CN110580185B - Data preprocessing method, device and storage medium - Google Patents
Data preprocessing method, device and storage medium Download PDFInfo
- Publication number
- CN110580185B CN110580185B CN201810581469.7A CN201810581469A CN110580185B CN 110580185 B CN110580185 B CN 110580185B CN 201810581469 A CN201810581469 A CN 201810581469A CN 110580185 B CN110580185 B CN 110580185B
- Authority
- CN
- China
- Prior art keywords
- data
- preprocessing
- script
- program
- steps
- 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
- 238000007781 pre-processing Methods 0.000 title claims abstract description 88
- 238000000034 method Methods 0.000 title claims abstract description 50
- 238000013515 script Methods 0.000 claims abstract description 92
- 238000012544 monitoring process Methods 0.000 claims abstract description 5
- 238000012545 processing Methods 0.000 claims description 14
- 238000004590 computer program Methods 0.000 claims description 8
- 230000006870 function Effects 0.000 claims description 8
- 238000001514 detection method Methods 0.000 description 8
- 230000008569 process Effects 0.000 description 8
- 238000012549 training Methods 0.000 description 7
- 238000004422 calculation algorithm Methods 0.000 description 5
- 238000005516 engineering process Methods 0.000 description 3
- 238000000605 extraction Methods 0.000 description 3
- 238000013473 artificial intelligence Methods 0.000 description 2
- 238000004140 cleaning Methods 0.000 description 2
- 238000004891 communication Methods 0.000 description 2
- 238000013527 convolutional neural network Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000002159 abnormal effect Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000005286 illumination Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000008707 rearrangement Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 230000007723 transport mechanism Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for program control, e.g. control units
- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
- G06F9/44—Arrangements for executing specific programs
- G06F9/448—Execution paradigms, e.g. implementations of programming paradigms
- G06F9/4482—Procedural
Landscapes
- Engineering & Computer Science (AREA)
- Software Systems (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Stored Programmes (AREA)
Abstract
The embodiment of the invention discloses a data preprocessing method, data preprocessing equipment and a storage medium, and belongs to the field of data preprocessing. Wherein the method comprises the following steps: monitoring the path of the original data; when the unprocessed original data is detected to exist, executing a preprocessing script or program corresponding to each step according to the execution sequence of each step preset in the configuration file; all steps of data preprocessing and the execution sequence thereof, data input paths and data output paths corresponding to the steps, and preprocessing scripts or programs are preset in the configuration file. According to the embodiment of the invention, through standardizing each step, each step is driven by data, the data is read from the data input path by the preprocessing script or program, and the generated result is stored in the data output path, so that various data and various program script languages can be applied, meanwhile, the user does not need to poll the execution results of each step, and the waiting of the execution results of each step is reduced.
Description
Technical Field
The present invention relates to the field of data preprocessing, and in particular, to an artificial intelligence data preprocessing method, apparatus, and storage medium.
Background
The training data needed by the artificial intelligent model training has a plurality of sources, various data file formats, various data content, various scripts or programs for data processing, and the artificial intelligent model training can be realized after the preprocessing. Different tasks (face, human shape and vehicle) and different algorithms, such as in face recognition, MTCNN (Multi-task convolutional neural networks, multi-task cascade convolutional neural network), need to write different preprocessing scripts, the required preprocessing steps are different, and the running time of the scripts is long or short.
At present, data preprocessing focuses on a specific step, and aims at automatic processing of file formats and different field types, each step of preprocessing is not standardized, each step is basically operated manually, and for the step with long processing time, the generated result is required to be checked by manual polling, and the next step is required to wait for the intermediate data output by preprocessing of the previous step.
Disclosure of Invention
In view of the above, an object of the embodiments of the present invention is to provide a data preprocessing method, apparatus and storage medium, so as to solve the technical problem that each step of preprocessing is not standardized, is basically performed manually, and needs to manually poll and view the generated result for the step with longer processing time.
The technical scheme adopted by the invention for solving the technical problems is as follows:
according to an aspect of an embodiment of the present invention, there is provided a data preprocessing method including:
monitoring the path of the original data;
when the unprocessed original data is detected to exist, executing a preprocessing script or program corresponding to each step according to the execution sequence of each step preset in the configuration file;
all steps of data preprocessing and the execution sequence thereof, data input paths and data output paths corresponding to all the steps, and preprocessing scripts or programs are preset in the configuration file.
According to another aspect of an embodiment of the present invention, there is provided a data preprocessing apparatus including a memory, a processor, and a computer program stored on the memory and executable on the processor, which when executed by the processor, implements the steps of the data preprocessing method described above.
According to still another aspect of the embodiments of the present invention, there is also provided a storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the above-described data preprocessing method.
The data preprocessing method, the device and the storage medium of the embodiment of the invention, by regarding the script or the program of executing the task of each step as a black box, each black box receives the preprocessing script or the program, the data input path and the data output path. The data is driven by the data among the steps, the data is read from the data input path by the preprocessing script or the program, and the generated result is stored in the data output path, so that the standardization is realized, and various data and various program script languages can be applied. Meanwhile, users do not need to poll the execution results of all the steps, and waiting among the steps is reduced.
Drawings
FIG. 1 is a flowchart of a data preprocessing method according to a first embodiment of the present invention;
FIG. 2 is a schematic diagram of a configuration file according to a first embodiment of the present invention;
FIG. 3 is a flowchart of a data preprocessing method according to a second embodiment of the present invention;
fig. 4 is a flowchart of a face recognition preprocessing method according to a third embodiment of the present invention.
The achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
In order to make the technical problems, technical schemes and beneficial effects to be solved more clear and obvious, the invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the particular embodiments described herein are illustrative only and are not limiting upon the invention.
Example 1
As shown in fig. 1, a data preprocessing method provided by an embodiment of the present invention includes:
s101, monitoring a path of the original data.
Specifically, the original data may be pre-processing of artificial intelligence training data, or may be big data requiring data cleaning. The data file format may be a variety of data sources including, but not limited to, in the form of images, text, or tables.
S102, executing a preprocessing script or program corresponding to each step according to the execution sequence of each step preset in the configuration file after detecting that unprocessed original data exists.
The preprocessing script or program is implemented by adopting the same or different program languages, and is used for preprocessing data under a data input path and storing a preprocessing result into the data output path.
Specifically, in the data preprocessing process, the flow of each step is basically consistent, and the original data calling script or program is read to process the data, so that a required result is generated. Therefore, the script or program for performing the task at each step is regarded as a black box in the embodiment of the present invention. In the configuration file, all steps of data preprocessing and the execution sequence thereof, data input paths, data output paths corresponding to the respective steps, and preprocessing scripts or programs are predefined. For example, in the schematic diagram of the configuration file shown in fig. 2, the defining preprocessing method includes N steps, numbered according to Step1, step2, … …, step N, where the data input path folder input, the data output path folder output, and the folder script of the data preprocessing script or program are respectively set. It will be appreciated that in general, the data output path of the previous step and the data input path of the next step are identical. The preprocessing script or program is provided by the user, and the user can adopt any programming language to realize according to the actual application of the user. However, the user must write the preprocessing script according to the agreed rules, i.e. the data input paths of the script or program are required to be parameterized and identical, and the data output paths are also required to be parameterized and identical.
For more flexibility, the pre-processing scripts or programs may be further standardized as entry scripts and subtask scripts or programs. The entry script is used for defining the execution sequence of the subtask script or program, and the subtask script or program is a series of subtask scripts or programs written according to preset rules and used for realizing the functions of the step. The basic content of the entry script is to call each script or program in the step to meet different requirements. In fig. 2, main.py is an entry Script, where both the entry Script and the subtask Script or program exist in the Script ipt path, and the subtask Script or program Script1 and Script2 are invoked by the entry Script.
In some preferred embodiments, in order to save disk space and improve scanning efficiency, after executing the preprocessing script or program corresponding to each step, the method further includes: and deleting the processed intermediate data under the data input path corresponding to each step.
In some preferred embodiments, in order to enable the user to know the data preprocessing situation in time, particularly after the preprocessing exception, the method further includes: and when the execution of the method is completed or failed, or each step is completed or failed, sending information to the user through the user address. The user address may be a mailbox address.
In the embodiment of the invention, each black box receives the preprocessing script or program, the data input path and the data output path by regarding the script or program for executing the tasks of each step as the black box. The data is driven by the data, the data is read from the data input path by the preprocessing script or the program, and the generated result is stored in the data output path, so that various data and various program script languages can be applied, and standardization is realized. Meanwhile, users do not need to poll the execution results of all the steps, and waiting among the steps is reduced.
Example two
As shown in fig. 3, a data preprocessing method provided by an embodiment of the present invention includes:
s301, predefining a configuration file for data preprocessing.
Wherein, according to actual application scene, define the preconditioning configuration file. In the configuration file, all steps of the data preprocessing, the execution sequence thereof, the data input path, the data output path, the entry script, and the subtask script or program corresponding to each step are predefined. The entry script is used to define the execution order of the subtask script or program. The basic content of the entry script is to call each script or program in the step, and judge whether to exit abnormally or continue the next step according to the return value. The subtask script or program is a series of subtasks script or program written according to preset rules to realize step functions. The number of subtask scripts or programs in each step and the adopted programming language are not required, and the subtask scripts or programs are completely written according to the own needs of users. The entry script solves the problems of different script realization languages, different numbers of scripts, different execution sequences and the like.
In order to enable a user to know the data preprocessing condition in time, manual polling is not needed, particularly after preprocessing is abnormal, the user is informed of timely processing, and the embodiment of the invention also configures the user address and sets prompt information to be fed back to the user through the user address when each step fails to execute or is executed successfully. The user address is a mailbox address.
S302, monitoring a path of the original data.
S303, calling an entry script of the first step after detecting that unprocessed original data exists, and executing a subtask script or program of the first step by the entry script.
Specifically, after the entry script of the first step executes a series of subtask scripts or programs of the first step, intermediate data after processing the original data is saved to the data output path of the first step, that is, under the data input path of the second step.
S304, scanning the data input path of the Nth step, and calling the entry script of the Nth step to execute the subtask script or the program of the Nth step if unprocessed intermediate data exists.
S305, judging whether the execution fails, if yes, turning to step S309, otherwise, executing step S306.
S306, sending a step processing success notice to a preset user address.
It should be noted that, as an alternative, a success notification may not be sent to the user, but may be sent to a preset user address after all the steps of the preprocessing are performed successfully.
S307, deleting the processed intermediate data in the data input path of the step.
S308, judging whether the step N is the last step, if so, returning to the step S302, and executing the step S309.
S309, N accumulates 1, and returns to step S302.
S310, sending a step processing failure notice to a preset user address.
In the embodiment of the invention, the data input path paths of each step are scanned and processed one by one according to the preprocessed configuration file. As shown in fig. 2, the Step1/input path is scanned from Step1, new data is found, step 1/src/entry script (e.g. main. Py) is called to perform preprocessing, mail notification is sent after the processing is finished, step2 is processed, the Step2/input path is scanned, and the processing is sequentially performed until the last Step StepN. After the first round is finished, the second round of scanning is started, and the Step1/input path is scanned from the beginning, so that the cycle is repeated. In some preferred embodiments, in order to save disk space and improve scanning efficiency, after executing the preprocessing script or program corresponding to each step, the method further includes: and deleting the processed intermediate data under the data input path corresponding to each step.
In the embodiment of the invention, the data input path of each step is scanned in turn according to the execution sequence of each step preset in the configuration file; when unprocessed intermediate data exists in the data input path of a certain step, the corresponding entry script of the step is called to execute the subtask script or the program of the step, so that the data driving between the steps is realized.
Example III
As shown in fig. 4, the embodiment of the present invention is illustrated with face recognition.
Taking the face picture preprocessing required by face recognition as an example, one source of the face picture is a famous face captured on a network, the captured face belonging to the same person is stored in the same path, and the face model training can be performed only after four steps of face detection, face positioning, face calibration, face weight removal and the like are preprocessed aiming at the picture under the path, and the four steps of products have corresponding processing scripts or programs, but are all manual operation, and the invention can be applied according to the following steps to be automated.
The step S401 is the steps of face detection, face positioning, face calibration and face rearrangement.
Step S402, respectively predefining subtasks of each step, and writing an entry function of each step and a script or program of each subtask.
Specifically, the face detection step is divided into three steps of background modeling, foreground extraction and block mass selection. Firstly, modifying scripts or programs such as background modeling, foreground extraction, block selection and the like, and transmitting a face input path and a face output path as parameters to the programs or scripts. Secondly, writing an entry function, calling background modeling, foreground extraction, block selection and the like in sequence, entering the next step if each small step is successfully called, and reporting errors and notifying the user if the small step is unsuccessful. Thus, the face detection step is completed.
Similarly, face positioning, face calibration and face weight removal are continuously predefined respectively. To this end, the script or programming of all steps is completed.
Step S403, writing a configuration file of face recognition, and writing the predefined steps and the execution sequence thereof, the entry script, the subtask script or program of each step and the data input path and the data output path into the configuration file.
Specifically, step1 corresponds to face detection, determines an input path, an output path and an entry script, and if notification is to be sent, user address information, such as a mailbox address, needs to be defined. Similarly, step2 corresponds to face positioning, step3 corresponds to face calibration, and Step4 corresponds to face weight removal. So far, the configuration of the preprocessing configuration file is completed.
Step S404, deploying the configuration file, the entry script, the subtask script or the program of each step into the environment, and starting the data preprocessing process.
In addition, in the tuning process of model training, higher quality requirements may be put on the data, and more refined pretreatment is often performed. Taking face recognition as an example, the original face detection algorithm is based on traditional image processing, faces are detected according to the outlines, textures and the like of images, the faces detected under the scenes of complex background, strong illumination and the like are not accurate, and an MTCNN algorithm with higher precision is needed to be changed. The MTCNN algorithm comes from the Shenzhen advanced technology institute, qiao Yu teacher group. Consists of 3 network structures (P-Net, R-Net, O-Net). Propos Network (P-Net): the network structure mainly obtains candidate windows of the face area. Finer Network (R-Net): and carrying out regression and maximum value on the candidate windows, and merging the highly overlapped candidate frames. Output Network (O-Net): and 5-point positioning coordinates of the face are obtained. This requires modification of the automated preprocessing script, which can operate as follows.
And 1, adopting an MTCNN algorithm to write a new face detection program or script.
The entry function of the face detection step is modified accordingly.
And 2, stopping the face recognition preprocessing process, and simultaneously clearing the data in the intermediate state.
And 3, replacing script or program of the face detection step.
And 4, starting a face recognition preprocessing process.
In the embodiment of the invention, the human face is taken as an example for explanation, and similar image recognition also comprises vehicle recognition and license plate recognition, but the application is not limited to image recognition, and the method can be applied to preprocessing of artificial intelligent data training and big data cleaning of all data formats.
Example IV
The embodiment of the invention provides data preprocessing equipment, which comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein the computer program realizes the steps of the data preprocessing method when being executed by the processor.
The data preprocessing device in the embodiment of the present invention belongs to the same concept as the methods in the first to third embodiments, and the specific implementation process is detailed in the corresponding method embodiment, and the technical features in the method embodiment are correspondingly applicable in the data preprocessing device embodiment, which is not described herein.
Example five
The embodiment of the invention provides a computer readable storage medium, wherein a computer program is stored on the computer readable storage medium, and when the computer program is executed by a processor, the steps of the data preprocessing method are realized.
The computer readable storage medium of the embodiment of the present invention belongs to the same concept as the methods of the first to third embodiments, and the specific implementation process is detailed in the corresponding method embodiment, and the technical features of the method embodiment are correspondingly applicable to the computer readable storage medium embodiment, and are not repeated herein.
According to the embodiment of the invention, through standardizing each step, each step is driven by data, the data is read from the data input path by the preprocessing script or program, and the generated result is stored in the data output path, so that various data and various program script languages can be applied, meanwhile, the user does not need to poll the execution results of each step, and the waiting of the execution results of each step is reduced.
Those of ordinary skill in the art will appreciate that all or some of the steps, systems, functional modules/units in the apparatus, and methods disclosed above may be implemented as software, firmware, hardware, and suitable combinations thereof.
In a hardware implementation, the division between the functional modules/units mentioned in the above description does not necessarily correspond to the division of physical components; for example, one physical component may have multiple functions, or one function or step may be performed cooperatively by several physical components. Some or all of the physical components may be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application specific integrated circuit. Such software may be distributed on computer readable media, which may include computer storage media (or non-transitory media) and communication media (or transitory media). The term computer storage media includes both volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data, as known to those skilled in the art. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital Versatile Disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by a computer. Furthermore, as is well known to those of ordinary skill in the art, communication media typically embodies computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media.
The preferred embodiments of the present invention have been described above with reference to the accompanying drawings, and thus do not limit the scope of the claims of the present invention. Any modifications, equivalent substitutions and improvements made by those skilled in the art without departing from the scope and spirit of the present invention shall fall within the scope of the appended claims.
Claims (8)
1. A method of preprocessing data, the method comprising:
monitoring the path of the original data;
when the unprocessed original data is detected to exist, executing a preprocessing script or program corresponding to each step according to the execution sequence of each step preset in the configuration file;
all steps of data preprocessing, the execution sequence of the steps, the data input path, the data output path and the preprocessing script or program corresponding to each step are preset in the configuration file, the preprocessing script or program is realized by adopting the same or different programming languages, and is used for preprocessing data in the data input path and storing the processing result in the data output path, wherein the data output path of the previous step is the same as the data input path of the next step;
wherein, according to the execution sequence of each step preset in the configuration file, executing the preprocessing script or program corresponding to each step includes: scanning the data input path of each step in turn according to the execution sequence of each step preset in the configuration file; when unprocessed intermediate data exists in the data input path of the step, the corresponding entry script of the step is invoked to execute at least one subtask script or program.
2. The data preprocessing method according to claim 1, wherein the preprocessing script or program includes an entry script, and at least one subtask script or program, wherein:
the entry script is used for defining the execution sequence of the at least one subtask script or program;
the subtask script or program is a series of subtask scripts or programs written according to preset rules and realizing the functions of the step.
3. The method for preprocessing data according to claim 1, wherein after executing the preprocessing script or program corresponding to each step, further comprising:
and deleting the processed intermediate data under the data input path corresponding to each step.
4. The method for preprocessing data according to claim 1, wherein a user address is further preset in the configuration file, and the user address includes a user mailbox.
5. The method of data preprocessing according to claim 1, characterized in that the method further comprises: and when the execution of the method is finished or each step is finished or fails, a prompt message is fed back to the user through the user address.
6. The method of any one of claims 1-5, wherein the raw data comprises an image comprising a face image, a vehicle image, or a license plate image.
7. A data preprocessing device, characterized in that it comprises a memory, a processor and a computer program stored on said memory and executable on said processor, which, when executed by said processor, implements the steps of the data preprocessing method according to any one of claims 1 to 6.
8. A storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the data preprocessing method according to any one of claims 1 to 6.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810581469.7A CN110580185B (en) | 2018-06-07 | 2018-06-07 | Data preprocessing method, device and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810581469.7A CN110580185B (en) | 2018-06-07 | 2018-06-07 | Data preprocessing method, device and storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110580185A CN110580185A (en) | 2019-12-17 |
| CN110580185B true CN110580185B (en) | 2023-10-31 |
Family
ID=68810221
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810581469.7A Active CN110580185B (en) | 2018-06-07 | 2018-06-07 | Data preprocessing method, device and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110580185B (en) |
Families Citing this family (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111414212B (en) * | 2020-03-27 | 2023-06-23 | 中国平安财产保险股份有限公司 | Global process control method, device, computer equipment and storage medium |
| CN113032042A (en) * | 2021-03-17 | 2021-06-25 | 中国工商银行股份有限公司 | Target file processing method and device and terminal equipment |
| CN113010232A (en) * | 2021-03-31 | 2021-06-22 | 建信金融科技有限责任公司 | Configuration-driven lightweight batch data processing method and device |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20100242034A1 (en) * | 2006-11-01 | 2010-09-23 | Microsoft Corporation | Distributing software products as an executable containing script logic with external resources |
| CN106021543A (en) * | 2016-05-26 | 2016-10-12 | 中国农业银行股份有限公司 | Data pre-processing method and device |
| CN107562790A (en) * | 2017-07-31 | 2018-01-09 | 北京北信源软件股份有限公司 | A kind of method and system for realizing data processing batch storage |
-
2018
- 2018-06-07 CN CN201810581469.7A patent/CN110580185B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20100242034A1 (en) * | 2006-11-01 | 2010-09-23 | Microsoft Corporation | Distributing software products as an executable containing script logic with external resources |
| CN106021543A (en) * | 2016-05-26 | 2016-10-12 | 中国农业银行股份有限公司 | Data pre-processing method and device |
| CN107562790A (en) * | 2017-07-31 | 2018-01-09 | 北京北信源软件股份有限公司 | A kind of method and system for realizing data processing batch storage |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110580185A (en) | 2019-12-17 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110580185B (en) | Data preprocessing method, device and storage medium | |
| CN109741517A (en) | A kind of invoice checking method, device and system | |
| CN108804215B (en) | Task processing method and device and electronic equipment | |
| WO2017193715A1 (en) | Method, device and apparatus for flow scheduling | |
| WO2020227163A1 (en) | Object Tracking and Redaction | |
| CN111666088A (en) | Pod replacement method and device, electronic equipment and computer-readable storage medium | |
| CN110297820B (en) | Data processing method, device, equipment and storage medium | |
| CN112114903A (en) | File loading method and device | |
| CN112540837B (en) | Service processing component calling method, system, electronic equipment and storage medium | |
| US20210042550A1 (en) | Information processing device, information processing method, and computer-readable recording medium recording information processing program | |
| CN111367934B (en) | Data consistency checking method, device, server and medium | |
| CN112817883A (en) | Method, device and system for adapting interface platform and computer readable storage medium | |
| US11663184B2 (en) | Information processing method of grouping data, information processing system for grouping data, and non-transitory computer readable storage medium | |
| CN115543662B (en) | Method and related device for issuing kafka message data | |
| CN112631850B (en) | Fault scene simulation method and device | |
| CN108985341A (en) | A kind of the training set appraisal procedure and system of neural network model | |
| CN114185938B (en) | Project traceability analysis method and system based on digital finance and big data traceability | |
| CN117251260A (en) | Data processing method and system | |
| CN110827261B (en) | Image quality detection method and device, storage medium and electronic equipment | |
| US20210312223A1 (en) | Automated determination of textual overlap between classes for machine learning | |
| CN111352395A (en) | Certificate making equipment scheduling method and device | |
| CN117033239B (en) | Control matching method and device, computer equipment and storage medium | |
| CN111311471A (en) | Batch picture segmentation, scheduling, processing and synthesizing system and method based on FPGA (field programmable Gate array) calculation | |
| CN111582109B (en) | Identification method, identification device, computer-readable storage medium, and electronic apparatus | |
| CN110298833B (en) | Picture processing method 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 |