CN110942017B - Multi-algorithm index comparison method and system based on automation - Google Patents
Multi-algorithm index comparison method and system based on automation Download PDFInfo
- Publication number
- CN110942017B CN110942017B CN201911163111.3A CN201911163111A CN110942017B CN 110942017 B CN110942017 B CN 110942017B CN 201911163111 A CN201911163111 A CN 201911163111A CN 110942017 B CN110942017 B CN 110942017B
- Authority
- CN
- China
- Prior art keywords
- algorithm
- comparison
- structured data
- algorithms
- data
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/94—Hardware or software architectures specially adapted for image or video understanding
-
- 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
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Databases & Information Systems (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Software Systems (AREA)
- Multimedia (AREA)
- Data Mining & Analysis (AREA)
- General Engineering & Computer Science (AREA)
- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention provides a multi-algorithm index comparison method and a system based on automation, wherein the method comprises the following steps: acquiring a material to be compared, wherein the material comprises an image or/and a video; sequentially calling different algorithms to process the material to obtain structured data of the material about multiple algorithms, wherein each algorithm in the multiple algorithms corresponds to different versions; and comparing the structured data with the respective corresponding reference data according to different algorithms to obtain a comparison result and multiple algorithm indexes. According to the invention, materials to be compared are automatically collected, and the materials comprise images or/and videos without manual assistance; different algorithms are automatically called according to the materials to process the materials, structured data of the materials about multiple algorithms are obtained, limited resources can be flexibly utilized through batch automatic scheduling and deployment algorithms, and the analysis efficiency is improved; meanwhile, through automatic comparison and summarization of structured data, the workload of workers is greatly reduced, and the comparison and analysis efficiency is improved.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a multi-algorithm index comparison method and system based on automation.
Background
In the image processing process, in the traditional multi-algorithm index comparison, each algorithm API interface needs to be called manually to process the acquired video, structured data analyzed by the algorithm are obtained, and then manual statistics, comparison and analysis are carried out. If analysis index comparison is carried out on a plurality of algorithms and different versions of the same algorithm, manual multi-algorithm index comparison is time-consuming, labor-consuming and low in efficiency, and requirements of agility and automation of devops (Development and Operations) cannot be met.
Disclosure of Invention
In view of the above disadvantages of the prior art, an object of the present invention is to provide an automatic-based multi-algorithm index comparison method and system, which are used to solve the problems of time consuming, labor consuming and low efficiency caused by manual multi-algorithm index comparison in the prior art.
In order to achieve the above objects and other related objects, a first aspect of the present invention provides an automated multi-algorithm index comparison method, including:
acquiring a material to be compared, wherein the material comprises an image or/and a video;
sequentially calling different algorithms to process the material to obtain structured data of the material about multiple algorithms, wherein each algorithm in the multiple algorithms corresponds to different versions;
and comparing the structured data with the respective corresponding reference data according to different algorithms to obtain a comparison result and multiple algorithm indexes.
In a second aspect of the present invention, an automatic-based multi-algorithm index comparison system is provided, which includes: .
The material acquisition module is used for acquiring a material to be compared, and the material comprises an image or/and a video;
the scheduling analysis module is used for calling different algorithms in sequence to process the material to obtain the structured data of the material about multiple algorithms, wherein each algorithm in the multiple algorithms corresponds to different versions;
and the comparison statistical module is used for comparing the structured data with the respective corresponding reference data according to different algorithms to obtain a comparison result and multiple algorithm indexes.
As described above, the multi-algorithm index comparison method and system based on automation of the present invention have the following advantages:
according to the invention, materials to be compared are automatically collected, and the materials comprise images or/and videos without manual assistance; different algorithms are automatically called according to the materials to process the materials, structured data of the materials about multiple algorithms are obtained, limited resources can be flexibly utilized through batch automatic scheduling and deployment algorithms, and the analysis efficiency is improved; meanwhile, through automatic comparison and summarization of structured data, the workload of workers is greatly reduced, and the comparison and analysis efficiency is improved.
Drawings
FIG. 1 is a flow chart of an automated multi-algorithm index comparison method according to an embodiment of the present invention;
FIG. 2 is a flowchart illustrating a step S3 of the method for comparing multiple algorithm indicators based on automation according to an embodiment of the present invention;
FIG. 3 is a flowchart illustrating a method for comparing multiple algorithm indicators based on automation according to an embodiment of the present invention;
FIG. 4 is a block diagram illustrating an exemplary automated multi-algorithm index comparison system according to an embodiment of the present invention;
FIG. 5 is a block diagram of a comparison statistics module in the automated multi-algorithm-index-based comparison system according to an embodiment of the present invention;
fig. 6 is a block diagram showing a complete structure of an automatic-based multi-algorithm index comparison system according to an embodiment of the present invention.
Detailed Description
The embodiments of the present invention are described below with reference to specific embodiments, and other advantages and effects of the present invention will be readily apparent to those skilled in the art from the disclosure of the present invention.
In the following description, reference is made to the accompanying drawings that describe several embodiments of the invention. The following detailed description is not to be taken in a limiting sense, and the scope of embodiments of the present invention is defined only by the claims of the issued patent. Spatially relative terms, such as "upper," "lower," "left," "right," "lower," "below," "lower," "above," "upper," and the like, may be used herein to facilitate describing one element or feature's relationship to another element or feature as illustrated in the figures.
Although the terms first, second, etc. may be used herein to describe various elements in some instances, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For example, the first preset threshold may be referred to as a second preset threshold, and similarly, the second preset threshold may be referred to as a first preset threshold, without departing from the scope of the various described embodiments. The first preset threshold and the preset threshold are both described as one threshold, but they are not the same preset threshold unless the context clearly dictates otherwise. Similar situations also include a first volume and a second volume.
As used herein, the terms "comprises," "comprising," "includes," "including," and/or "including," when used in this specification, specify the presence of stated features, steps, operations, elements, components, items, species, and/or groups, but do not preclude the presence, occurrence, or addition of one or more other features, steps, operations, elements, components, items, species, and/or groups thereof. A; b; c; a and B; a and C; b and C; A. b and C "are only exceptions to this definition should be done when combinations of elements, functions, steps or operations are inherently mutually exclusive in some manner.
Referring to fig. 1, a flowchart of an automatic-based multi-algorithm index comparison method according to an embodiment of the present invention includes:
the method comprises the following steps of S1, obtaining materials to be compared, wherein the materials comprise images or/and videos;
the material can be directly downloaded through the video management platform, the video stream or/and the picture stream can be automatically acquired, the material can also be uploaded according to the user requirement, the material to be compared is automatically acquired in the embodiment, and the workload of manual operation can be reduced.
S2, sequentially calling different algorithms to process the material to obtain structured data of the material about multiple algorithms, wherein each algorithm in the multiple algorithms corresponds to different versions;
kubernets (which is an open source and used for managing containerized applications on a plurality of hosts in a platform) is used for arranging algorithms stored in containers, and the kubernets aim to enable the containerized applications to be deployed simply and efficiently so as to provide a mechanism for deploying, planning, updating and maintaining the applications, so that the materials are sequentially scheduled to be analyzed by different types and versions of each pod, and structured data of multiple algorithms is obtained.
Here, according to the algorithm type and version set by the user in advance, the corresponding kubernets pod is scheduled to analyze the material in sequence, and because the kubernets scheduling can schedule the pods of different types and versions in sequence, a complete analysis service is provided for the outside, when an algorithm API (Application Programming Interface) test is completed, overhead of switching to the next algorithm or the next version is usually only tens of seconds, so that computer resources can be completely multiplexed, and efficiency is extremely high.
And S3, comparing the structured data with the respective corresponding reference data according to different algorithms to obtain a comparison result and multiple algorithm indexes.
In the embodiment, materials to be compared are automatically acquired, wherein the materials comprise images or/and videos without human assistance; different algorithms are automatically called according to the materials to process the materials, structured data of the materials about multiple algorithms are obtained, limited resources can be flexibly utilized through batch automatic scheduling and deploying algorithms, and the analysis efficiency is improved.
Referring to fig. 2, a flowchart of a step S3 of the automatic-based multi-algorithm index comparison method according to the embodiment of the present invention is detailed as follows:
step S301, receiving structured data corresponding to each algorithm, comparing the structured data with reference data corresponding to the structured data one by one according to time stamps or sequence numbers, and marking a comparison result in a source format (the same as an original format of the data);
the structured data is data logically expressed and realized by a two-dimensional table structure, and the data are compared one by one in any mode of time stamp or sequence number to obtain a comparison result of the structural data corresponding to each algorithm.
In some examples, all the received structured data is compared with reference data (which is commonly manually labeled within the industry as the source of the reference data to ensure its correctness and accuracy), the basic principle of comparison: the reference data and the structured data both contain video time stamps or picture sequence numbers, corresponding two pieces of structured information are obtained through the time stamps and the numbers, the structured information is compared one by one, and comparison results are marked according to source formats.
Step S302, structured data are counted based on different dimensions, and are processed in an abnormal value screening and difference comparison mode to obtain two data sets and multiple algorithm indexes.
The method comprises the steps of carrying out further screening and processing on a series of structured data, and processing the original data into result data (a data set and multiple algorithm indexes) which are clear in classification, good in sequencing, correct and reliable based on modes of data statistics of different dimensions, outlier screening, difference comparison and the like.
In another more specific example, the multi-algorithm indicators include, but are not limited to, snapshot recognition rate (algorithm correct snapshot number/baseline total snapshot number), false recognition rate (algorithm false snapshot number/algorithm parsed total snapshot number), feature recognition rate (recognition accuracy based on certain structured features), feature false recognition rate (false recognition rate based on certain structured features); statistical structured data binding algorithm classes and versions.
In this embodiment, through a series of structured data of automatic comparison, screening receipt, with the categorised sequencing of structured data of receipt, avoided follow-up artifical loaded down with trivial details work load of statistics of comparing, improved the efficiency of comparing the statistics, realized intelligent contrast statistical function.
Referring to fig. 3, a complete flowchart of an automatic-based multi-algorithm index comparison method according to an embodiment of the present invention is shown, and on the basis of the above embodiment, the method further includes:
and S4, visually displaying the statistical structured data on a front-end page in a chart form by taking the dimension or the keyword as a basis.
The front-end page adopts an echarts visual library under a bootstrap development framework, and a corresponding background of the front-end page adopts a python language to execute an operation flow under a django framework.
In some examples, a background python + django framework is adopted, interface logic processing is packaged through the django framework, a complex business operation process is carried out, and the embarrassing situation that each module in the background needs to bear various complex interface requests and an operation interface is unfriendly to a user is overcome.
In another more specific example, the front-end page is displayed by adopting bootstraps + echarts, and a simple, visual and strong front-end development framework is developed based on HTML, CSS and JavaScript, so that Web development is faster, and elegant HTML and CSS specifications can be provided. A visual library is realized on the basis of JavaScript based on Echarts, can be smoothly operated on a PC and mobile equipment, is compatible with most current browsers (IE 8/9/10/11, chrome, firefox, safari and the like), and provides a visual, interactive and highly personalized and customized data visual chart. The method is characterized in that the snapping recognition rate and the snapping error recognition rate among different algorithms and versions are displayed in a line graph mode by default, condition screening is supported, and other structured information can be selected through a pull-down menu to display the corresponding recognition rate and the corresponding error recognition rate.
In this embodiment, the result data after the comparison and statistics is displayed on the front-end page in the form of a chart or the like, on one hand, the input dimension or the keyword is taken as the statistical basis, which is beneficial for the staff to query the comparison result according to the requirement; on the other hand, the visual and clear contrast result of staff is facilitated.
Referring to fig. 4, a block diagram of an automatic-based multi-algorithm index comparison system according to an embodiment of the present invention includes:
the system comprises a material acquisition module 1, a comparison module and a comparison module, wherein the material acquisition module is used for acquiring a material to be compared, and the material comprises an image or/and a video;
the scheduling analysis module 2 is used for sequentially calling different algorithms to process the material to obtain the structured data of the material about multiple algorithms, wherein each algorithm in the multiple algorithms corresponds to different versions;
wherein the scheduling resolution module further comprises: and arranging algorithms stored in the container by utilizing kubernets, so that the material is sequentially scheduled to analyze each pod with different types and different versions to obtain the multi-algorithm structured data.
And the comparison statistical module 3 is used for comparing the structured data with the respective corresponding reference data according to different algorithms to obtain a comparison result and multiple algorithm indexes.
Referring to fig. 5, a structural block diagram of a comparison statistics module in an automatic-based multi-algorithm index comparison system according to an embodiment of the present invention includes:
the comparison unit is used for receiving the structured data corresponding to each algorithm, comparing the structured data with the corresponding reference data one by one according to the time stamps or sequence numbers, and marking the comparison result in a source format;
and the statistical unit is used for counting the structured data based on different dimensions and processing the structured data by utilizing an abnormal value screening and difference comparison mode to obtain two data sets and a multi-algorithm index.
Referring to fig. 6, a block diagram of a complete structure of an automatic-based multi-algorithm index comparison system according to an embodiment of the present invention is shown, and further includes, on the basis of fig. 5: and the page display module is used for visually displaying the statistical structured data on a front-end page in a chart form according to the dimension or the keyword.
The front-end page adopts an echarts visual library under a bootstrap development framework, and a corresponding background of the front-end page adopts a python language to execute an operation flow under a django framework.
Because the multi-algorithm index comparison system based on automation and the multi-algorithm index comparison method based on automation are in a one-to-one correspondence relationship, the technical details and technical effects related thereto are described with reference to the above method, and are not described in detail.
In other embodiments, it is assumed that the number of the existing picture material is T, the number of the existing video material is S, and the numbers of the preset algorithms to be scheduled are A1, A2, B1, and B2, where A1, A2, B1, and B2 are different versions of the same algorithm, respectively.
The material acquisition module is used for acquiring from an external platform, the scheduling analysis module analyzes required video and picture materials, corresponding material parameters are specified by a user, and the material acquisition module can acquire specified video and picture resources from the external platform through a network, and stores the specified video and picture resources into standard video and picture formats for the scheduling analysis module to use. Here, the material acquisition module acquires the materials T and S from the external platform according to the user selection.
The scheduling analysis module receives user input to select a designated material and an algorithm of a corresponding type and version, and firstly, a kubernetes pod (hereinafter referred to as pod) corresponding to a standard interface scheduling algorithm is used as a virtual environment, so that resources of a real environment can be utilized, complete network service is provided for the outside, and the destruction and creation efficiency of the pod is very high. And the material is transmitted in by parameters by calling an algorithm API in the pod, and after the material is processed by an internal algorithm in the pod, the structured data about the material characteristics is returned. Different algorithms are continuously scheduled to process different materials, and a series of multi-algorithm structured data based on different materials can be obtained. Here, the scheduling analysis module schedules pod of the algorithm A1, then transmits the materials T and S into pods respectively, obtains result data D (A1-T) and D (A1-S) after algorithm analysis, then deletes the pod destroying the A1, schedules pod of the algorithms A2, B1 and B2 in sequence, transmits the materials T and S into the result data D (A2-T), D (A2-S), D (B1-T), D (B1-S), D (B2-T) and D (B2-S) respectively,
the comparison statistical module further screens and processes a series of structured data obtained by the scheduling analysis module, processes original data into result data which is clear in classification, ordered and correct and reliable through data statistics based on different dimensions, outlier screening, difference comparison and the like, and then sends the result data to the page display module. Here, the comparison statistical module receives multiple pieces of result data, performs internal screening, and arranges the result data into two data sets D (a) and D (T) according to different materials after statistical processing.
And the page display module displays the data to a foreground page in the forms of numbers, tables, icons and the like after acquiring the result data processed by the comparison and statistics module. Here, the page display module obtains results D (a) and D (T) of two different algorithms based on the same material, which are processed by the comparison statistics module, and can display data results of different dimensions and different keywords according to user selection, and can well reflect relationships and differences between data, such as differences between certain indexes of the algorithm a and the algorithm B, index changes between different versions of the algorithm a, and the like.
It should be understood that, in various embodiments of the present invention, the sequence numbers of the above-mentioned processes do not mean the execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
It should be noted that, through the above description of the embodiments, those skilled in the art can clearly understand that part or all of the present invention can be implemented by software and combined with necessary general hardware platform. Based on the understanding that the functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may also be stored in a computer readable storage medium, the present invention also provides a computer readable storage medium storing at least one program that, when executed, implements any of the foregoing power resource management methods, such as the foregoing automation-based multi-algorithm index comparison method described with respect to fig. 1.
Based on this understanding, the technical solutions of the present invention may be embodied in the form of a software product, which may include one or more machine-readable media having stored thereon machine-executable instructions that, when executed by one or more machines such as a computer, network of computers, or other electronic devices, may cause the one or more machines to perform operations according to embodiments of the present invention. Such as the steps in the power resource management method. The machine-readable medium may include, but is not limited to, floppy diskettes, optical disks, CD-ROMs (compact disc-read only memories), magneto-optical disks, ROMs (read only memories), RAMs (random access memories), EPROMs (erasable programmable read only memories), EEPROMs (electrically erasable programmable read only memories), magnetic or optical cards, flash memory, or other type of media/machine-readable medium suitable for storing machine-executable instructions. The storage medium may be located in a local server or a third-party server, such as a third-party cloud service platform. The specific cloud service platform is not limited herein, such as the Ali cloud, tencent cloud, etc. The invention is operational with numerous general purpose or special purpose computing system environments or configurations. For example: a personal computer, dedicated server computer, mainframe computer, etc. configured as a node in a distributed system.
Also, any connection is properly termed a computer-readable medium. For example, if the instructions are transmitted from a website, server, or other remote source using a coaxial cable, fiber optic cable, twisted pair, digital Subscriber Line (DSL), or wireless technologies such as infrared, radio, and microwave, then the coaxial cable, fiber optic cable, twisted pair, DSL, or wireless technologies such as infrared, radio, and microwave are included in the definition of medium. It should be understood, however, that computer-readable-writable storage media and data storage media do not include connections, carrier waves, signals, or other transitory media, but are intended to be non-transitory, tangible storage media. Disk and disc, as used herein, includes Compact Disc (CD), laser disc, optical disc, digital Versatile Disc (DVD), floppy disk and blu-ray disc where disks usually reproduce data magnetically, while discs reproduce data optically with lasers.
It should be understood that, in various embodiments of the present invention, the sequence numbers of the above-mentioned processes do not mean the execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.
In conclusion, the invention automatically collects the materials to be compared, and the materials comprise images or/and videos without human assistance; different algorithms are automatically called according to the material to process the material, so that the structural data of the material about multiple algorithms is obtained, limited resources can be flexibly utilized through batch automatic scheduling and deploying algorithms, and the analysis efficiency is improved; meanwhile, through automatic comparison and summarization of structured data, the workload of workers is greatly reduced, and the comparison and analysis efficiency is improved. Therefore, the invention effectively overcomes various defects in the prior art and has high industrial utilization value.
The foregoing embodiments are merely illustrative of the principles and utilities of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or change the above-mentioned embodiments without departing from the spirit and scope of the present invention. Accordingly, it is intended that all equivalent modifications or changes which may be made by those skilled in the art without departing from the spirit and scope of the present invention as defined in the appended claims.
Claims (6)
1. An automatic-based multi-algorithm index comparison method is characterized by comprising the following steps:
acquiring a material to be compared, wherein the material comprises an image or/and a video;
sequentially calling different algorithms to process the material to obtain structured data of the material about multiple algorithms, wherein each algorithm in the multiple algorithms corresponds to different versions; arranging algorithms stored in the container by utilizing kubernets, and enabling the material to sequentially schedule and analyze each pod with different types and different versions to obtain structured data of multiple algorithms;
receiving structural data corresponding to each algorithm, comparing the structural data with corresponding reference data one by one according to a time stamp or a sequence number, and marking a comparison result in a source format;
structured data are counted based on different dimensions, and the structured data are processed by utilizing an abnormal value screening and difference comparison mode to obtain two data sets and multiple algorithm indexes.
2. The automated multi-algorithm index comparison method based on claim 1, further comprising: and visually displaying the statistical structured data on a front-end page in a chart form by taking the dimension or the keyword as a basis.
3. The automatic multi-algorithm index comparison method based on claim 1 is characterized in that an echarts visual library is adopted by a front-end page under a bootstrap development framework, and a corresponding background of the front-end page executes an operation flow under a django framework by adopting a python language.
4. An automation-based multi-algorithm index comparison system, the system comprising:
the material acquisition module is used for acquiring materials to be compared, and the materials comprise images or/and videos;
the scheduling analysis module is used for calling different algorithms in sequence to process the material to obtain the structured data of the material about multiple algorithms, wherein each algorithm in the multiple algorithms corresponds to different versions; arranging algorithms stored in the container by kubernets, and enabling the material to sequentially schedule and analyze each pod with different types and versions to obtain multi-algorithm structured data;
the comparison statistical module is used for comparing the structured data with the respective corresponding reference data according to different algorithms to obtain a comparison result and multiple algorithm indexes; the comparison statistic module comprises:
the comparison unit is used for receiving the structural data corresponding to each algorithm, comparing the structural data with the corresponding reference data one by one according to a time stamp or a sequence number, and marking a comparison result in a source format;
and the statistical unit is used for counting the structured data based on different dimensions, and processing the structured data by using an abnormal value screening and difference comparison mode to obtain two data sets and a multi-algorithm index.
5. The automated multi-algorithm index comparison system of claim 4, further comprising: and the page display module is used for visually displaying the statistical structured data on a front-end page in a chart form by taking the dimension or the keyword as a basis.
6. The automation-based multi-algorithm index comparison system of claim 4, wherein the front-end page adopts an echarts visual library under a bootstrap development framework, and the corresponding background executes the operation flow under a django framework by adopting a python language.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911163111.3A CN110942017B (en) | 2019-11-25 | 2019-11-25 | Multi-algorithm index comparison method and system based on automation |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911163111.3A CN110942017B (en) | 2019-11-25 | 2019-11-25 | Multi-algorithm index comparison method and system based on automation |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110942017A CN110942017A (en) | 2020-03-31 |
| CN110942017B true CN110942017B (en) | 2022-12-02 |
Family
ID=69908121
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201911163111.3A Active CN110942017B (en) | 2019-11-25 | 2019-11-25 | Multi-algorithm index comparison method and system based on automation |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110942017B (en) |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9442696B1 (en) * | 2014-01-16 | 2016-09-13 | The Math Works, Inc. | Interactive partitioning and mapping of an application across multiple heterogeneous computational devices from a co-simulation design environment |
| CN107133626A (en) * | 2017-05-10 | 2017-09-05 | 安徽大学 | Medical image classification method based on partial average stochastic optimization model |
| CN108829595A (en) * | 2018-06-11 | 2018-11-16 | Oppo（重庆）智能科技有限公司 | Test method, device, storage medium and electronic equipment |
| CN109189750A (en) * | 2018-09-06 | 2019-01-11 | 北京九章云极科技有限公司 | Operation method, data analysis system and the storage medium of data analysis workflow |
| CN109697153A (en) * | 2018-12-28 | 2019-04-30 | 浙江省公众信息产业有限公司 | Monitoring method, monitoring system and computer readable storage medium |
Family Cites Families (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101882263B (en) * | 2010-06-13 | 2012-09-05 | 中国人民解放军国防科学技术大学 | Demonstration and verification integrated system based on algorithm synthesis integration |
| CN104598219A (en) * | 2014-12-04 | 2015-05-06 | 东北大学 | Service evolving consistency judgment method and system based on change |
| CN110019201B (en) * | 2017-10-09 | 2023-03-07 | 阿里巴巴集团控股有限公司 | Method, device and system for generating structured data |
| CN108920259B (en) * | 2018-03-30 | 2022-06-24 | 华为云计算技术有限公司 | Deep learning job scheduling method, system and related equipment |
| US10824412B2 (en) * | 2018-04-27 | 2020-11-03 | Nutanix, Inc. | Method and apparatus for data driven and cluster specific version/update control |
| CN109167835B (en) * | 2018-09-13 | 2021-11-26 | 重庆邮电大学 | Physical resource scheduling method and system based on kubernets |
-
2019
- 2019-11-25 CN CN201911163111.3A patent/CN110942017B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9442696B1 (en) * | 2014-01-16 | 2016-09-13 | The Math Works, Inc. | Interactive partitioning and mapping of an application across multiple heterogeneous computational devices from a co-simulation design environment |
| CN107133626A (en) * | 2017-05-10 | 2017-09-05 | 安徽大学 | Medical image classification method based on partial average stochastic optimization model |
| CN108829595A (en) * | 2018-06-11 | 2018-11-16 | Oppo（重庆）智能科技有限公司 | Test method, device, storage medium and electronic equipment |
| CN109189750A (en) * | 2018-09-06 | 2019-01-11 | 北京九章云极科技有限公司 | Operation method, data analysis system and the storage medium of data analysis workflow |
| CN109697153A (en) * | 2018-12-28 | 2019-04-30 | 浙江省公众信息产业有限公司 | Monitoring method, monitoring system and computer readable storage medium |
Non-Patent Citations (2)
| Title |
|---|
| 基于Kubernetes的容器自动伸缩技术的研究;杨茂 等;《计算机与数字工程》;20190920;第47卷(第9期);2217-2220 * |
| 计算机应用与软件;祝家烨;《计算机应用与软件》;20181231;第35卷(第1期);281-287 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110942017A (en) | 2020-03-31 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US11870558B1 (en) | Identification of related event groups for IT service monitoring system | |
| JP7460237B2 (en) | Distributed Industrial Performance Monitoring and Analysis | |
| US10417644B2 (en) | Identifying clusters for service management operations | |
| US10628136B2 (en) | Internet of things (IoT) application development | |
| US10839504B2 (en) | User interface for managing defects | |
| US20200013070A1 (en) | Identifying clusters for service management operations | |
| EP2808790A2 (en) | Migration assessment for cloud computing platforms | |
| US9135714B1 (en) | Method and system for integrating a graphical user interface capture for automated test and retest procedures | |
| US20080010535A1 (en) | Automated and configurable system for tests to be picked up and executed | |
| CN103490941A (en) | Real-time monitoring on-line configuration method in cloud computing environment | |
| US20140237554A1 (en) | Unified platform for big data processing | |
| EP2631791A1 (en) | Method and apparatus for analyzing application program by analysis of source code | |
| CN114721964A (en) | Automatic generation method, device, equipment and medium of test case | |
| CN110716804A (en) | Method and device for automatically deleting useless resources, storage medium and electronic equipment | |
| CN112346952A (en) | Method, equipment and electronic equipment for regression testing of test cases | |
| CN111435329A (en) | Automatic testing method and device | |
| CN110942017B (en) | Multi-algorithm index comparison method and system based on automation | |
| US20180067837A1 (en) | Framework for detecting source code anomalies | |
| US20220342794A1 (en) | Operation logs acquiring device, operation logs acquiring method, and operation logs acquiring program | |
| CN114757157B (en) | Method, apparatus, device and medium for generating an aircraft kit | |
| CN111078905A (en) | Data processing method, device, medium and equipment | |
| JP2021506010A (en) | Methods and systems for tracking application activity data from remote devices and generating modified behavioral data structures for remote devices | |
| CN115080271A (en) | Event data processing method and device, electronic equipment and storage medium | |
| CN112256566B (en) | Fresh-keeping method and device for test cases | |
| US20210142104A1 (en) | Visual artificial intelligence in scada systems |
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 |