CN110334816B - Industrial equipment detection method, device, equipment and readable storage medium - Google Patents
Industrial equipment detection method, device, equipment and readable storage medium Download PDFInfo
- Publication number
- CN110334816B CN110334816B CN201910630366.XA CN201910630366A CN110334816B CN 110334816 B CN110334816 B CN 110334816B CN 201910630366 A CN201910630366 A CN 201910630366A CN 110334816 B CN110334816 B CN 110334816B
- Authority
- CN
- China
- Prior art keywords
- model
- target
- training
- industrial equipment
- parameters
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N20/00—Machine learning
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/30—Computing systems specially adapted for manufacturing
Abstract
The application discloses an industrial equipment detection method, which comprises the following steps: acquiring a target machine learning algorithm and model parameters selected by a user based on a human-computer interaction interface, wherein the model parameters are parameters involved in training a model by utilizing the target machine learning algorithm; obtaining a target model for analyzing state parameters of the industrial equipment by utilizing a target machine learning algorithm and model parameter training; and inputting the state parameters of the target industrial equipment into the target model, and outputting the detection result of the target industrial equipment. According to the scheme, the state parameters of the industrial equipment are analyzed by using the model obtained through training of the machine learning algorithm, so that the detection efficiency of the industrial equipment can be improved, and the accuracy and the comprehensiveness of a detection result can be improved. Meanwhile, a user freely selects a machine learning algorithm and model parameters of the training model, so that the flexibility of model training is improved, and the technical requirement of training the model is reduced. The industrial equipment detection device, the industrial equipment detection equipment and the readable storage medium have the technical effects.
Description
Technical Field
The present disclosure relates to the field of data analysis technologies, and in particular, to an industrial device detection method, an industrial device detection apparatus, and a readable storage medium.
Background
In the prior art, in order to detect the operating state of an industrial plant, it is necessary to analyze and detect a state parameter of the industrial plant. Typically, the operating state of the industrial plant is determined by detecting and calculating these state parameters one by experienced plant operators.
However, since the industrial devices in the internet of things are numerous and the state parameters of different industrial devices are various, the manual detection and calculation are inevitably inefficient. Meanwhile, the state parameters can be detected one by one manually, and global consideration can not be carried out on all the state parameters of one industrial device, so that the existing detection method has obvious unilateral performance. The manual detection is inevitably overlooked, so that the accuracy and precision of the detection result cannot be ensured by the existing detection method.
Therefore, how to improve the detection efficiency of industrial equipment and the accuracy of the detection result is a problem that needs to be solved by those skilled in the art.
Disclosure of Invention
In view of the foregoing, it is an object of the present application to provide an industrial equipment detection method, apparatus, device and readable storage medium, so as to improve the detection efficiency of the industrial equipment and the accuracy of the detection result. The specific scheme is as follows:
in a first aspect, the present application provides an industrial equipment detection method, comprising:
acquiring a target machine learning algorithm and model parameters selected by a user based on a human-computer interaction interface, wherein the model parameters are parameters involved in training a model by utilizing the target machine learning algorithm;
obtaining a target model for analyzing state parameters of the industrial equipment by utilizing a target machine learning algorithm and model parameter training;
and inputting the state parameters of the target industrial equipment into the target model, and outputting the detection result of the target industrial equipment.
Preferably, obtaining a target model for analyzing state parameters of an industrial device using a target machine learning algorithm and model parameter training comprises:
acquiring training state parameters for training a target model;
preprocessing the training state parameters according to a preprocessing method selected by a user based on a man-machine interaction interface;
processing the preprocessed training state parameters by using a target machine learning algorithm and model parameters until a model meeting preset requirements is obtained;
and determining the model meeting the preset requirements as a target model.
Preferably, the method further comprises:
in the training process of the target model, the updated value of the model parameter selected by the user based on the man-machine interaction interface is obtained, and the target model is trained by utilizing the updated value and a target machine learning algorithm.
Preferably, after determining the model meeting the preset requirement as the target model, the method further comprises:
and evaluating the training process of the target model according to a preset evaluation index to obtain an evaluation result, and visually displaying the evaluation result.
Preferably, the method further comprises the steps of:
and acquiring the state parameters of the target industrial equipment selected by the user based on the man-machine interaction interface.
Preferably, the method further comprises:
if a user selects a plurality of target machine learning algorithms and model parameters corresponding to each target machine learning algorithm based on the human-computer interaction interface, respectively training to obtain a target model corresponding to each target machine learning algorithm;
inputting the test state parameters into each target model respectively, and outputting a test result corresponding to each target model;
determining the test result with highest accuracy from all the test results;
and inputting the state parameters of the target industrial equipment into a target model corresponding to the test result with highest accuracy, and outputting the detection result of the target industrial equipment.
Preferably, after outputting the detection result of the target industrial device, the method further comprises:
and visually displaying the detection result.
In a second aspect, the present application provides an industrial equipment detection device comprising:
the acquisition module is used for acquiring a target machine learning algorithm and model parameters selected by a user based on a human-computer interaction interface, wherein the model parameters are parameters involved in training a model by using the target machine learning algorithm;
the training module is used for training and obtaining a target model for analyzing the state parameters of the industrial equipment by utilizing a target machine learning algorithm and model parameters;
and the detection module is used for inputting the state parameters of the target industrial equipment into the target model and outputting the detection result of the target industrial equipment.
In a third aspect, the present application provides an industrial equipment detection device comprising:
a memory for storing a computer program;
and a processor for executing the computer program to implement the industrial equipment detection method disclosed above.
In a fourth aspect, the present application provides a readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the industrial device detection method disclosed previously.
According to the scheme, the application provides an industrial equipment detection method, which comprises the following steps of: acquiring a target machine learning algorithm and model parameters selected by a user based on a human-computer interaction interface, wherein the model parameters are parameters involved in training a model by utilizing the target machine learning algorithm; obtaining a target model for analyzing state parameters of the industrial equipment by utilizing a target machine learning algorithm and model parameter training; and inputting the state parameters of the target industrial equipment into the target model, and outputting the detection result of the target industrial equipment.
In the application, when the operation state of the industrial equipment needs to be detected, the state parameters of the industrial equipment are input into the target model obtained through training, and the detection result of the industrial equipment can be output. The state parameters of the industrial equipment can be analyzed by utilizing the model obtained through training of the machine learning algorithm, so that the analysis efficiency, the accuracy and the comprehensiveness of the analysis result are improved, and the detection efficiency of the industrial equipment, the accuracy and the comprehensiveness of the detection result are also improved. Meanwhile, the user can freely select a machine learning algorithm and model parameters for training the model, so that the flexibility of model training is improved, the technical requirement for training the model is reduced, and the interaction process between model training and the user is increased, so that the model obtained through training is closer to the will of the user, and has personalized characteristics.
Correspondingly, the industrial equipment detection device and the readable storage medium have the technical effects.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings that are required to be used in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are only embodiments of the present application, and that other drawings may be obtained according to the provided drawings without inventive effort to a person skilled in the art.
FIG. 1 is a flow chart of a first industrial equipment detection method disclosed herein;
FIG. 2 is a detailed flow chart of S102 in FIG. 1;
FIG. 3 is a schematic diagram of an evaluation index disclosed in the present application;
FIG. 4 is a schematic diagram of a prediction index disclosed in the present application;
FIG. 5 is a flow chart of a second industrial equipment detection method disclosed herein;
FIG. 6 is a schematic diagram of an industrial equipment detection device disclosed herein;
fig. 7 is a schematic diagram of an industrial equipment detection device disclosed in the present application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are only some, but not all, of the embodiments of the present application. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are within the scope of the present disclosure.
At present, because the industrial equipment in the Internet of things is numerous, and the state parameters of different industrial equipment are various, the manual detection and calculation are inevitably low in efficiency. Meanwhile, the state parameters can be detected one by one manually, and all the state parameters of one industrial device cannot be globally considered, so that the existing detection method has one-sided property. The manual detection is inevitably overlooked, so that the accuracy and precision of the detection result cannot be ensured by the existing detection method. Therefore, the application provides an industrial equipment detection scheme which can improve the detection efficiency of industrial equipment and the accuracy and the comprehensiveness of detection results.
Referring to fig. 1, an embodiment of the present application discloses a first industrial equipment detection method, including:
s101, acquiring a target machine learning algorithm and model parameters selected by a user based on a human-computer interaction interface, wherein the model parameters are parameters involved in training a model by using the target machine learning algorithm;
specifically, the target machine learning algorithm includes: a ridge regression algorithm, a logarithmic probability regression algorithm, a support vector machine algorithm, a random forest algorithm, a long-term and short-term memory algorithm, a neural network algorithm and the like.
S102, obtaining a target model for analyzing state parameters of industrial equipment by utilizing a target machine learning algorithm and model parameter training;
s103, inputting the state parameters of the target industrial equipment into the target model, and outputting the detection result of the target industrial equipment.
In this embodiment, the method further includes, before inputting the state parameter of the target industrial device into the target model and outputting the detection result of the target industrial device: and acquiring the state parameters of the target industrial equipment selected by the user based on the man-machine interaction interface. That is, the user can freely select the state parameters of the industrial equipment to be analyzed and detected based on the man-machine interaction interface, and the state parameters to be analyzed can be one or more.
When the industrial device is a compressor, the state parameters thereof may include: operating temperature, gas pressure, current, voltage, operating frequency, etc. Then when the user selects the state parameters, only the operating temperature may be selected, or a plurality of state parameters such as the operating temperature, the gas pressure, the current, the voltage, and the operating frequency may be simultaneously selected.
In this embodiment, after outputting the detection result of the target industrial device, the method further includes: and visually displaying the detection result. The visual display of the detection result can enable a user to know the running state of industrial equipment in time. Of course, the detection result can also be sent to the mailbox of the operation and maintenance technician according to the preset mailbox address so as to be checked by the operation and maintenance technician.
Referring to fig. 2, fig. 2 is a flowchart illustrating refinement of S102 in fig. 1, and the refinement step of S102 in fig. 1 includes:
s201, acquiring training state parameters for training a target model;
s202, preprocessing training state parameters according to a preprocessing method selected by a user based on a human-computer interaction interface;
specifically, the method for preprocessing the training state parameters at least comprises the following steps: mosaic processing, data aggregation, missing value processing, box-Cox transformation. Wherein, since the status parameters of the industrial equipment are generally fragmented, merging by "mosaic processing" is required; meanwhile, the acquisition time of the state parameters is uncertain, and the data aggregation can enable the state parameters to be aggregated according to time; the missing value processing is to complement missing state parameters by using average value, median value, fractional number, mode number, random number and the like; box-Cox transformation can make the distribution of state parameters centralized, and avoid larger bias. The method of preprocessing further comprises: normalization processing, deletion of redundant parameters, and the like.
The device for collecting the status parameters of the industrial device may be an adapter in the industrial field.
S203, processing the preprocessed training state parameters by using a target machine learning algorithm and model parameters until a model meeting preset requirements is obtained;
s204, determining the model meeting the preset requirements as a target model.
In the training process of the target model, a user can flexibly adjust the value of the model parameter. Namely: in the training process of the target model, the updated value of the model parameter selected by the user based on the human-computer interaction interface can be obtained, so that the target model is trained by using the updated value and a target machine learning algorithm.
In this embodiment, after determining the model meeting the preset requirement as the target model, the method further includes: and evaluating the training process of the target model according to a preset evaluation index to obtain an evaluation result, and visually displaying the evaluation result. The preset evaluation index can be seen in fig. 3.
In fig. 3, the evaluation index includes: accuracy of algorithm function implementation, accuracy of code implementation, influence of objective function, influence of training data set, influence of software and hardware platform dependence and influence of environment data. The evaluation indexes include more detailed indexes, refer to fig. 3, and are not described herein. Meanwhile, fig. 3 can also be regarded as an evaluation result, and through evaluation of each evaluation index, the actual operation condition of the current model can be seen, specifically please refer to the implementation stage and the operation stage in fig. 3.
It should be noted that, before training the model, the model may also be estimated based on the evaluation index in fig. 3, specifically please refer to fig. 4. The demand phase and the design phase in fig. 4 are the predictions of the model. Wherein the explanations regarding "," -and "-" in fig. 3 still apply in fig. 4.
The effect of the model can also be evaluated using the evaluation index in fig. 3 during the training of the model. Specifically, the preset test state parameters are input into the current model to obtain an evaluation result of the current model, the model structure is corrected according to the evaluation result, and the training process of the model is continued until the model meeting the preset requirements is obtained.
In this embodiment, when the operation state of the industrial equipment needs to be detected, the state parameters of the industrial equipment are input into the target model obtained by training, and the detection result of the industrial equipment can be output. The state parameters of the industrial equipment can be analyzed by utilizing the model obtained through training of the machine learning algorithm, so that the analysis efficiency, the accuracy and the comprehensiveness of the analysis result are improved, and the detection efficiency of the industrial equipment, the accuracy and the comprehensiveness of the detection result are also improved. Meanwhile, the user can freely select a machine learning algorithm and model parameters for training the model, so that the flexibility of model training is improved, the technical requirement for training the model is reduced, and the interaction process between model training and the user is increased, so that the model obtained through training is closer to the will of the user, and has personalized characteristics.
Referring to fig. 5, an embodiment of the present application discloses a second industrial equipment detection method, including:
s501, acquiring a plurality of target machine learning algorithms selected by a user based on a human-computer interaction interface and model parameters corresponding to each target machine learning algorithm;
s502, respectively training to obtain a target model corresponding to each target machine learning algorithm;
s505, respectively inputting the test state parameters into each target model, and outputting a test result corresponding to each target model;
s504, determining a test result with highest accuracy from all test results;
s505, inputting the state parameters of the target industrial equipment into a target model corresponding to the test result with the highest accuracy, and outputting the detection result of the target industrial equipment.
The embodiment can be realized based on a cloud platform or a server. In this embodiment, the user may freely select a plurality of machine learning algorithms and model parameters corresponding to each target machine learning algorithm, so as to obtain a plurality of target models; and then determining the target model with highest accuracy from the plurality of target models to analyze the state parameters of the target industrial equipment, so that the detection result of the target industrial equipment can be further improved.
According to the method provided by the embodiment, an interactive analysis system can be realized, and a user can participate in the training process of the model based on the human-computer interaction interface of the system.
It should be noted that other implementation steps in the present embodiment are the same as or similar to those in the above embodiment, so that the description of the present embodiment is omitted here.
In this embodiment, when the operation state of the industrial equipment needs to be detected, the state parameters of the industrial equipment are input into the target model obtained by training, and the detection result of the industrial equipment can be output. The state parameters of the industrial equipment can be analyzed by utilizing the model obtained through training of the machine learning algorithm, so that the analysis efficiency, the accuracy and the comprehensiveness of the analysis result are improved, and the detection efficiency of the industrial equipment, the accuracy and the comprehensiveness of the detection result are also improved. Meanwhile, the user can freely select a machine learning algorithm and model parameters for training the model, so that the flexibility of model training is improved, the technical requirement for training the model is reduced, and the interaction process between model training and the user is increased, so that the model obtained through training is closer to the will of the user, and has personalized characteristics.
An industrial equipment detection device provided in the embodiments of the present application is described below, and an industrial equipment detection device described below and an industrial equipment detection method described above may be referred to with reference to each other.
Referring to fig. 6, an embodiment of the present application discloses an industrial equipment detecting device, including:
the obtaining module 601 is configured to obtain a target machine learning algorithm and model parameters selected by a user based on a human-computer interaction interface, where the model parameters are parameters involved in training a model by using the target machine learning algorithm;
a training module 602 for training to obtain a target model for analyzing state parameters of the industrial equipment using a target machine learning algorithm and model parameters;
the detection module 603 is configured to input a state parameter of the target industrial device into the target model, and output a detection result of the target industrial device.
In one embodiment, the training module comprises:
the acquisition unit is used for acquiring training state parameters for training the target model;
the preprocessing unit is used for preprocessing the training state parameters according to a preprocessing method selected by a user based on the human-computer interaction interface;
the training unit is used for processing the preprocessed training state parameters by utilizing a target machine learning algorithm and model parameters until a model meeting preset requirements is obtained;
and the determining unit is used for determining the model meeting the preset requirements as a target model.
In one specific embodiment, the method further comprises:
and the updated value acquisition module is used for acquiring the updated value of the model parameter selected by the user based on the man-machine interaction interface in the training process of the target model, and training the target model by utilizing the updated value and a target machine learning algorithm.
In one specific embodiment, the method further comprises:
the evaluation module is used for evaluating the training process of the target model according to a preset evaluation index, obtaining an evaluation result and visually displaying the evaluation result.
In one specific embodiment, the method further comprises:
and the state parameter acquisition module is used for acquiring the state parameters of the target industrial equipment selected by the user based on the man-machine interaction interface.
In one specific embodiment, the method further comprises:
the parallel training module is used for respectively training and obtaining a target model corresponding to each target machine learning algorithm if a user selects a plurality of target machine learning algorithms and model parameters corresponding to each target machine learning algorithm based on the human-computer interaction interface;
the test module is used for inputting the test state parameters into each target model respectively and outputting a test result corresponding to each target model;
the determining module is used for determining the test result with highest accuracy from all the test results;
and the high-precision detection module is used for inputting the state parameters of the target industrial equipment into the target model corresponding to the test result with the highest precision and outputting the detection result of the target industrial equipment.
In one specific embodiment, the method further comprises:
and the display module is used for visually displaying the detection result.
The more specific working process of each module and unit in this embodiment may refer to the corresponding content disclosed in the foregoing embodiment, and will not be described herein.
It can be seen that this embodiment provides an industrial equipment detection device, comprising: the device comprises an acquisition module, a training module and a detection module. Firstly, an acquisition module acquires a target machine learning algorithm and model parameters selected by a user based on a human-computer interaction interface, wherein the model parameters are parameters involved in training a model by using the target machine learning algorithm; the training module is used for training and obtaining a target model for analyzing the state parameters of the industrial equipment by utilizing a target machine learning algorithm and model parameters; and finally, the detection module inputs the state parameters of the target industrial equipment into the target model and outputs the detection result of the target industrial equipment. Therefore, the modules work separately, and each module performs its own role, so that the detection efficiency of the industrial equipment, and the accuracy and the comprehensiveness of the detection result are improved.
An industrial equipment detection device provided in the embodiments of the present application is described below, and an industrial equipment detection device described below and an industrial equipment detection method and apparatus described above may be referred to with reference to each other.
Referring to fig. 7, an embodiment of the present application discloses an industrial equipment detection device, including:
a memory 701 for storing a computer program;
a processor 702 for executing the computer program to implement the method disclosed in any of the embodiments above.
The following describes a readable storage medium provided in the embodiments of the present application, and the readable storage medium described below and the method, apparatus and device for detecting an industrial device described above may be referred to with each other.
A readable storage medium storing a computer program, wherein the computer program when executed by a processor implements the industrial equipment detection method disclosed in the foregoing embodiments. For specific steps of the method, reference may be made to the corresponding contents disclosed in the foregoing embodiments, and no further description is given here.
Reference to "first," "second," "third," "fourth," etc. (if present) herein is used to distinguish similar objects from each other and does not necessarily describe a particular order or sequence. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments described herein may be implemented in other sequences than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed or inherent to such process, method, or apparatus.
It should be noted that the description herein of "first," "second," etc. is for descriptive purposes only and is not to be construed as indicating or implying a relative importance or implying an indication of the number of technical features being indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include at least one such feature. In addition, the technical solutions of the embodiments may be combined with each other, but it is necessary to base that the technical solutions can be realized by those skilled in the art, and when the technical solutions are contradictory or cannot be realized, the combination of the technical solutions should be regarded as not exist and not within the protection scope of the present application.
In this specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different point from other embodiments, so that the same or similar parts between the embodiments are referred to each other.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. The software modules may be disposed in Random Access Memory (RAM), memory, read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of readable storage medium known in the art.
The principles and embodiments of the present application are described herein with specific examples, the above examples being provided only to assist in understanding the methods of the present application and their core ideas; meanwhile, as those skilled in the art will have modifications in the specific embodiments and application scope in accordance with the ideas of the present application, the present description should not be construed as limiting the present application in view of the above.
Claims (8)
1. An industrial equipment detection method, comprising:
acquiring a target machine learning algorithm and model parameters selected by a user based on a human-computer interaction interface, wherein the model parameters are parameters involved in training a model by utilizing the target machine learning algorithm;
training to obtain a target model for analyzing state parameters of industrial equipment by utilizing the target machine learning algorithm and the model parameters;
inputting state parameters of target industrial equipment into the target model, and outputting detection results of the target industrial equipment;
wherein said training with said target machine learning algorithm and said model parameters to obtain a target model for analyzing state parameters of an industrial device comprises:
acquiring training state parameters for training the target model;
preprocessing the training state parameters according to a preprocessing method selected by a user based on a man-machine interaction interface;
processing the preprocessed training state parameters by using the target machine learning algorithm and the model parameters until a model meeting preset requirements is obtained;
determining the model meeting the preset requirements as the target model;
and in the training process of the target model, acquiring an updated value of the model parameter selected by a user based on a human-computer interaction interface, and training the target model by using the updated value and the target machine learning algorithm.
2. The industrial equipment testing method according to claim 1, wherein after determining the model meeting the preset requirements as the target model, further comprising:
and evaluating the training process of the target model according to a preset evaluation index to obtain an evaluation result, and visually displaying the evaluation result.
3. The industrial equipment detection method according to claim 1, wherein before inputting the state parameter of the target industrial equipment into the target model and outputting the detection result of the target industrial equipment, further comprising:
and acquiring the state parameters of the target industrial equipment selected by the user based on the man-machine interaction interface.
4. A method of industrial equipment detection according to any one of claims 1 to 3, further comprising:
if a user selects a plurality of target machine learning algorithms and model parameters corresponding to each target machine learning algorithm based on the human-computer interaction interface, respectively training to obtain a target model corresponding to each target machine learning algorithm;
inputting the test state parameters into each target model respectively, and outputting a test result corresponding to each target model;
determining the test result with highest accuracy from all the test results;
and inputting the state parameters of the target industrial equipment into a target model corresponding to the test result with the highest accuracy, and outputting the detection result of the target industrial equipment.
5. The industrial equipment inspection method according to claim 1, wherein after outputting the inspection result of the target industrial equipment, further comprising:
and visually displaying the detection result.
6. An industrial equipment detection device, comprising:
the acquisition module is used for acquiring a target machine learning algorithm and model parameters selected by a user based on a human-computer interaction interface, wherein the model parameters are parameters involved in training a model by utilizing the target machine learning algorithm;
the training module is used for training and obtaining a target model for analyzing the state parameters of the industrial equipment by utilizing the target machine learning algorithm and the model parameters;
the detection module is used for inputting the state parameters of the target industrial equipment into the target model and outputting the detection result of the target industrial equipment;
wherein, training module includes:
the acquisition unit is used for acquiring training state parameters for training the target model;
the preprocessing unit is used for preprocessing the training state parameters according to a preprocessing method selected by a user based on a human-computer interaction interface;
the training unit is used for processing the preprocessed training state parameters by utilizing the target machine learning algorithm and the model parameters until a model meeting the preset requirements is obtained;
the determining unit is used for determining the model meeting the preset requirements as the target model;
the apparatus further comprises: and the updated value acquisition module is used for acquiring the updated value of the model parameter selected by the user based on the human-computer interaction interface in the training process of the target model, and training the target model by utilizing the updated value and the target machine learning algorithm.
7. An industrial equipment detection device, comprising:
a memory for storing a computer program;
a processor for executing the computer program to implement the industrial device detection method of any one of claims 1 to 5.
8. A readable storage medium for storing a computer program, wherein the computer program when executed by a processor implements the industrial device detection method according to any one of claims 1 to 5.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910630366.XA CN110334816B (en) | 2019-07-12 | 2019-07-12 | Industrial equipment detection method, device, equipment and readable storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910630366.XA CN110334816B (en) | 2019-07-12 | 2019-07-12 | Industrial equipment detection method, device, equipment and readable storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110334816A CN110334816A (en) | 2019-10-15 |
| CN110334816B true CN110334816B (en) | 2023-06-16 |
Family
ID=68146626
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910630366.XA Active CN110334816B (en) | 2019-07-12 | 2019-07-12 | Industrial equipment detection method, device, equipment and readable storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110334816B (en) |
Families Citing this family (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111177655B (en) * | 2019-12-30 | 2022-04-22 | 联想(北京)有限公司 | Data processing method and device and electronic equipment |
| CN111914789A (en) * | 2020-08-13 | 2020-11-10 | 杭州云笔智能科技有限公司 | Method and system for determining running state of target equipment |
| CN112508457A (en) * | 2020-12-25 | 2021-03-16 | 树根互联技术有限公司 | Data processing method and device, industrial equipment and storage medium |
| CN113359623B (en) * | 2021-05-08 | 2022-08-23 | 深圳有象智联科技有限公司 | Method and device for monitoring working state and computer readable storage medium |
| CN113589789A (en) * | 2021-07-28 | 2021-11-02 | 盛景智能科技(嘉兴)有限公司 | Equipment detection method and device |
| CN113779330B (en) * | 2021-09-13 | 2024-01-26 | 河海大学常州校区 | Ultrasonic detection process parameter intelligent computing system based on similarity measurement |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107992888A (en) * | 2017-11-29 | 2018-05-04 | 深圳市智物联网络有限公司 | The recognition methods of operation of industrial installation and server |
Family Cites Families (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20190102693A1 (en) * | 2017-09-29 | 2019-04-04 | Facebook, Inc. | Optimizing parameters for machine learning models |
| CN108875963A (en) * | 2018-06-28 | 2018-11-23 | 北京字节跳动网络技术有限公司 | Optimization method, device, terminal device and the storage medium of machine learning model |
| CN109325541A (en) * | 2018-09-30 | 2019-02-12 | 北京字节跳动网络技术有限公司 | Method and apparatus for training pattern |
| CN109754014B (en) * | 2018-12-29 | 2021-04-27 | 北京航天数据股份有限公司 | Industrial model training method, device, equipment and medium |
| CN109815991B (en) * | 2018-12-29 | 2021-02-19 | 北京城市网邻信息技术有限公司 | Training method and device of machine learning model, electronic equipment and storage medium |
-
2019
- 2019-07-12 CN CN201910630366.XA patent/CN110334816B/en active Active
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107992888A (en) * | 2017-11-29 | 2018-05-04 | 深圳市智物联网络有限公司 | The recognition methods of operation of industrial installation and server |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110334816A (en) | 2019-10-15 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110334816B (en) | Industrial equipment detection method, device, equipment and readable storage medium | |
| JP6978541B2 (en) | Computer implementation method, computer system and computer equipment to reduce dynamic deviation value bias | |
| CN110968985B (en) | Method and device for determining integrated circuit repair algorithm, storage medium and electronic equipment | |
| JP6467186B2 (en) | System and method for detecting, correcting, and inspecting bad data in a data stream | |
| WO2018052015A1 (en) | Analysis support device for system, analysis support method and program for system | |
| CN106598822B (en) | A kind of abnormal deviation data examination method and device for Capacity Assessment | |
| CN110941648A (en) | Abnormal data identification method, system and storage medium based on cluster analysis | |
| US20140188777A1 (en) | Methods and systems for identifying a precursor to a failure of a component in a physical system | |
| CN115409283A (en) | Equipment failure prediction method, equipment failure prediction device, equipment and storage medium | |
| CN113723861A (en) | Abnormal electricity consumption behavior detection method and device, computer equipment and storage medium | |
| CN111612149A (en) | Main network line state detection method, system and medium based on decision tree | |
| JP2019197245A (en) | Information processing apparatus, quality related formula generation method and quality related formula generation program | |
| CN111025041A (en) | Electric vehicle charging pile monitoring method and system, computer equipment and medium | |
| CN112183555B (en) | Method and system for detecting welding quality, electronic device and storage medium | |
| CN112906896A (en) | Information processing method and device and computing equipment | |
| US11429909B2 (en) | Information-technology utilization evaluation device and information-technology utilization evaluation method | |
| KR102417675B1 (en) | Expression apparatus for level of disaster crisis, and control method thereof | |
| CN107121616B (en) | Method and device for fault positioning of intelligent instrument | |
| CN113093584B (en) | Welding abnormity detection method and device, computer equipment and storage medium | |
| CN115344495A (en) | Data analysis method and device for batch task test, computer equipment and medium | |
| JP6247777B2 (en) | Abnormality diagnosis apparatus and abnormality diagnosis method | |
| CN113887101A (en) | Visualization method and device of network model, electronic equipment and storage medium | |
| CN115372752A (en) | Fault detection method, device, electronic equipment and storage medium | |
| US20190095848A1 (en) | Action-information processing apparatus | |
| US11042786B2 (en) | Learning processing device, data analysis device, analytical procedure selection method, and recording medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |