CN118796905B - Model database management method and system based on parameter matching - Google Patents
Model database management method and system based on parameter matching Download PDFInfo
- Publication number
- CN118796905B CN118796905B CN202411238654.8A CN202411238654A CN118796905B CN 118796905 B CN118796905 B CN 118796905B CN 202411238654 A CN202411238654 A CN 202411238654A CN 118796905 B CN118796905 B CN 118796905B
- Authority
- CN
- China
- Prior art keywords
- model
- user
- similarity
- parameter
- algorithm
- 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
- 238000007726 management method Methods 0.000 title claims abstract description 53
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 125
- 238000000034 method Methods 0.000 claims abstract description 23
- 238000012216 screening Methods 0.000 claims abstract description 16
- 230000004044 response Effects 0.000 claims abstract description 3
- 230000006870 function Effects 0.000 claims description 21
- 238000010845 search algorithm Methods 0.000 claims description 6
- 238000012549 training Methods 0.000 claims description 5
- 238000012545 processing Methods 0.000 description 22
- 238000004364 calculation method Methods 0.000 description 12
- 238000004590 computer program Methods 0.000 description 12
- 238000010586 diagram Methods 0.000 description 10
- 230000008569 process Effects 0.000 description 7
- 238000005516 engineering process Methods 0.000 description 5
- 230000005540 biological transmission Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 238000013473 artificial intelligence Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000001413 cellular effect Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 238000013523 data management Methods 0.000 description 1
- 238000013500 data storage Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000001914 filtration Methods 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 230000005055 memory storage Effects 0.000 description 1
- 230000000750 progressive effect Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2457—Query processing with adaptation to user needs
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/28—Databases characterised by their database models, e.g. relational or object models
- G06F16/284—Relational databases
- G06F16/285—Clustering or classification
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/22—Matching criteria, e.g. proximity measures
-
- 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
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Databases & Information Systems (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Evolutionary Computation (AREA)
- Evolutionary Biology (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Bioinformatics & Computational Biology (AREA)
- Artificial Intelligence (AREA)
- Life Sciences & Earth Sciences (AREA)
- Computational Linguistics (AREA)
- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
Abstract
本发明公开了一种基于参数匹配的模型数据库管理方法及系统，该方法包括：获取多个用户上传的多个算法模型数据；根据每一所述算法模型数据对应的模型参数和用户参数，对所有所述算法模型数据进行分组，得到多个模型集合；响应于目标用户的网站访问操作，根据所述目标用户的用户参数，从所述多个模型集合中筛选出目标模型集合；根据所述网站访问操作，在所述目标模型集合中确定出多个推荐模型数据。可见，本发明能够有效提高模型数据的管理效率和分组合理性，为用户提供更合理的模型推荐服务，提高用户体验。
The present invention discloses a model database management method and system based on parameter matching, the method comprising: obtaining multiple algorithm model data uploaded by multiple users; grouping all the algorithm model data according to the model parameters and user parameters corresponding to each of the algorithm model data to obtain multiple model sets; in response to a website access operation of a target user, screening out a target model set from the multiple model sets according to the user parameters of the target user; and determining multiple recommended model data in the target model set according to the website access operation. It can be seen that the present invention can effectively improve the management efficiency and grouping rationality of model data, provide users with more reasonable model recommendation services, and improve user experience.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a model database management method and system based on parameter matching.
Background
Under the great background that artificial intelligence technology is continuously developed and paid attention to, a great number of technicians or technical institutions begin to train and research algorithm models by adopting algorithm technology, and upload self-trained algorithm models on a data sharing platform or a computing platform, so that how to effectively and efficiently manage the algorithm models becomes an important technical problem. In the existing algorithm model data management technology, most of the algorithm model data storage and search functions are only realized, and user parameters are not further considered to group and recommend the model data, so that the management effect is general, and the user experience is poor. It can be seen that the prior art has defects and needs to be solved.
Disclosure of Invention
The technical problem to be solved by the invention is to provide the model database management method and system based on parameter matching, which can effectively improve the management efficiency and grouping rationality of model data, provide more reasonable model recommendation service for users and improve user experience.
In order to solve the technical problem, the first aspect of the present invention discloses a model database management method based on parameter matching, which comprises the following steps:
Acquiring a plurality of algorithm model data uploaded by a plurality of users;
Grouping all the algorithm model data according to the model parameters and the user parameters corresponding to each algorithm model data to obtain a plurality of model sets;
responding to website access operation of a target user, and screening a target model set from the plurality of model sets according to user parameters of the target user;
And determining a plurality of recommendation model data in the target model set according to the website access operation.
As an optional implementation manner, in the first aspect of the present invention, the model parameters include a model size, a model usage, a model training data type, a model architecture, a model input output data type, and a model history usage record.
As an optional implementation manner, in the first aspect of the present invention, the user parameters include a user type, a user level, a number of user upload models, a user history upload record, and a user history download record.
In a first aspect of the present invention, as an optional implementation manner, the grouping all the algorithm model data according to the model parameters and the user parameters corresponding to each algorithm model data to obtain a plurality of model sets includes:
Calculating the similarity of model parameters and user parameters between any two algorithm model data to obtain the parameter similarity between the two algorithm model data;
And grouping all algorithm model data based on a dynamic programming algorithm according to the parameter similarity to obtain a plurality of model sets.
As an optional implementation manner, in the first aspect of the present invention, the calculating the similarity between the model parameters and the user parameters between any two pieces of algorithm model data, to obtain the parameter similarity between the two pieces of algorithm model data, includes:
for any two pieces of algorithm model data, calculating first similarity between the model parameters corresponding to the two pieces of algorithm model data;
Calculating a second similarity between the users corresponding to the two algorithm model data;
And calculating the product of the first similarity and the second similarity to obtain the parameter similarity between the two algorithm models.
As an optional implementation manner, in the first aspect of the present invention, the grouping, based on a dynamic programming algorithm, all the algorithm model data according to the parameter similarity, to obtain a plurality of model sets includes:
Setting an objective function to maximize the number of the algorithm model data in each model set;
Setting a limiting condition, wherein the parameter similarity between any two algorithm model data in the same model set is larger than a first similarity threshold, and the parameter similarity between any two algorithm model data respectively belonging to different model sets is smaller than a second similarity threshold, and the second similarity threshold is smaller than the first similarity threshold;
And based on a space search algorithm, carrying out iterative grouping calculation on all algorithm model data according to the objective function and the limiting condition until an optimal result is obtained, and obtaining a plurality of model sets.
As an optional implementation manner, in a first aspect of the present invention, the screening the target model set from the plurality of model sets according to the user parameter of the target user includes:
for each model set, vectorizing the union of the user parameters corresponding to all the algorithm model data in the model set, and determining the union as a user parameter vector corresponding to the model set;
calculating a vectorization result corresponding to the user parameters of the target user;
Calculating the vector distance between the user parameter vector and the vectorization result to obtain the user parameter similarity corresponding to the model set;
and determining all the model sets with the user parameter similarity higher than a third similarity threshold as a target model set.
As an optional implementation manner, in the first aspect of the present invention, the determining, according to the website access operation, a plurality of recommendation model data in the target model set includes:
Determining a cursor moving path and a cursor clicking object corresponding to the website access operation;
Determining a plurality of path passing areas on the current browser page according to the cursor moving path;
determining a plurality of clicked objects from a plurality of model objects of the current browser page according to the cursor clicked object;
Determining model parameters corresponding to all model objects in all the path passing region as a first model parameter set;
determining all model parameters corresponding to the clicked objects as a second model parameter set;
For each model data in the set of target models, calculating a third similarity between model parameters of the model data and the set of first model parameters;
Calculating a fourth similarity between model parameters of the model data and the second set of model parameters;
calculating the product of the third similarity and the fourth similarity to obtain a priority parameter corresponding to the model data;
and determining all the model data with the priority parameter larger than a preset parameter threshold value as recommended model data.
The second aspect of the embodiment of the invention discloses a model database management system based on parameter matching, which comprises the following components:
the acquisition module is used for acquiring a plurality of algorithm model data uploaded by a plurality of users;
The grouping module is used for grouping all the algorithm model data according to the model parameters and the user parameters corresponding to each algorithm model data to obtain a plurality of model sets;
The screening module is used for responding to the website access operation of the target user and screening a target model set from the plurality of model sets according to the user parameters of the target user;
and the determining module is used for determining a plurality of recommendation model data in the target model set according to the website access operation.
As an alternative embodiment, in the second aspect of the present invention, the model parameters include model size, model usage, model training data type, model architecture, model input output data type, and model history usage record.
As an optional implementation manner, in the second aspect of the present invention, the user parameters include a user type, a user level, a number of user upload models, a user history upload record, and a user history download record.
In a second aspect of the present invention, as an optional implementation manner, the grouping module groups all the algorithm model data according to the model parameters and the user parameters corresponding to each algorithm model data to obtain a specific mode of multiple model sets, where the specific mode includes:
Calculating the similarity of model parameters and user parameters between any two algorithm model data to obtain the parameter similarity between the two algorithm model data;
And grouping all algorithm model data based on a dynamic programming algorithm according to the parameter similarity to obtain a plurality of model sets.
As an optional implementation manner, in the second aspect of the present invention, the specific manner in which the grouping module calculates the similarity between the model parameters and the user parameters between any two pieces of the algorithm model data to obtain the parameter similarity between the two pieces of the algorithm model data includes:
for any two pieces of algorithm model data, calculating first similarity between the model parameters corresponding to the two pieces of algorithm model data;
Calculating a second similarity between the users corresponding to the two algorithm model data;
And calculating the product of the first similarity and the second similarity to obtain the parameter similarity between the two algorithm models.
As an optional implementation manner, in the second aspect of the present invention, the grouping module groups all the algorithm model data based on a dynamic programming algorithm according to the parameter similarity, so as to obtain a specific mode of multiple model sets, where the specific mode includes:
Setting an objective function to maximize the number of the algorithm model data in each model set;
Setting a limiting condition, wherein the parameter similarity between any two algorithm model data in the same model set is larger than a first similarity threshold, and the parameter similarity between any two algorithm model data respectively belonging to different model sets is smaller than a second similarity threshold, and the second similarity threshold is smaller than the first similarity threshold;
And based on a space search algorithm, carrying out iterative grouping calculation on all algorithm model data according to the objective function and the limiting condition until an optimal result is obtained, and obtaining a plurality of model sets.
In a second aspect of the present invention, as an optional implementation manner, the filtering module filters a target model set from the multiple model sets according to a user parameter of the target user, where the specific manner includes:
for each model set, vectorizing the union of the user parameters corresponding to all the algorithm model data in the model set, and determining the union as a user parameter vector corresponding to the model set;
calculating a vectorization result corresponding to the user parameters of the target user;
Calculating the vector distance between the user parameter vector and the vectorization result to obtain the user parameter similarity corresponding to the model set;
and determining all the model sets with the user parameter similarity higher than a third similarity threshold as a target model set.
In a second aspect of the present invention, as an optional implementation manner, the determining module determines, according to the website access operation, a specific manner of a plurality of recommended model data in the target model set, where the specific manner includes:
Determining a cursor moving path and a cursor clicking object corresponding to the website access operation;
Determining a plurality of path passing areas on the current browser page according to the cursor moving path;
determining a plurality of clicked objects from a plurality of model objects of the current browser page according to the cursor clicked object;
Determining model parameters corresponding to all model objects in all the path passing region as a first model parameter set;
determining all model parameters corresponding to the clicked objects as a second model parameter set;
For each model data in the set of target models, calculating a third similarity between model parameters of the model data and the set of first model parameters;
Calculating a fourth similarity between model parameters of the model data and the second set of model parameters;
calculating the product of the third similarity and the fourth similarity to obtain a priority parameter corresponding to the model data;
and determining all the model data with the priority parameter larger than a preset parameter threshold value as recommended model data.
In a third aspect, the present invention discloses another model database management system based on parameter matching, the system includes:
A memory storing executable program code;
A processor coupled to the memory;
The processor invokes the executable program code stored in the memory to perform some or all of the steps in the parameter matching based model database management method disclosed in the first aspect of the present invention.
A fourth aspect of the invention discloses a computer storage medium storing computer instructions which, when invoked, are adapted to perform part or all of the steps of the parameter matching based model database management method disclosed in the first aspect of the invention.
Compared with the prior art, the embodiment of the invention has the following beneficial effects:
The method and the system can accurately and reasonably group the model data uploaded by the user based on the model parameters and the user parameters, and then perform set screening and recommendation based on the parameters and the operations of the target user to determine more matched multiple recommendation model data, so that the management efficiency and grouping rationality of the model data can be effectively improved, more reasonable model recommendation service is provided for the user, and the user experience is improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic flow chart of a model database management method based on parameter matching according to an embodiment of the present invention.
Fig. 2 is a schematic structural diagram of a model database management system based on parameter matching according to an embodiment of the present invention.
FIG. 3 is a schematic diagram of another model database management system based on parameter matching according to an embodiment of the present invention.
Detailed Description
In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The terms first, second and the like in the description and in the claims and in the above-described figures are used for distinguishing between different objects and not necessarily for describing a sequential or chronological order. Furthermore, the terms "comprise" and "have," as well as any variations thereof, are intended to cover a non-exclusive inclusion. For example, a process, method, apparatus, article, or device that comprises a list of steps or elements is not limited to the list of steps or elements but may, in the alternative, include other steps or elements not expressly listed or inherent to such process, method, article, or device.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the invention. The appearances of such phrases in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those of skill in the art will explicitly and implicitly appreciate that the embodiments described herein may be combined with other embodiments.
The invention discloses a model database management method and system based on parameter matching, which can accurately and reasonably group model data uploaded by a user based on model parameters and user parameters, and then perform set screening and recommendation based on parameters and operations of a target user to determine more matched multiple recommendation model data, so that the management efficiency and grouping rationality of the model data can be effectively improved, more reasonable model recommendation service is provided for the user, and user experience is improved. The following will describe in detail.
Example 1
Referring to fig. 1, fig. 1 is a flow chart of a model database management method based on parameter matching according to an embodiment of the present invention. The model database management method based on parameter matching described in fig. 1 can be applied to a data processing system/data processing device/data processing server (wherein the server comprises a local processing server or a cloud processing server). As shown in fig. 1, the parameter matching-based model database management method may include the following operations:
101. and acquiring a plurality of algorithm model data uploaded by a plurality of users.
102. And grouping all the algorithm model data according to the model parameters and the user parameters corresponding to each algorithm model data to obtain a plurality of model sets.
103. And responding to the website access operation of the target user, and screening a target model set from a plurality of model sets according to the user parameters of the target user.
104. And determining a plurality of recommendation model data in the target model set according to the website access operation.
Therefore, the embodiment of the invention can accurately and reasonably group the model data uploaded by the user based on the model parameters and the user parameters, and then perform set screening and recommendation based on the parameters and the operation of the target user so as to determine more matched multiple recommendation model data, thereby effectively improving the management efficiency and grouping rationality of the model data, providing more reasonable model recommendation service for the user and improving the user experience.
As an alternative embodiment, in the above steps, the model parameters include model size, model usage, model training data type, model architecture, model input output data type, and model history usage record.
Therefore, through the optional embodiment, the content of the model parameters is limited, so that the characteristics of the model data are more comprehensively represented, the management efficiency and grouping rationality of the model data are improved, more reasonable model recommendation service is provided for the user, and the user experience is improved.
As an alternative embodiment, in the above steps, the user parameters include user type, user level, number of user upload models, user history upload record, and user history download record.
Therefore, through the optional embodiment, the content of the user parameter is limited, so that the characteristics of the uploading user are more comprehensively represented, the management efficiency and grouping rationality of the model data are improved, more reasonable model recommendation service is provided for the user, and the user experience is improved.
As an optional embodiment, in the step, according to the model parameter and the user parameter corresponding to each algorithm model data, grouping all algorithm model data to obtain a plurality of model sets, including:
Calculating the similarity of model parameters and user parameters between any two algorithm model data to obtain the parameter similarity between the two algorithm model data;
and according to the parameter similarity, based on a dynamic programming algorithm, grouping all algorithm model data to obtain a plurality of model sets.
Therefore, through the optional embodiment, the model data uploaded by the user can be accurately and reasonably grouped based on the calculation of the similarity and the dynamic programming algorithm, the management efficiency and the grouping rationality of the model data are improved, more reasonable model recommendation service is provided for the user, and the user experience is improved.
As an optional embodiment, in the step, calculating the similarity between the model parameters and the user parameters between any two algorithm model data to obtain the parameter similarity between the two algorithm model data includes:
for any two algorithm model data, calculating a first similarity between model parameters corresponding to the two algorithm model data;
Calculating a second similarity between users corresponding to the two algorithm model data;
and calculating the product of the first similarity and the second similarity to obtain the parameter similarity between the two algorithm models.
Therefore, through the optional embodiment, the similarity between the model parameters of the model data and the user parameters can be calculated through calculation and product calculation, so that the parameter similarity between the model data can be calculated, the accurate and reasonable grouping of the model data uploaded by the user can be conveniently realized, the management efficiency and grouping rationality of the model data can be improved, more reasonable model recommendation service can be provided for the user, and the user experience can be improved.
As an optional embodiment, in the step, based on the dynamic programming algorithm, all algorithm model data are grouped according to the parameter similarity, so as to obtain a plurality of model sets, including:
Setting an objective function as the number of algorithm model data in each model set to be maximum;
The method comprises the steps that a limiting condition is set, wherein the parameter similarity between any two algorithm model data in the same model set is larger than a first similarity threshold value, and the parameter similarity between any two algorithm model data respectively belonging to different model sets is smaller than a second similarity threshold value;
based on a space searching algorithm, carrying out iterative grouping calculation on all algorithm model data according to an objective function and a limiting condition until an optimal result is obtained, and obtaining a plurality of model sets.
Therefore, through the optional embodiment, accurate and reasonable grouping of the model data uploaded by the user can be realized based on the preset objective function and the limiting condition and based on the space search algorithm, so that the management efficiency and grouping rationality of the model data are improved, more reasonable model recommendation service is provided for the user, and the user experience is improved.
As an optional embodiment, in the step, selecting the target model set from the multiple model sets according to the user parameters of the target user includes:
For each model set, vectorizing the union of user parameters corresponding to all algorithm model data in the model set, and determining the user parameter vector corresponding to the model set;
Calculating a vectorization result corresponding to the user parameters of the target user;
calculating a vector distance between the user parameter vector and the vectorization result to obtain user parameter similarity corresponding to the model set;
And determining all model sets with user parameter similarity higher than a third similarity threshold as target model sets.
Therefore, through the above optional embodiment, the similarity of the user parameters corresponding to the model set can be calculated based on the vector distance between the user parameters, and then the target model set is screened out based on the similarity, so that a plurality of more matched recommendation model data are determined later, thereby improving the management efficiency and grouping rationality of the model data, providing more reasonable model recommendation service for the user, and improving the user experience.
As an optional embodiment, in the step, determining, according to the website access operation, a plurality of recommendation model data in the target model set includes:
Determining a cursor moving path and a cursor clicking object corresponding to website access operation;
Determining a plurality of path passing areas on the current browser page according to the cursor moving path;
According to the object clicked by the cursor, determining a plurality of clicked objects in a plurality of model objects of the current browser page;
Determining model parameters corresponding to all model objects in all path passing areas as a first model parameter set;
Determining model parameters corresponding to all clicked objects as a second model parameter set;
For each model data in the set of target models, calculating a third similarity between model parameters of the model data and the set of first model parameters;
Calculating a fourth similarity between the model parameters of the model data and the second set of model parameters;
Calculating the product of the third similarity and the fourth similarity to obtain a priority parameter corresponding to the model data;
And determining all model data with priority parameters larger than a preset parameter threshold as recommended model data.
Therefore, through the optional embodiment, a plurality of model objects which are possibly interested by the user can be determined based on the website access operation of the user, and a plurality of more matched recommended model data are accurately determined based on similarity calculation and screening among model parameters, so that the management efficiency and grouping rationality of the model data are improved, more reasonable model recommendation service is provided for the user, and the user experience is improved.
Example two
Referring to fig. 2, fig. 2 is a schematic structural diagram of a model database management system based on parameter matching according to an embodiment of the present invention. The model database management system based on parameter matching described in fig. 2 can be applied to a data processing system/data processing device/data processing server (wherein the server comprises a local processing server or a cloud processing server). As shown in fig. 2, the parameter matching-based model database management system may include:
An obtaining module 201, configured to obtain a plurality of algorithm model data uploaded by a plurality of users.
The grouping module 202 is configured to group all the algorithm model data according to the model parameters and the user parameters corresponding to each algorithm model data, so as to obtain a plurality of model sets.
And the screening module 203 is configured to screen the target model set from the multiple model sets according to the user parameters of the target user in response to the website access operation of the target user.
The determining module 204 is configured to determine a plurality of recommendation model data from the target model set according to the website access operation.
Therefore, the embodiment of the invention can accurately and reasonably group the model data uploaded by the user based on the model parameters and the user parameters, and then perform set screening and recommendation based on the parameters and the operation of the target user so as to determine more matched multiple recommendation model data, thereby effectively improving the management efficiency and grouping rationality of the model data, providing more reasonable model recommendation service for the user and improving the user experience.
As an alternative embodiment, the model parameters include model size, model usage, model training data type, model architecture, model input output data type, and model history usage record.
Therefore, through the optional embodiment, the content of the model parameters is limited, so that the characteristics of the model data are more comprehensively represented, the management efficiency and grouping rationality of the model data are improved, more reasonable model recommendation service is provided for the user, and the user experience is improved.
As an alternative embodiment, the user parameters include user type, user level, number of user upload models, user history upload records, and user history download records.
Therefore, through the optional embodiment, the content of the user parameter is limited, so that the characteristics of the uploading user are more comprehensively represented, the management efficiency and grouping rationality of the model data are improved, more reasonable model recommendation service is provided for the user, and the user experience is improved.
As an optional embodiment, the grouping module groups all the algorithm model data according to the model parameters and the user parameters corresponding to each algorithm model data to obtain a specific mode of a plurality of model sets, including:
Calculating the similarity of model parameters and user parameters between any two algorithm model data to obtain the parameter similarity between the two algorithm model data;
and according to the parameter similarity, based on a dynamic programming algorithm, grouping all algorithm model data to obtain a plurality of model sets.
Therefore, through the optional embodiment, the model data uploaded by the user can be accurately and reasonably grouped based on the calculation of the similarity and the dynamic programming algorithm, the management efficiency and the grouping rationality of the model data are improved, more reasonable model recommendation service is provided for the user, and the user experience is improved.
As an optional embodiment, the grouping module calculates the similarity of the model parameters and the user parameters between any two algorithm model data, to obtain a specific mode of parameter similarity between the two algorithm model data, including:
for any two algorithm model data, calculating a first similarity between model parameters corresponding to the two algorithm model data;
Calculating a second similarity between users corresponding to the two algorithm model data;
and calculating the product of the first similarity and the second similarity to obtain the parameter similarity between the two algorithm models.
Therefore, through the optional embodiment, the similarity between the model parameters of the model data and the user parameters can be calculated through calculation and product calculation, so that the parameter similarity between the model data can be calculated, the accurate and reasonable grouping of the model data uploaded by the user can be conveniently realized, the management efficiency and grouping rationality of the model data can be improved, more reasonable model recommendation service can be provided for the user, and the user experience can be improved.
As an optional embodiment, the grouping module groups all algorithm model data based on a dynamic programming algorithm according to the parameter similarity, to obtain a specific mode of multiple model sets, including:
Setting an objective function as the number of algorithm model data in each model set to be maximum;
The method comprises the steps that a limiting condition is set, wherein the parameter similarity between any two algorithm model data in the same model set is larger than a first similarity threshold value, and the parameter similarity between any two algorithm model data respectively belonging to different model sets is smaller than a second similarity threshold value;
based on a space searching algorithm, carrying out iterative grouping calculation on all algorithm model data according to an objective function and a limiting condition until an optimal result is obtained, and obtaining a plurality of model sets.
Therefore, through the optional embodiment, accurate and reasonable grouping of the model data uploaded by the user can be realized based on the preset objective function and the limiting condition and based on the space search algorithm, so that the management efficiency and grouping rationality of the model data are improved, more reasonable model recommendation service is provided for the user, and the user experience is improved.
As an optional embodiment, the screening module screens the specific mode of the target model set from the multiple model sets according to the user parameters of the target user, including:
For each model set, vectorizing the union of user parameters corresponding to all algorithm model data in the model set, and determining the user parameter vector corresponding to the model set;
Calculating a vectorization result corresponding to the user parameters of the target user;
calculating a vector distance between the user parameter vector and the vectorization result to obtain user parameter similarity corresponding to the model set;
And determining all model sets with user parameter similarity higher than a third similarity threshold as target model sets.
Therefore, through the above optional embodiment, the similarity of the user parameters corresponding to the model set can be calculated based on the vector distance between the user parameters, and then the target model set is screened out based on the similarity, so that a plurality of more matched recommendation model data are determined later, thereby improving the management efficiency and grouping rationality of the model data, providing more reasonable model recommendation service for the user, and improving the user experience.
As an alternative embodiment, the determining module determines a specific mode of the plurality of recommended model data in the target model set according to the website access operation, including:
Determining a cursor moving path and a cursor clicking object corresponding to website access operation;
Determining a plurality of path passing areas on the current browser page according to the cursor moving path;
According to the object clicked by the cursor, determining a plurality of clicked objects in a plurality of model objects of the current browser page;
Determining model parameters corresponding to all model objects in all path passing areas as a first model parameter set;
Determining model parameters corresponding to all clicked objects as a second model parameter set;
For each model data in the set of target models, calculating a third similarity between model parameters of the model data and the set of first model parameters;
Calculating a fourth similarity between the model parameters of the model data and the second set of model parameters;
Calculating the product of the third similarity and the fourth similarity to obtain a priority parameter corresponding to the model data;
And determining all model data with priority parameters larger than a preset parameter threshold as recommended model data.
Therefore, through the optional embodiment, a plurality of model objects which are possibly interested by the user can be determined based on the website access operation of the user, and a plurality of more matched recommended model data are accurately determined based on similarity calculation and screening among model parameters, so that the management efficiency and grouping rationality of the model data are improved, more reasonable model recommendation service is provided for the user, and the user experience is improved.
Example III
Referring to fig. 3, fig. 3 is a schematic diagram of another model database management system based on parameter matching according to an embodiment of the present invention. The parameter matching based model database management system depicted in fig. 3 is applied in a data processing system/data processing device/data processing server (wherein the server comprises a local processing server or a cloud processing server). As shown in fig. 3, the parameter matching-based model database management system may include:
a memory 301 storing executable program code;
a processor 302 coupled with the memory 301;
Wherein the processor 302 invokes executable program code stored in the memory 301 for performing the steps of the parameter matching based model database management method described in embodiment one.
Example IV
The embodiment of the invention discloses a computer-readable storage medium storing a computer program for electronic data exchange, wherein the computer program causes a computer to execute the steps of the model database management method based on parameter matching described in the embodiment.
Example five
The embodiment of the invention discloses a computer program product, which comprises a non-transitory computer readable storage medium storing a computer program, and the computer program is operable to cause a computer to execute the steps of the model database management method based on parameter matching described in the embodiment.
The foregoing describes certain embodiments of the present disclosure, other embodiments being within the scope of the following claims. In some cases, the actions or steps recited in the claims can be performed in a different order than in the embodiments and still achieve desirable results. Furthermore, the processes depicted in the accompanying drawings do not necessarily have to be in the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
The system, apparatus, module or unit set forth in the above embodiments may be implemented in particular by a computer chip or entity, or by a product having a certain function. One typical implementation is a computer. In particular, the computer may be, for example, a personal computer, a laptop computer, a cellular telephone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.
For convenience of description, the above devices are described as being functionally divided into various units, respectively. Of course, the functions of each element may be implemented in one or more software and/or hardware elements when implemented in the present specification.
It will be appreciated by those skilled in the art that the present description may be provided as a method, system, or computer program product. Accordingly, the present specification embodiments may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present description embodiments may take the form of a computer program product on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) having computer-usable program code embodied therein.
The present description is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the specification. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In one typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include volatile memory in a computer-readable medium, random Access Memory (RAM) and/or nonvolatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of computer-readable media.
Computer readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of storage media for a computer include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Disks (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by a computing device. Computer-readable media, as defined herein, does not include transitory computer-readable media (transmission media), such as modulated data signals and carrier waves.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises an element.
The description may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments. In particular, for system embodiments, since they are substantially similar to method embodiments, the description is relatively simple, as relevant to see a section of the description of method embodiments.
Finally, it should be noted that the method and system for managing a model database based on parameter matching disclosed in the embodiments of the present invention are only disclosed in the preferred embodiments of the present invention, and are only used for illustrating the technical scheme of the present invention, but not limiting the technical scheme; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art will understand that modifications may be made to the technical solutions described in the foregoing embodiments or equivalents may be substituted for some of the technical features thereof, and that these modifications or substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention in essence of the corresponding technical solutions.
Claims (5)
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202411238654.8A CN118796905B (en) | 2024-09-05 | 2024-09-05 | Model database management method and system based on parameter matching |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202411238654.8A CN118796905B (en) | 2024-09-05 | 2024-09-05 | Model database management method and system based on parameter matching |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN118796905A CN118796905A (en) | 2024-10-18 |
| CN118796905B true CN118796905B (en) | 2024-12-13 |
Family
ID=93025344
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202411238654.8A Active CN118796905B (en) | 2024-09-05 | 2024-09-05 | Model database management method and system based on parameter matching |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN118796905B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN119597875A (en) * | 2024-11-14 | 2025-03-11 | 广州云硕科技发展有限公司 | Operation and maintenance question-answering data processing method and system based on large language model |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116932909A (en) * | 2023-07-26 | 2023-10-24 | 中国工商银行股份有限公司 | Model recommendation method and device, processor and electronic equipment |
| CN118113685A (en) * | 2024-04-22 | 2024-05-31 | 长春设备工艺研究所 | Big data-based model database management system and method |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US11115432B2 (en) * | 2019-07-08 | 2021-09-07 | Servicenow, Inc. | Multi-application recommendation engine for a remote network management platform |
| WO2023084447A1 (en) * | 2021-11-11 | 2023-05-19 | Siteimprove A/S | Website content management, including generating recommendations for new content and website improvements |
| CN114282284B (en) * | 2021-12-27 | 2025-03-14 | 苏州同元软控信息技术有限公司 | Model data processing method, device, equipment and storage medium |
-
2024
- 2024-09-05 CN CN202411238654.8A patent/CN118796905B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116932909A (en) * | 2023-07-26 | 2023-10-24 | 中国工商银行股份有限公司 | Model recommendation method and device, processor and electronic equipment |
| CN118113685A (en) * | 2024-04-22 | 2024-05-31 | 长春设备工艺研究所 | Big data-based model database management system and method |
Also Published As
| Publication number | Publication date |
|---|---|
| CN118796905A (en) | 2024-10-18 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107358247B (en) | Method and device for determining lost user | |
| CN118796905B (en) | Model database management method and system based on parameter matching | |
| CN118520385B (en) | Intelligent ring data processing method and system based on data classification processing | |
| CN112070550A (en) | Keyword determination method, device and equipment based on search platform and storage medium | |
| US20170286522A1 (en) | Data file grouping analysis | |
| CN113742580A (en) | Target type data recall method and device, electronic equipment and storage medium | |
| CN118760528B (en) | Calculation force matching method and system based on artificial intelligence | |
| CN118446744B (en) | Rights and interests data conversion method and system based on artificial intelligence | |
| CN118760529A (en) | Method and system for determining computing power scheduling strategy based on heterogeneous planning | |
| CN116051118B (en) | Analysis method and device of behavior time sequence model | |
| CN110880005B (en) | Vector index establishing method and device and vector retrieving method and device | |
| CN118052582A (en) | Customer churn probability prediction method, apparatus, computer device and storage medium | |
| CN114792256B (en) | Crowd expansion method and device based on model selection | |
| CN117076770A (en) | Data recommendation method and device based on graph calculation, storage value and electronic equipment | |
| CN116560968A (en) | Simulation calculation time prediction method, system and equipment based on machine learning | |
| CN119940982B (en) | Clue analysis method and system based on association rules | |
| CN119668463B (en) | Model display processing method and system for slice data management | |
| CN114968028A (en) | Method, apparatus, and medium for dynamically changing menu layout based on user behavior analysis | |
| CN115705383A (en) | Sequence Recommendation Algorithm, System, Terminal and Media Based on Image Volume Neural Network Timing Feature Extraction | |
| CN119648860B (en) | Insertion chart data processing method and system based on prediction algorithm | |
| CN118642972B (en) | A software testing method and system based on multiple AI models | |
| CN111753206A (en) | Information pushing method and system | |
| CN119049740A (en) | Community health monitoring method and system based on neural network algorithm prediction | |
| CN117861217B (en) | Game guidance data processing method and system based on user behavior feedback | |
| CN119066356A (en) | Computer-based big data analysis method, system, storage medium and device |
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 |