault algorithm, statistical algorithm, machine learning algorithm, deep learning algorithm, algorithm based on rules and knowledge;
the model set is a verified equipment state detection model;
The canvas provides the following functions: zooming, dragging, toolbar (frame selection, copy, delete, full screen, display hidden operator node configuration sidebar, check, save, return, and download);
3) The operator nodes are configured, the measuring points and the corresponding characteristic values of the operation measurement data selection equipment are used as operator inputs, and other parameters of the operators are configured;
Selecting measuring points and corresponding characteristic values of equipment as an input data set of an operator, and initializing other parameters of the operator;
4) Connecting a plurality of operators by using a flow line according to input and output and a calculation flow to form a complete visual equipment state detection model;
5) Checking the validity of the created equipment state detection model, and if the equipment state detection model is valid, storing the model; the method comprises the following steps:
acquiring all combinations of a plurality of operators, which are connected by using a flow line according to the input and output conditions and the calculation flow, to form complete visual equipment state detection models as candidate models, and performing index verification on each candidate model;
In this embodiment, according to basic information of a device, historical operation measurement data and corresponding device states, a training sample is generated, an initial device state detection result is generated by a candidate model based on the training sample, the initial device state detection result is compared with the historical data, if the comparison consistency degree of the initial device state detection result and the historical data is detected to be not met by a preset requirement (consistency index is lower than 90%), the candidate model is trained based on the historical operation measurement data of the training sample and the corresponding device states, model parameters of the candidate model are updated, a candidate model with optimized parameters is obtained, the candidate model is used for generating a device state detection result based on the training sample, if the comparison consistency degree of the device state detection result and the historical data is detected to meet the requirement (consistency index is up to 90%), the candidate model is judged to be an effective model, and if the comparison consistency degree of the device state detection result and the historical data is not met by the preset requirement (consistency index is lower than 90%), otherwise, the candidate model is discarded;
The consistency index is the same as the result of the equipment state, and the equipment state comprises a normal running state and a fault state; the 90% threshold value of the consistency index is selected according to the actual application condition in the embodiment, and can be adjusted according to the application precision and the time requirement;
If the historical operation measurement data of the equipment E is Q (Q1, Q2, Q3, … …, qn), the corresponding equipment state is S (S1, S2, S3, … …, sn), wherein the value of si (i=1, 2,3 … … n) is 0 or 1,0 represents the normal operation state, and 1 represents the fault state; for the candidate model M1, inputting historical operation measurement data Q (Q1, Q2, Q3, … …, qn) of the equipment, obtaining an initial equipment state detection result S 0（s01,s02,s03,……,s0 n predicted by the candidate model M1, comparing n pieces of state data of the initial equipment state detection result with equipment states in the historical data, and if the same proportion of the states reaches 90% after the comparison, considering that the candidate model meets the preset requirement, judging the candidate model as an effective model and storing the effective model as a model to be distributed;
If the ratio of the same states is lower than 90% after comparison, setting the ratio of the same initial equipment state detection results predicted by the candidate model M1 as 40%, considering that the candidate model does not meet the preset requirement, training the candidate model based on the historical operation measurement data of the training sample and the corresponding equipment state, updating the model parameters of the candidate model, obtaining the candidate model after parameter optimization, predicting the equipment state detection results again, and if the comparison consistency degree of the equipment state detection prediction results and the historical data meets the requirement, storing the candidate model, otherwise discarding the candidate model;
6) Releasing a model;
7) And inputting the operation measurement data of the equipment into a model, acquiring the equipment state according to the calculation result of the model, reporting the equipment state, and storing the model into a model set.
A visual modeling platform applied to a device state algorithm model based on WEB, which adopts a B/S WEB framework, as shown in figure 2,
Comprising the following steps:
An operator library for storing operators for performing a visual modeling module, the operators comprising: algorithm sets, model sets, rule operations and logic operations;
the algorithm set includes: data preprocessing algorithm, signal processing algorithm, vibration mechanism fault algorithm, statistical algorithm, machine learning algorithm, deep learning algorithm, algorithm based on rules and knowledge;
the model set is a verified equipment state detection model;
The canvas provides the following functions: zooming, dragging, toolbar (frame selection, copy, delete, full screen, display hidden operator node configuration sidebar, check, save, return, and download);
and a visual modeling module: constructing a visual model of an equipment state algorithm through operator, flow lines and operator node configuration of an operator library;
comprising the following steps:
The visual model sub-module is configured and used for dragging operators which can be used for establishing the model to canvas according to the equipment and the preliminary state of the equipment;
The operator node configuration submodule is used for selecting a measuring point of the equipment and a corresponding characteristic value as operator input in running measurement data, and configuring other parameters of the operator;
The model building sub-module is used for connecting a plurality of operators by using flow lines according to the input and output conditions and the calculation flow to form a complete visual equipment state detection model;
the model management module is used for managing the constructed visual model, editing, publishing and canceling the visual model;
the model operation module is used for operating the model issued by the model management module, inputting the operation measurement data of the equipment into the model, acquiring the equipment state according to the model calculation result, and reporting the equipment state.
The invention realizes the diagnosis of the equipment state through visual modeling and the operation of the model, and carries out timely and reasonable early warning on the equipment state according to the diagnosis result.
It will be understood that modifications and variations will be apparent to those skilled in the art from the foregoing description, and it is intended that all such modifications and variations be included within the scope of the following claims.
Claims (8)
1. A visual modeling method applied to a device state algorithm model based on WEB is characterized by comprising the following steps:
1) Collecting basic information and operation measurement data of equipment, and judging the preliminary state of the equipment according to the basic information of the equipment; the basic information comprises the type of the equipment, the operation time of the equipment and the maintenance times of the equipment;
2) Configuring a visual model, and dragging operators usable for establishing the model to canvas according to the equipment and the preliminary state of the equipment;
The operator comprises: algorithm sets, model sets, rule operations and logic operations;
3) The operator nodes are configured, the measuring points and the corresponding characteristic values of the operation measurement data selection equipment are used as operator inputs, and other parameters of the operators are configured;
4) Connecting a plurality of operators by using a flow line according to input and output and a calculation flow to form a complete visual equipment state detection model;
5) Checking the validity of the created equipment state detection model, and if the equipment state detection model is valid, storing the model; the method comprises the following steps:
acquiring all combinations of a plurality of operators, which are connected by using a flow line according to the input and output conditions and the calculation flow, to form complete visual equipment state detection models as candidate models, and performing index verification on each candidate model;
6) Releasing a model;
7) And inputting the operation measurement data of the equipment into a model, acquiring the equipment state according to the calculation result of the model, reporting the equipment state, and storing the model into a model set.
2. The visual modeling method applied to a device state algorithm model based on WEB according to claim 1, wherein the algorithm set comprises: data preprocessing algorithms, signal processing algorithms, vibration mechanism failure algorithms, statistical algorithms, machine learning algorithms, deep learning algorithms, and algorithms based on rules and knowledge.
3. The visual modeling method applied to a device state algorithm model based on WEB according to claim 1, wherein the model set is a verified device state detection model.
4. A visual modeling platform applied to a device state algorithm model based on WEB, comprising:
An operator library for storing operators for performing a visual modeling module, the operators comprising: algorithm sets, model sets, rule operations and logic operations;
and a visual modeling module: constructing a visual model of an equipment state algorithm through operator, flow lines and operator node configuration of an operator library;
comprising the following steps:
The visual model sub-module is configured and used for dragging operators which can be used for establishing the model to canvas according to the equipment and the preliminary state of the equipment;
The operator node configuration submodule is used for selecting a measuring point of the equipment and a corresponding characteristic value as operator input in running measurement data, and configuring other parameters of the operator;
The model building sub-module is used for connecting a plurality of operators by using flow lines according to the input and output conditions and the calculation flow to form a complete visual equipment state detection model;
the model management module is used for managing the constructed visual model, editing, publishing and canceling the visual model;
the model operation module is used for operating the model issued by the model management module, inputting the operation measurement data of the equipment into the model, acquiring the equipment state according to the model calculation result, and reporting the equipment state.
5. The WEB-based visualization modeling platform for application to a device state algorithm model of claim 4, wherein the algorithm set comprises: data preprocessing algorithms, signal processing algorithms, vibration mechanism failure algorithms, statistical algorithms, machine learning algorithms, deep learning algorithms, and algorithms based on rules and knowledge.
6. The WEB-based visualization modeling platform for device state algorithm models of claim 4, wherein the set of models is a validated device state detection model.
7. An electronic device, the electronic device comprising:
At least one processor; and
A memory communicatively coupled to the at least one processor; wherein,
The memory stores a computer program executable by the at least one processor, the computer program being
The at least one processor executing to enable the at least one processor to perform the method of any one of claims 1 to 3.
8. A computer readable storage medium storing computer instructions for causing a processor to perform the method of any one of claims 1 to 3.
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