CN109710819A - A kind of model display method, apparatus, equipment and medium - Google Patents
A kind of model display method, apparatus, equipment and medium Download PDFInfo
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Abstract
This application provides a kind of model display method, apparatus, equipment and media, which comprises according to user demand, determines multiple computation models；According to the attribute information of each computation model, classification corresponding with each computation model is determined；For each categorization module in multiple default categorization modules in display platform, corresponding computation model of classifying corresponding with the categorization module is shown in the categorization module.
Description
Technical field
This application involves technical field of data processing, in particular to a kind of model display method, apparatus, equipment and
Medium.
Background technique
With the rapid development of industry internet, the knowledge base and model library of industrial circle, mechanism model, process flow etc.
The digitlization assets of enterprise increase at grade several levels.Currently in enterprise, go to manage mainly by the information system of each encouragement
These digitlization assets, or these digital assets are managed by experienced staff, each operation system data are each other
Separation, ununified system is managed collectively, more it is essential that allowing these digitized assets without suitable mode
The iteration of the technology succession and experience that become entire enterprise updates, and can not be formed and be recycled to digital resource, constantly play it
Market value.
Summary of the invention
In view of this, the application's is designed to provide a kind of model display method, apparatus, equipment and medium, for mentioning
For a kind of simple, convenient and fast model display method.
In a first aspect, the embodiment of the present application provides a kind of model display method, which comprises
According to user demand, multiple computation models are determined；
According to the attribute information of each computation model, classification corresponding with each computation model is determined；
For each categorization module in multiple default categorization modules in display platform, shows and be somebody's turn to do in the categorization module
The corresponding computation model of the corresponding classification of categorization module.
Optionally, described according to user demand, determine multiple computation models, comprising:
Multiple groups sample corpus is determined from preset corpus data library according to user demand；
From the sample corpus, multiple preset models to construct in advance determine corresponding sample corpus；
For each preset model, the corresponding sample corpus of the preset model is input to the preset model and is trained,
Obtain completing the preset model of training；
Each preset model for completing training is determined as the multiple computation model.
Optionally, it is directed to each preset model described, the corresponding sample corpus of the preset model is input to this and is preset
When model is trained, further includes:
The physical training condition and training result of each preset model are monitored, the training result includes the preset model
Parameter information；
Based on the physical training condition and the training result, monitoring journal is generated.
It is optionally, described to show corresponding computation model of classifying corresponding with the categorization module in the categorization module, comprising:
The corresponding calculating of corresponding with categorization module classification is shown by three dimensional constitution or the list mode categorization module
Model.
Optionally, after each preset model for completing training is determined as the multiple computation model, further includes:
Determine that corresponding test data, the test data include the data by manually marking for each computation model
With without the data manually marked；
It is input in the computation model, obtains without the data manually marked by corresponding with each computation model
Export result；
According to the difference of the data by manually marking and the output result, model accuracy is obtained.
Second aspect, the embodiment of the present application provide a kind of model display device, and described device includes:
First determining module, for determining multiple computation models according to user demand；
Second determining module, for the attribute information according to each computation model, determination is corresponding with each computation model
Classification；
Display module, each categorization module in multiple default categorization modules for being directed in display platform, in this point
Generic module shows corresponding computation model of classifying corresponding with the categorization module.
Optionally, first determining module is specifically used for:
Multiple groups sample corpus is determined from preset corpus data library according to user demand；
From the sample corpus, multiple preset models to construct in advance determine corresponding sample corpus；
For each preset model, the corresponding sample corpus of the preset model is input to the preset model and is trained,
Obtain completing the preset model of training；
Each preset model for completing training is determined as the multiple computation model.
Optionally, first determining module is also used to:
The physical training condition and training result of each preset model are monitored, the training result includes the preset model
Parameter information；
Based on the physical training condition and the training result, monitoring journal is generated.
The third aspect, the embodiment of the present application provide a kind of computer equipment and include memory, processor and be stored in institute
The computer program that can be run on memory and on the processor is stated, the processor executes real when the computer program
The step of existing above method.
Fourth aspect, the embodiment of the present application provide a kind of computer readable storage medium, the computer-readable storage
The step of being stored with computer program on medium, the above method executed when the computer program is run by processor.
Model display method provided by the embodiments of the present application is determining each calculating mould according to the attribute information of each computation model
After the classification of type, corresponding computation model of classifying corresponding with the categorization module is shown in each categorization module.In this way, to be used for
Displaying for each device of industrial circle, the computation model of equipment is more clear, and selects needs according to self-demand convenient for user
Computation model, reduce user search demand computation model time.
To enable the above objects, features, and advantages of the application to be clearer and more comprehensible, preferred embodiment is cited below particularly, and cooperate
Appended attached drawing, is described in detail below.
Detailed description of the invention
Technical solution in ord to more clearly illustrate embodiments of the present application, below will be to needed in the embodiment attached
Figure is briefly described, it should be understood that the following drawings illustrates only some embodiments of the application, therefore is not construed as pair
The restriction of range for those of ordinary skill in the art without creative efforts, can also be according to this
A little attached drawings obtain other relevant attached drawings.
Fig. 1 is a kind of flow diagram of model display method provided by the embodiments of the present application；
Fig. 2 is a kind of schematic diagram at model display interface provided by the embodiments of the present application；
Fig. 3 is a kind of structural schematic diagram of model display device provided by the embodiments of the present application；
Fig. 4 is a kind of structural schematic diagram of computer equipment provided by the embodiments of the present application.
Specific embodiment
To keep the purposes, technical schemes and advantages of the embodiment of the present application clearer, below in conjunction with the embodiment of the present application
Middle attached drawing, the technical scheme in the embodiment of the application is clearly and completely described, it is clear that described embodiment is only
It is some embodiments of the present application, instead of all the embodiments.The application being usually described and illustrated herein in the accompanying drawings is real
The component for applying example can be arranged and be designed with a variety of different configurations.Therefore, below to the application's provided in the accompanying drawings
The detailed description of embodiment is not intended to limit claimed scope of the present application, but is merely representative of the selected reality of the application
Apply example.Based on embodiments herein, those skilled in the art institute obtained without making creative work
There are other embodiments, shall fall in the protection scope of this application.
The embodiment of the present application provides a kind of model display method, as shown in Figure 1, being applied in computer equipment, wherein
Computer equipment includes but is not limited to mobile phone, plate, notebook, server etc., and the application not limits this.The method packet
Include following steps:
S101 determines multiple computation models according to user demand；
Here, user demand is generally user determines according to actual conditions, can carry model function in user demand
Energy parameter determines computation model corresponding with active user's demand according to model functional parameter.
For example, if carried in user demand be predict tight blade service life information, it is determined that model be precision
Main blade life prediction.
According to user demand, when determining multiple computation models, comprising:
Multiple groups sample corpus is determined from preset corpus data library according to user demand；
From the sample corpus, multiple preset models to construct in advance determine corresponding sample corpus；
For each preset model, the corresponding sample corpus of the preset model is input to the preset model and is trained,
Obtain completing the preset model of training；
Each preset model for completing training is determined as the multiple computation model.
Here, the data for being largely used to training pattern are stored in corpus data library；Every group of sample corpus is for training one
A preset model；Preset model can be but not limited to convolutional neural networks model, Hidden Markov Model etc.；Training nerve net
The algorithm of network has detailed introduction in the prior art, is no longer excessively illustrated herein.
In specific implementation, after receiving user demand, according to model functional parameter in user demand, from corpus data
Sample corpus corresponding with model functional parameter is selected in library, the sample corpus of selection is input to corresponding with model functional parameter
The preset model constructed in advance in, which is trained, obtain training completion preset model, will training complete
Model as above-mentioned computation model.If can not determine sample corpus according to user demand, user can input preparatory structure
The sample corpus of building is uploaded to computer equipment by the sample corpus built.
For example, carrying in user demand is the information for predicting the tight blade service life, prediction essence is carried in above- mentioned information
The data demand that close blade needs determines sample corpus corresponding with data demand according to data demand from corpus data library,
Preset model corresponding with the prediction information in tight blade service life is convolutional neural networks model, then determining sample corpus is defeated
Enter to encounter in above-mentioned convolutional neural networks model, convolutional neural networks model is trained, the convolutional Neural of training will be completed
Computation model of the network model as the prediction tight blade service life.
After the completion of to preset model training, further, the accuracy in computation of each computation model is determined, instruct by each completion
Experienced preset model is determined as after the multiple computation model, further includes:
Determine that corresponding test data, the test data include the data by manually marking for each computation model
With without the data manually marked；
It is input in the computation model, obtains without the data manually marked by corresponding with each computation model
Export result；
According to the difference of the data by manually marking and the output result, model accuracy is obtained.
Here, test data can be the data obtained from corpus data library, which is for actual environment
Input data.
In specific implementation, after determining each computation model, according to the corresponding model functional parameter of each computation model, from language
Expect to determine that test data corresponding with each computation model, determining test data generally comprise the data manually marked in database
With the data without manually marking, it will be input in corresponding computation model without the data manually marked, obtain data result,
The data for counting result and manually marking are compared, if the difference of output result and the data manually marked is little, obtained model
Accuracy is relatively high, and model accuracy is higher, illustrates that the accuracy of model in application process is higher, if output result and artificial
The difference of the data of mark is very big, then the model accuracy obtained is relatively low, and model accuracy is lower, illustrates that model was being applied
Accuracy in journey is lower, if model accuracy is relatively low, at this point it is possible to the difference based on the data for exporting result and manually marking
Not Tiao Zheng computation model parameter.
For example, obtaining the artificial mark service life from corpus data library after the computation model for obtaining the prediction tight blade service life
Data and do not mark the data in service life, the data for not marking the service life are input to the computation model in prediction tight blade service life,
The service life for the blade that prediction obtains is obtained, the service life for comparing the service life of the blade of prediction and manually marking calculates comparison result one
The ratio of the number of straight blade and the number of all blades, using the ratio as model prediction accuracy.
It is being directed to each preset model, the corresponding sample corpus of the preset model is input to the preset model and is trained
When, further includes:
The physical training condition and training result of each preset model are monitored, the training result includes the preset model
Parameter information；
Based on the physical training condition and the training result, monitoring journal is generated.
Here, physical training condition includes training beginning state, training end state, and training result includes the ginseng in computation model
Number, frequency of training etc., physical training condition are usually to be exported by training engine according to hands-on result.
In specific implementation, the state of each preset model in the training process is detected, record cast starts the training of training
Beginning state, and the model training after record cast training terminates state, frequency of training in record cast training process,
The parameter of computation model, model accuracy generate monitoring journal according to physical training condition, training result is obtained.
For example, computation model A has trained 10 times in the training process, and model accuracy 96%, the parameter of computation model A
For A1-A10, the time of record cast training beginning state and the time of training end state, record above- mentioned information are monitored
Report.
S102 determines classification corresponding with each computation model according to the attribute information of each computation model；
Here, the attribute information of computation model includes equipment class, predictive maintenance, device class etc., classification include equipment class,
Predictive dimension class, device class etc..
In specific implementation, classification corresponding with the attribute information of each model is determined respectively, and e.g., the attribute of computation model is believed
Breath is equipment class, it is determined that computation model be classified as equipment class, other attribute informations are identical as above-mentioned example, not one by one into
Row example.
S103, it is aobvious in the categorization module for each categorization module in multiple default categorization modules in display platform
Show corresponding computation model of classifying corresponding with the categorization module.
When the categorization module shows the corresponding computation model of classification corresponding with the categorization module, comprising:
The corresponding calculating of corresponding with categorization module classification is shown by three dimensional constitution or the list mode categorization module
Model.
Here, three dimensional constitution can be stereoscopic display side, and list mode is generally table and shows；Display platform can be
Preset website etc.；Categorization module includes equipment class, predictive maintenance class, device class etc..
In specific implementation, categorization module corresponding to classification corresponding with computation model is determined, it is aobvious in each categorization module
Show each computation model, specifically refer to Fig. 2, it should be noted that Fig. 2 is only schematical, concrete condition should be subject to,
The application not limits this.Wherein, categorization module is when showing each computation model, can also according to the comprehensive score of model,
The accuracy rate of model, the frequency of use of model are shown, to facilitate user according to evaluating deg, accuracy and usage frequency pair
The computing module of displaying is selected, and the Experience Degree of user is improved.
When showing each model, user can also select multiple computation models, and compile in display platform according to self-demand
The logical order for collecting multiple computation models, obtains a collective model, to realize the test to multifunction device, e.g., Yong Huxu
Predict the service life of lathe, lathe includes that tight blade, multiple functional modules etc. need to select when selecting computation model
It predicts that the computation model in tight blade service life, and selection calculate the computation model in each functional mode service life, and selects one
For the computation model that the service life to each component is weighted, final collective model is obtained.
The application is aobvious in each categorization module after the classification for determining each computation model according to the attribute information of each computation model
Show corresponding computation model of classifying corresponding with the categorization module.In this way, making for being directed to each device of industrial circle, equipment
The displaying of computation model is more clear, and convenient for the computation model that user needs according to self-demand selection, reduces user's lookup
The time of demand computation model.
The embodiment of the present application provides a kind of model display device, as shown in figure 3, the device includes:
First determining module 31, for determining multiple computation models according to user demand；
Second determining module 32, for the attribute information according to each computation model, determination is corresponding with each computation model
Classification；
Display module 33, each categorization module in multiple default categorization modules for being directed in display platform, at this
Categorization module shows corresponding computation model of classifying corresponding with the categorization module.
Optionally, first determining module 31 is specifically used for:
Multiple groups sample corpus is determined from preset corpus data library according to user demand；
From the sample corpus, multiple preset models to construct in advance determine corresponding sample corpus；
For each preset model, the corresponding sample corpus of the preset model is input to the preset model and is trained,
Obtain completing the preset model of training；
Each preset model for completing training is determined as the multiple computation model.
Optionally, first determining module 31 is also used to:
The physical training condition and training result of each preset model are monitored, the training result includes the preset model
Parameter information；
Based on the physical training condition and the training result, monitoring journal is generated.
Optionally, the display module 33 is specifically used for:
The corresponding calculating of corresponding with categorization module classification is shown by three dimensional constitution or the list mode categorization module
Model.
Optionally, first determining module 31 is also used to:
Determine that corresponding test data, the test data include the data by manually marking for each computation model
With without the data manually marked；
It is input in the computation model, obtains without the data manually marked by corresponding with each computation model
Export result；
According to the difference of the data by manually marking and the output result, model accuracy is obtained.
Corresponding to the model display method in Fig. 1, the embodiment of the present application also provides a kind of computer equipments 400, such as Fig. 4
Shown, which includes memory 401, processor 402 and is stored on the memory 401 and can transport on the processor 402
Capable computer program, wherein above-mentioned processor 402 realizes above-mentioned model display method when executing above-mentioned computer program.
Specifically, above-mentioned memory 401 and processor 402 can be general memory and processor, do not do have here
Body limits, and when the computer program of 402 run memory 401 of processor storage, is able to carry out above-mentioned model display method, uses
In providing a kind of simple, convenient and fast model display method, the application is determining each calculating according to the attribute information of each computation model
After the classification of model, corresponding computation model of classifying corresponding with the categorization module is shown in each categorization module.In this way, to use
It is more clear in the displaying of the computation model for each device of industrial circle, equipment, need is selected according to self-demand convenient for user
The computation model wanted reduces the time that user searches demand computation model.
Corresponding to the model display method in Fig. 1, the embodiment of the present application also provides a kind of computer readable storage medium,
It is stored with computer program on the computer readable storage medium, which executes above-mentioned model when being run by processor
The step of methods of exhibiting.
Specifically, which can be general storage medium, such as mobile disk, hard disk, on the storage medium
Computer program when being run, above-mentioned model display method is able to carry out, for providing a kind of simple, convenient and fast model display
Method, the application are shown after the classification for determining each computation model according to the attribute information of each computation model in each categorization module
Corresponding computation model of classifying corresponding with the categorization module.In this way, making based on for each device of industrial circle, equipment
The displaying for calculating model is more clear, and convenient for the computation model that user needs according to self-demand selection, reducing user's lookup is needed
Ask the time of computation model.
In embodiment provided herein, it should be understood that disclosed system and method, it can be by others side
Formula is realized.System embodiment described above is only schematical, for example, the division of the unit, only one kind are patrolled
Function division is collected, there may be another division manner in actual implementation, in another example, multiple units or components can combine or can
To be integrated into another system, or some features can be ignored or not executed.Another point, shown or discussed is mutual
Coupling, direct-coupling or communication connection can be INDIRECT COUPLING or communication link by some communication interfaces, system or unit
It connects, can be electrical property, mechanical or other forms.
The unit as illustrated by the separation member may or may not be physically separated, aobvious as unit
The component shown may or may not be physical unit, it can and it is in one place, or may be distributed over multiple
In network unit.It can select some or all of unit therein according to the actual needs to realize the mesh of this embodiment scheme
's.
In addition, each functional unit in embodiment provided by the present application can integrate in one processing unit, it can also
To be that each unit physically exists alone, can also be integrated in one unit with two or more units.
It, can be with if the function is realized in the form of SFU software functional unit and when sold or used as an independent product
It is stored in a computer readable storage medium.Based on this understanding, the technical solution of the application is substantially in other words
The part of the part that contributes to existing technology or the technical solution can be embodied in the form of software products, the meter
Calculation machine software product is stored in a storage medium, including some instructions are used so that a computer equipment (can be a
People's computer, server or network equipment etc.) execute each embodiment the method for the application all or part of the steps.
And storage medium above-mentioned includes: that USB flash disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), arbitrary access are deposited
The various media that can store program code such as reservoir (RAM, Random Access Memory), magnetic or disk.
It should also be noted that similar label and letter indicate similar terms in following attached drawing, therefore, once a certain Xiang Yi
It is defined in a attached drawing, does not then need that it is further defined and explained in subsequent attached drawing, in addition, term " the
One ", " second ", " third " etc. are only used for distinguishing description, are not understood to indicate or imply relative importance.
Finally, it should be noted that embodiment described above, the only specific embodiment of the application, to illustrate the application
Technical solution, rather than its limitations, the protection scope of the application is not limited thereto, although with reference to the foregoing embodiments to this Shen
It please be described in detail, those skilled in the art should understand that: anyone skilled in the art
Within the technical scope of the present application, it can still modify to technical solution documented by previous embodiment or can be light
It is readily conceivable that variation or equivalent replacement of some of the technical features；And these modifications, variation or replacement, do not make
The essence of corresponding technical solution is detached from the spirit and scope of the embodiment of the present application technical solution.The protection in the application should all be covered
Within the scope of.Therefore, the protection scope of the application shall be subject to the protection scope of the claim.
Claims (10)
1. a kind of model display method, which is characterized in that the described method includes:
According to user demand, multiple computation models are determined；
According to the attribute information of each computation model, classification corresponding with each computation model is determined；
For each categorization module in multiple default categorization modules in display platform, shown and the classification in the categorization module
The corresponding computation model of the corresponding classification of module.
2. the method as described in claim 1, which is characterized in that described to determine multiple computation models according to user demand, packet
It includes:
Multiple groups sample corpus is determined from preset corpus data library according to user demand；
From the sample corpus, multiple preset models to construct in advance determine corresponding sample corpus；
For each preset model, the corresponding sample corpus of the preset model is input to the preset model and is trained, is obtained
Complete the preset model of training；
Each preset model for completing training is determined as the multiple computation model.
3. method according to claim 2, which is characterized in that each preset model is directed to described, by the preset model pair
When the sample corpus answered is input to the preset model and is trained, further includes:
The physical training condition and training result of each preset model are monitored, the training result includes the ginseng of the preset model
Number information；
Based on the physical training condition and the training result, monitoring journal is generated.
4. the method as described in claim 1, which is characterized in that it is described shown in the categorization module it is corresponding with the categorization module
Classify corresponding computation model, comprising:
The corresponding computation model of corresponding with categorization module classification is shown by three dimensional constitution or the list mode categorization module.
5. method according to claim 2, which is characterized in that by it is each complete training preset model be determined as it is the multiple
After computation model, further includes:
Corresponding test data is determined for each computation model, and the test data includes data by manually marking and not
By the data manually marked；
It is input in the computation model, is exported without the data manually marked by corresponding with each computation model
As a result；
According to the difference of the data by manually marking and the output result, model accuracy is obtained.
6. a kind of model display device, which is characterized in that described device includes:
First determining module, for determining multiple computation models according to user demand；
Second determining module determines classification corresponding with each computation model for the attribute information according to each computation model；
Display module, each categorization module in multiple default categorization modules for being directed in display platform, in the classification mould
Block shows corresponding computation model of classifying corresponding with the categorization module.
7. device as claimed in claim 6, which is characterized in that first determining module is specifically used for:
Multiple groups sample corpus is determined from preset corpus data library according to user demand；
From the sample corpus, multiple preset models to construct in advance determine corresponding sample corpus；
For each preset model, the corresponding sample corpus of the preset model is input to the preset model and is trained, is obtained
Complete the preset model of training；
Each preset model for completing training is determined as the multiple computation model.
8. device as claimed in claim 7, which is characterized in that first determining module is also used to:
The physical training condition and training result of each preset model are monitored, the training result includes the ginseng of the preset model
Number information；
Based on the physical training condition and the training result, monitoring journal is generated.
9. a kind of computer equipment includes memory, processor and is stored on the memory and can transport on the processor
Capable computer program, which is characterized in that the processor realizes that the claims 1-5 appoints when executing the computer program
The step of method described in one.
10. a kind of computer readable storage medium, computer program, feature are stored on the computer readable storage medium
The step of being, the claims 1-5 described in any item methods executed when the computer program is run by processor.
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Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106228185A (en) * | 2016-07-20 | 2016-12-14 | 武汉盈力科技有限公司 | A kind of general image classifying and identifying system based on neutral net and method |
| CN107766940A (en) * | 2017-11-20 | 2018-03-06 | 北京百度网讯科技有限公司 | Method and apparatus for generation model |
| US20180068021A1 (en) * | 2011-09-04 | 2018-03-08 | Leigh M. Rothschild | Systems, devices, and methods for providing multidimensional search results |
-
2018
- 2018-12-29 CN CN201811643390.9A patent/CN109710819A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20180068021A1 (en) * | 2011-09-04 | 2018-03-08 | Leigh M. Rothschild | Systems, devices, and methods for providing multidimensional search results |
| CN106228185A (en) * | 2016-07-20 | 2016-12-14 | 武汉盈力科技有限公司 | A kind of general image classifying and identifying system based on neutral net and method |
| CN107766940A (en) * | 2017-11-20 | 2018-03-06 | 北京百度网讯科技有限公司 | Method and apparatus for generation model |
Non-Patent Citations (3)
| Title |
|---|
| 宋旭 等: "《中小学体育教学研究方法》", 29 February 2016, 武汉大学出版社 * |
| 舒天然: "《我国中央银行流动性救助及其决策支持系统研究》", 31 August 2018, 西安交通大学出版社 * |
| 苏凯 等: "《企业信息系统表现规范探索与实践》", 30 June 2015, 华南理工大学出版社 * |
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