CN116319875A - Data uploading method, device, equipment, computer storage medium and system - Google Patents
Data uploading method, device, equipment, computer storage medium and system Download PDFInfo
- Publication number
- CN116319875A CN116319875A CN202310186182.5A CN202310186182A CN116319875A CN 116319875 A CN116319875 A CN 116319875A CN 202310186182 A CN202310186182 A CN 202310186182A CN 116319875 A CN116319875 A CN 116319875A
- Authority
- CN
- China
- Prior art keywords
- data
- uploading
- production equipment
- normal
- model
- 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.)
- Pending
Links
Images
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/01—Protocols
- H04L67/12—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L43/00—Arrangements for monitoring or testing data switching networks
- H04L43/08—Monitoring or testing based on specific metrics, e.g. QoS, energy consumption or environmental parameters
- H04L43/0823—Errors, e.g. transmission errors
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L43/00—Arrangements for monitoring or testing data switching networks
- H04L43/16—Threshold monitoring
Landscapes
- Engineering & Computer Science (AREA)
- Computer Networks & Wireless Communication (AREA)
- Signal Processing (AREA)
- Health & Medical Sciences (AREA)
- Computing Systems (AREA)
- General Health & Medical Sciences (AREA)
- Medical Informatics (AREA)
- Environmental & Geological Engineering (AREA)
- Testing And Monitoring For Control Systems (AREA)
Abstract
The application belongs to the technical field of data uploading, and particularly relates to a data uploading method, a device, equipment, a computer storage medium and a system. The method is applied to an intelligent gateway, and operation data of different detection positions of the production equipment in operation are obtained by acquiring first initial data of the production equipment and performing data processing on the first initial data; judging whether the operation data of each detection position is normal data or not according to the preset threshold value ranges of different detection positions of the production equipment, and uploading the normal data to an AI model for operation when the operation data is normal data; therefore, before data uploading, abnormal data are screened out, normal data are only uploaded to the AI model, the stability of the operation of the AI model is guaranteed, and the AI model can output operation results faster and better.
Description
Technical Field
The application belongs to the technical field of data uploading, and particularly relates to a data uploading method, a device, equipment, a computer storage medium and a system.
Background
Artificial intelligence (Artificial Intelligence, AI) is a new technical science to study, develop theories, methods, techniques and application systems for simulating, extending and expanding human intelligence.
At present, in the running process of equipment, the equipment is influenced by the environment and the parameters of the equipment, so that abnormal data are generated, the abnormal data have a larger difference from the normal running data of the equipment, serious deviation and even different types and ranges of interference data occur, if the data are uploaded to the AI model for simulation learning, the impact is generated on the original normal simulation result, the maturity of the model is reduced, the low quality of the running result of the AI model is caused, the model learning time is increased, and even wrong result guidance occurs.
Therefore, how to reasonably upload data into the AI model is a problem to be solved at present.
Disclosure of Invention
The application provides a data uploading method, device, equipment, computer storage medium and system, which are used for solving the defects that in the AI model training process in the prior art, low-quality data can cause AI models to deviate from the type or range of model learning, so that the AI model operation learning is disordered, and the low-quality model operation result is caused.
In one aspect, the present application provides a data uploading method, applied to an intelligent gateway, including:
acquiring first initial data of production equipment, wherein the first initial data are data detected by different detection positions when the production equipment operates;
performing data processing on the first initial data to obtain operation data of different detection positions of the production equipment during operation;
judging whether the operation data of each detection position is normal data or not according to a preset threshold range corresponding to each detection position, wherein the preset threshold range is determined according to the technical requirements of the production equipment, and the technical requirements of different detection positions are different;
and if the operation data are normal data, uploading the normal data to an AI model.
Optionally, the data processing for the first initial data to obtain operation data of different detection positions of the production equipment during operation includes:
respectively carrying out protocol analysis and marking on the first initial data according to the detection positions to obtain a plurality of second initial data, wherein different second initial data correspond to different marks, and different marks correspond to different detection positions;
determining a data processing rule corresponding to each second initial data according to the marks of the plurality of second initial data, wherein the intelligent gateway stores a corresponding relation between the marks and the data processing rule;
and carrying out data processing on each second initial data according to the data processing rule to obtain the operation data of different detection positions of the production equipment during operation.
Optionally, the determining whether the operation data of each detection position is normal data according to the preset threshold range corresponding to each detection position includes:
acquiring technical requirements of the production equipment at different detection positions, wherein the technical requirements of the production equipment are stored in the intelligent gateway in advance;
determining a preset threshold range corresponding to each detection position according to the technical requirements;
judging whether the operation data is in a preset threshold range corresponding to the detection position;
if not, determining that the operation data is abnormal data;
if yes, determining that the operation data is normal data.
Optionally, if the operation data is normal data, uploading the normal data to an AI model includes:
acquiring format requirements of the AI model simulation parameters;
according to the format requirement, carrying out protocol conversion and standardized encapsulation on the normal data to obtain data to be uploaded;
and encrypting the data to be uploaded and uploading the data to an AI model.
Optionally, the uploading the normal data to the AI model includes:
classifying and storing the normal data according to the detection positions, wherein the storage spaces corresponding to different detection positions are different;
and uploading the normal data to an AI model after determining that the normal data storage is completed.
Optionally, the method further comprises: if the operation data is abnormal data,
the abnormal data is deleted and the data is deleted,
or generating abnormal data information, wherein the abnormal data information is used for indicating that the current operation data is abnormal data.
In a second aspect, the present application provides a data uploading device, including:
the device comprises an acquisition module, a detection module and a control module, wherein the acquisition module is used for acquiring first initial data of production equipment, wherein the first initial data are data detected by different detection positions when the production equipment operates;
the processing module is used for carrying out data processing on the first initial data to obtain operation data of different detection positions of the production equipment during operation;
the judging module is used for judging whether the operation data of each detection position is normal data or not according to a preset threshold range corresponding to each detection position, wherein the preset threshold range is determined according to the technical requirements of the production equipment, and the technical requirements of different detection positions are different;
and the uploading module is used for uploading the normal data to an AI model when the operation data are the normal data.
Optionally, the processing module is specifically configured to perform protocol analysis and marking on the first initial data according to a detection position, so as to obtain a plurality of second initial data, where different second initial data corresponds to different marks, and different marks correspond to different detection positions;
determining a data processing rule corresponding to each second initial data according to the marks of the plurality of second initial data, wherein the intelligent gateway stores a corresponding relation between the marks and the data processing rule;
and carrying out data processing on each second initial data according to the data processing rule to obtain the operation data of different detection positions of the production equipment during operation.
The acquisition module is further used for acquiring technical requirements of the production equipment at different detection positions, and the intelligent gateway stores the technical requirements of the production equipment in advance;
the processing module is further used for determining a preset threshold range corresponding to each detection position according to the technical requirements;
the judging module is specifically configured to judge whether the operation data is in a preset threshold range corresponding to the detection position;
the processing module is further configured to determine that the operation data is normal data if the operation data is within a preset threshold range corresponding to the detection position; and if the operation data is not in the preset threshold range corresponding to the detection position, determining that the operation data is abnormal data.
Optionally, the acquiring module is further configured to acquire a format requirement of the AI model simulation parameter;
the processing module is specifically configured to perform protocol conversion and standardized encapsulation on the normal data according to the format requirement to obtain data to be uploaded; encrypting the data to be uploaded;
the uploading module is specifically configured to upload the encrypted data to be uploaded to an AI model.
Optionally, the apparatus further includes: a storage module;
the storage module is used for classifying and storing the normal data according to the detection positions, wherein the storage spaces corresponding to different detection positions are different;
the uploading module is specifically configured to upload the normal data to an AI model after determining that the normal data is stored.
Optionally, the processing module is further configured to delete the abnormal data when the operation data is the abnormal data,
or generating abnormal data information, wherein the abnormal data information is used for indicating that the current operation data is abnormal data.
In a third aspect, the present application provides a data uploading device, including:
a memory;
a processor;
wherein the memory stores computer-executable instructions;
the processor executes computer-executable instructions stored in the memory to implement the data uploading method as described in the first aspect and various possible implementations of the first aspect.
In a fourth aspect, the present application provides a computer storage medium having stored thereon computer-executable instructions that are executed by a processor to implement the data uploading method as described in the first aspect and the various possible implementations of the first aspect.
In a fifth aspect, the present application provides a data upload system, including: production equipment, intelligent gateway and AI model;
the intelligent gateway is configured to collect data of the production device and upload the data to the AI model, where the intelligent gateway is configured to implement the data uploading method described in the first aspect and various possible implementation manners of the first aspect when the data uploading is performed.
According to the data uploading method, the first initial data of the production equipment are obtained, and the first initial data are subjected to data processing to obtain the operation data of different detection positions of the production equipment during operation; judging whether the operation data of each detection position is normal data or not according to the preset threshold value ranges of different detection positions of the production equipment, and uploading the normal data to an AI model for operation when the operation data is the normal data; therefore, before data uploading, abnormal data are screened out, normal data are only uploaded to the AI model, the stability of the operation of the AI model is guaranteed, and the AI model can output operation results faster and better.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the application and together with the description, serve to explain the principles of the application.
FIG. 1 is a block diagram of the operation of the data upload system provided herein;
FIG. 2 is a flowchart I of a data upload method provided herein;
FIG. 3 is a second flowchart of the data uploading method provided in the present application;
fig. 4 is a schematic structural diagram of a data uploading device provided in the present application;
fig. 5 is a schematic structural diagram of a data uploading device provided in the present application.
Specific embodiments thereof have been shown by way of example in the drawings and will herein be described in more detail. These drawings and the written description are not intended to limit the scope of the inventive concepts in any way, but to illustrate the concepts of the present application to those skilled in the art by reference to specific embodiments.
Detailed Description
For the purposes of making the objects, technical solutions and advantages of the present application more apparent, the technical solutions in the present application will be clearly and completely described below with reference to the drawings in the present application, and it is apparent that the described embodiments are some, but not all, embodiments of the present application. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are within the scope of the present disclosure.
The terms "first," "second," "third," "fourth" and the like in the description and in the claims and in the above drawings, if any, are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the invention described herein may be implemented, for example, in sequences other than those illustrated or otherwise described herein.
In the embodiments of the present application, words such as "exemplary" or "such as" are used to mean examples, illustrations, or descriptions. Any embodiment or design described herein as "exemplary" or "for example" should not be construed as preferred or advantageous over other embodiments or designs. Rather, the use of words such as "exemplary" or "such as" is intended to present related concepts in a concrete fashion.
First, terms related to the present application will be explained.
Artificial intelligence (Artificial Intelligence, AI): is a new technical science for researching and developing theories, methods, technologies and application systems for simulating, extending and expanding human intelligence.
And (3) an intelligent gateway: the network interconnection equipment has the functions of protocol conversion and data regrouping so as to be convenient for communication between networks of different types (different formats, protocols and structures), and can realize the functions of information acquisition, information input, information output, centralized control, remote control, linkage control and the like of various sensors, network equipment, cameras, hosts and other equipment in a local area network.
At present, in the running process of equipment, the equipment is influenced by the environment and the parameters of the equipment, so that abnormal data are generated, the abnormal data have a larger difference from the normal running data of the equipment, serious deviation and even different types and ranges of interference data occur, if the data are uploaded to the AI model for simulation learning, the impact is generated on the original normal simulation result, the maturity of the model is reduced, the low quality of the running result of the AI model is caused, the model learning time is increased, and even wrong result guidance occurs.
Therefore, how to reasonably upload data into the AI model is a problem to be solved at present.
In view of the above problems, the present application provides a data uploading method and system, and fig. 1 is a running structure diagram of the data uploading system provided in the present application. As shown in fig. 1, the intelligent gateway includes: the system comprises a local area network gateway communication module, a protocol processing module, a control core and a management module.
The local area network communication module supports various remote communication interfaces and is in communication link with industrial production equipment, so that access and data acquisition to the industrial production equipment are realized;
the protocol processing module supports and analyzes various industrial protocols, namely, comprises universal industrial protocols Modbus, OPC UA and the like, and also comprises various industrial equipment protocols such as PLC, CNC, industrial robot, numerical control equipment and the like. The protocol processing module is used for carrying out protocol analysis and data extraction on the data acquired by the local area network communication module, and then carrying out protocol conversion and standardized encapsulation.
The control core is the processing core of the whole intelligent gateway. The system controls other functional modules for data processing and analysis, calculation and other functions. After the data is subjected to protocol conversion, standardized encapsulation and encryption, the control core uploads the encrypted data to be uploaded to the AI model for operation through a remote communication interface such as 4G/5G, NB-IoT, cat.1 and the like.
The management module comprises a node management module, a service management module and a security management module.
The node management is used for carrying out label management on the data of different detection positions so as to distinguish the data of the different detection positions and ensure the integrity of the whole period of the data processing detected by each detection position.
The service management is used for distributing resources for the tasks and allocating computing resources so as to ensure the normal and stable operation of the control core.
The security management module is used for encrypting the data packet.
Optionally, the intelligent gateway may further include a protocol update module. The protocol updating module can be matched with the existing protocol of the intelligent gateway according to the industrial protocol adopted by the production equipment, and meanwhile, supplement can be added to the missing protocol, so that the normal operation of data acquisition protocol analysis is ensured.
The application provides a data uploading method which is applied to an intelligent gateway, wherein after data of production equipment are acquired, the equipment is processed, so that operation data of different detection positions are obtained, whether the operation data of each detection position are normal data is judged according to technical requirements of the different detection positions of the production equipment, and when the operation data are the normal data, the normal data are uploaded to an AI model for operation. According to the method, before data uploading, abnormal data are screened out, and normal data are only uploaded to the AI model, so that the stability of the operation of the AI model can be ensured, and the AI model can output an operation result faster and better.
The following describes the technical solutions of the present application and how the technical solutions of the present application solve the above technical problems in detail with specific embodiments. The following embodiments may be combined with each other, and the same or similar concepts or processes may not be described in detail in some embodiments. Embodiments of the present application will be described below with reference to the accompanying drawings.
Fig. 2 is a flowchart of a data uploading method according to an embodiment of the present application. The execution body of the embodiment may be, for example, an intelligent gateway. As shown in fig. 2, the data uploading method shown in this embodiment includes:
s101: first initial data of production equipment are obtained, wherein the first initial data are data detected by different detection positions when the production equipment operates.
Wherein initial data of the production facility can be acquired, for example, by means of sensors.
The first initial data are data detected by the detecting devices at different detecting positions when the production device is in operation.
It will be appreciated that the detection device comprises a plurality of types and that the first initial data comprises items of data detected by the production device at run-time by the detection device. Examples include: data indicating temperature, data indicating humidity, data indicating pressure, and the like. The data types of the data detected by the different detection positions may be the same or different, and the application is not limited thereto.
S102: and carrying out data processing on the first initial data to obtain operation data of different detection positions of the production equipment in operation.
After the first initial data is acquired, data processing is needed to be carried out on the data, so that operation data detected by different detection positions when the production equipment is operated are obtained.
For example: because the first initial data comprises data detected by a plurality of detection positions and the data types are different, the first initial data can be firstly classified and processed, and the data of different detection positions can be respectively processed, so that the operation data detected by different detection positions can be obtained. For another example: the first initial data may be processed first, and then classified according to the detection positions, so as to obtain operation data of different detection positions. The specific process of data processing is not limited, and the operation data of different detection positions can be obtained.
S103: judging whether the operation data of each detection position is normal data or not according to a preset threshold range corresponding to each detection position; if yes, step S104 is executed, and if no, step S105 is executed.
The preset threshold range is determined according to the technical requirements of production equipment, and the technical requirements of different detection positions are different.
The corresponding preset threshold range of each detection position can be determined according to the technical requirements of the production equipment at different detection positions. For example: determining a preset threshold range of the detection position 1 according to the technical requirements of production equipment, wherein the preset threshold range is as follows: 10-20 ℃; the preset threshold range of the detection position 2 is as follows: 20N-50N.
And judging whether the operation data of each detection position is normal data or not according to the preset threshold value ranges corresponding to different detection positions.
In making the judgment, for example: whether the operation data is within a corresponding preset threshold range or whether the operation data and the preset threshold range satisfy a specific condition or the like can be judged. The present embodiment does not limit the judging process, as long as it can be determined whether the operation data is normal data.
S104: and uploading the normal data to an AI model.
If the operation data is determined to be normal data, the normal data is uploaded to the AI model. Because the uploaded data is normal data, the operation of the AI model is not deviated from the type or range of model learning, thereby avoiding confusion of the AI model operation learning.
Alternatively, the specific implementation manner of uploading the data to be uploaded to the AI model may be, for example:
classifying and storing the normal data according to the detection position; and uploading the normal data to an AI model after determining that the normal data storage is completed.
Wherein, the storage spaces corresponding to different detection positions are different. After the normal data is acquired, the normal data can be classified and stored in the corresponding storage space according to the detection position, so that the data can be prevented from being confused, and the data processing efficiency is improved.
After the normal data is stored, the normal data is uploaded to an AI model.
S105: deleting the abnormal data or generating abnormal data information.
If it is determined that the operation data is abnormal data, the abnormal data cannot be uploaded to the AI model. At the moment, the abnormal data needs to be deleted, so that impact on the original normal simulation result of the AI model is avoided; or generating abnormal data information, wherein the abnormal data information is used for indicating that the current operation data is abnormal data.
According to the data uploading method provided by the embodiment, the first initial data of the production equipment are obtained, and the first initial data are subjected to data processing to obtain the operation data of different detection positions of the production equipment during operation; judging whether the operation data of each detection position is normal data or not according to the preset threshold value ranges of different detection positions of the production equipment, and uploading the normal data to an AI model for operation when the operation data is the normal data; therefore, before data uploading, abnormal data are screened out, normal data are only uploaded to the AI model, the stability of the operation of the AI model is guaranteed, and the AI model can output operation results faster and better.
Fig. 3 is a second flowchart of a data uploading method provided in an embodiment of the present application. The execution body of the embodiment may be, for example, an intelligent gateway. As shown in fig. 3, the embodiment describes a data uploading method in detail based on the embodiment of fig. 2, and the data uploading method shown in the embodiment includes:
s201: first initial data of production equipment are obtained, wherein the first initial data are data detected by different detection positions when the production equipment operates.
Step S201 is similar to step S101 described above, and will not be described again.
S202: and respectively carrying out protocol analysis and marking on the first initial data according to the detection positions to obtain a plurality of second initial data.
Protocol parsing refers to the capability of industrial equipment facing different protocols to realize data acquisition and transmission.
Existing AI models often operate under specific industrial protocols during learning. In actual production, because production equipment is complex and various, the used industrial protocols are various, and therefore extra processing time is brought to AI model operation data integration.
In this step, protocol analysis needs to be performed on the obtained first initial data, so that protocol conversion and standardized encapsulation are performed on normal data later.
Since the first initial data includes data detected at a plurality of detection positions, the data types are also different. For example: the detection position 1 detects data indicating temperature, the detection position 2 detects data indicating pressure, and the like. Therefore, when processing the data, the first initial data needs to be respectively subjected to data analysis and marking according to different detection positions, so that a plurality of second initial data are obtained.
The second, different initial data corresponds to different markers, which correspond to different detection positions. That is, the detection positions of the different second initial data are different, the marks are also different, and the detection positions, the marks and the second initial data have an association relation.
S203: and determining a data processing rule corresponding to each piece of second initial data according to the marks of the plurality of pieces of second initial data.
S204: and carrying out data processing on each second initial data according to the data processing rule to obtain the operation data of different detection positions of the production equipment during operation.
The intelligent gateway stores a corresponding relation between the marks and the data processing rules, and after the first initial data are marked to obtain a plurality of second initial data, the corresponding data processing rules can be determined according to the marks of each second initial data. The data processing rules corresponding to different marks may be the same or different, depending on the technical parameters of the second initial data corresponding to the marks.
After the data processing rules corresponding to each second initial data are determined, the second initial data can be processed by adopting the corresponding data processing rules, so that operation data of different detection positions of the production equipment in operation are obtained.
S205: obtaining technical requirements of the production equipment at different detection positions, and determining a preset threshold range corresponding to each detection position according to the technical requirements.
Wherein, the technical requirements of the production equipment are prestored in the intelligent gateway. The corresponding preset threshold range of each detection position can be determined according to the technical requirements of the production equipment at different detection positions. For example: determining a preset threshold range of the detection position 1 according to the technical requirements of production equipment, wherein the preset threshold range is as follows: 10-20 ℃; the preset threshold range of the detection position 2 is as follows: 20N-50N.
S206: judging whether the operation data is in a preset threshold range corresponding to the detection position; if yes, step S207 is executed, and if no, step S208 is executed.
After determining the corresponding preset threshold range of each detection position, judging whether the operation data of the detection position is in the corresponding preset threshold range, if so, determining that the operation data of the detection position is normal data; if not, determining that the operation data of the detection position is abnormal data.
For example: the preset threshold range of the detection position 1 is as follows: 10-20 ℃; the preset threshold range of the detection position 2 is as follows: 20N-50N. The operating data at detection position 1 was 15℃and the operating data at detection position 2 was 80N. The operation data of the detection position 1 is determined to be normal data, and the operation data of the detection position 2 is determined to be abnormal data.
S207: and determining that the operation data is normal data.
S208: determining that the operational data is anomalous data.
S209: deleting the abnormal data or generating abnormal data information.
Step S209 is similar to step S105 described above, and will not be described again.
S210: and acquiring the format requirement of the analog parameters of the AI model, and carrying out protocol conversion and standardized encapsulation on the normal data according to the format requirement to obtain the data to be uploaded.
Wherein the format requirements of the AI model simulation parameters are determined from the functional parameters. The format requirements of the simulation parameters of the AI model may be obtained when determining normal data at the time of operation data. The purpose of acquiring the format requirement of the analog parameters of the AI model in the step is to perform protocol conversion and standardized encapsulation on the data to be uploaded so as to realize standardized protocol encapsulation and improve the efficiency of AI model data processing.
S211: and encrypting the data to be uploaded and uploading the data to an AI model.
After normal data is standardized and packaged to obtain data to be uploaded, encryption processing is needed to be carried out on the data, so that the safety of data uploading is ensured.
According to the data uploading method, the data are marked according to the detection positions, so that corresponding data processing rules can be matched rapidly when the subsequent data processing is performed, data confusion is avoided, and the data processing efficiency is improved; meanwhile, according to the technical requirements of different detection positions, a corresponding preset threshold range is determined, and when the operation data is in the corresponding preset threshold range, the data is uploaded to the AI model, so that the uploading of abnormal data is avoided, the effectiveness of the data is ensured, and the AI model can output the operation result faster and better; and meanwhile, before uploading, normal data are subjected to protocol conversion, standardized encapsulation and encryption according to the protocol requirements of an AI model, so that standardized protocol encapsulation is realized, the safety of uploading data is ensured, and the efficiency of AI model data processing is improved.
Fig. 4 is a schematic structural diagram of a data uploading device provided in the present application. As shown in fig. 4, the present application provides a data uploading device, and the data uploading device 300 includes:
an obtaining module 301, configured to obtain first initial data of a production device, where the first initial data is data detected by different detection positions when the production device is running;
the processing module 302 is configured to perform data processing on the first initial data to obtain operation data of different detection positions of the production equipment during operation;
the judging module 303 is configured to judge whether the operation data of each detection position is normal data according to a preset threshold range corresponding to each detection position, where the preset threshold range is determined according to a technical requirement of the production equipment, and technical requirements of different detection positions are different;
and the uploading module 304 is configured to upload the normal data to the AI model when the operation data is normal data.
Optionally, the processing module 302 is specifically configured to perform protocol analysis and marking on the first initial data according to the detection position, so as to obtain a plurality of second initial data, where different second initial data corresponds to different marks, and different marks correspond to different detection positions;
determining a data processing rule corresponding to each second initial data according to the marks of the plurality of second initial data, wherein the intelligent gateway stores a corresponding relation between the marks and the data processing rule;
and carrying out data processing on each second initial data according to the data processing rule to obtain the operation data of different detection positions of the production equipment during operation.
The obtaining module 301 is further configured to obtain technical requirements of the production device at different detection positions, where the technical requirements of the production device are stored in the intelligent gateway in advance;
the processing module 302 is further configured to determine a preset threshold range corresponding to each detection position according to the technical requirement;
the judging module 303 is specifically configured to judge whether the operation data is in a preset threshold range corresponding to the detection position;
the processing module 302 is further configured to determine that the operation data is normal data if the operation data is within a preset threshold range corresponding to the detection position; and if the operation data is not in the preset threshold range corresponding to the detection position, determining that the operation data is abnormal data.
Optionally, the acquiring module 301 is further configured to acquire a format requirement of the AI model simulation parameter;
the processing module 302 is specifically configured to perform protocol conversion and standardized encapsulation on the normal data according to the format requirement, so as to obtain data to be uploaded; encrypting the data to be uploaded;
the uploading module 304 is specifically configured to upload the data to be uploaded in the encryption process to an AI model.
Optionally, the apparatus further includes: a storage module 305;
the storage module 305 is configured to store the normal data in a classified manner according to the detection positions, where storage spaces corresponding to different detection positions are different;
the uploading module 304 is specifically configured to upload the normal data to an AI model after determining that the normal data is stored.
Optionally, the processing module 302 is further configured to, when the operation data is abnormal data, delete the abnormal data,
or generating abnormal data information, wherein the abnormal data information is used for indicating that the current operation data is abnormal data.
Fig. 5 is a schematic structural diagram of a data uploading device provided in the present application. As shown in fig. 5, the present application provides a data uploading apparatus, the data uploading apparatus 400 includes: a receiver 401, a transmitter 402, a processor 403 and a memory 404.
A receiver 401 for receiving instructions and data;
a transmitter 402 for transmitting instructions and data;
Alternatively, the memory 404 may be separate or integrated with the processor 403.
When the memory 404 is provided separately, the electronic device further comprises a bus for connecting the memory 404 and the processor 403.
The application also provides a computer readable storage medium, in which computer executable instructions are stored, which when executed by a processor, implement a data uploading method executed by the data uploading device.
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.
While the present application has been described in connection with the preferred embodiments illustrated in the accompanying drawings, it will be readily understood by those skilled in the art that the scope of the application is not limited to such specific embodiments, and the above examples are intended to illustrate the technical aspects of the application, but not to limit it; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present application.
Claims (10)
1. The data uploading method is characterized by being applied to an intelligent gateway and comprising the following steps:
acquiring first initial data of production equipment, wherein the first initial data are data detected by different detection positions when the production equipment operates;
performing data processing on the first initial data to obtain operation data of different detection positions of the production equipment during operation;
judging whether the operation data of each detection position is normal data or not according to a preset threshold range corresponding to each detection position, wherein the preset threshold range is determined according to the technical requirements of the production equipment, and the technical requirements of different detection positions are different;
and if the operation data are normal data, uploading the normal data to an AI model.
2. The method according to claim 1, wherein the data processing the first initial data to obtain operation data of different detection positions of the production equipment during operation includes:
respectively carrying out protocol analysis and marking on the first initial data according to the detection positions to obtain a plurality of second initial data, wherein different second initial data correspond to different marks, and different marks correspond to different detection positions;
determining a data processing rule corresponding to each second initial data according to the marks of the plurality of second initial data, wherein the intelligent gateway stores a corresponding relation between the marks and the data processing rule;
and carrying out data processing on each second initial data according to the data processing rule to obtain the operation data of different detection positions of the production equipment during operation.
3. The method of claim 2, wherein the determining whether the operation data of each detection position is normal data according to the preset threshold range corresponding to each detection position comprises:
acquiring technical requirements of the production equipment at different detection positions, wherein the technical requirements of the production equipment are stored in the intelligent gateway in advance;
determining a preset threshold range corresponding to each detection position according to the technical requirements;
judging whether the operation data is in a preset threshold range corresponding to the detection position;
if not, determining that the operation data is abnormal data;
if yes, determining that the operation data is normal data.
4. The method of claim 1, wherein if the operational data is normal data, uploading the normal data to an AI model comprises:
acquiring format requirements of the AI model simulation parameters;
according to the format requirement, carrying out protocol conversion and standardized encapsulation on the normal data to obtain data to be uploaded;
and encrypting the data to be uploaded and uploading the data to an AI model.
5. The method of claim 1, wherein the uploading the normal data to an AI model comprises:
classifying and storing the normal data according to the detection positions, wherein the storage spaces corresponding to different detection positions are different;
and uploading the normal data to an AI model after determining that the normal data storage is completed.
6. The method according to claim 1, wherein the method further comprises: if the operation data is abnormal data,
the abnormal data is deleted and the data is deleted,
or generating abnormal data information, wherein the abnormal data information is used for indicating that the current operation data is abnormal data.
7. A data uploading apparatus, the apparatus comprising:
the device comprises an acquisition module, a detection module and a control module, wherein the acquisition module is used for acquiring first initial data of production equipment, wherein the first initial data are data detected by different detection positions when the production equipment operates;
the processing module is used for carrying out data processing on the first initial data to obtain operation data of different detection positions of the production equipment during operation;
the judging module is used for judging whether the operation data of each detection position is normal data or not according to a preset threshold range corresponding to each detection position, wherein the preset threshold range is determined according to the technical requirements of the production equipment, and the technical requirements of different detection positions are different;
and the uploading module is used for uploading the normal data to an AI model when the operation data are the normal data.
8. A data uploading device, comprising:
a memory;
a processor;
wherein the memory stores computer-executable instructions;
the processor executes computer-executable instructions stored in the memory to implement the data upload method of any one of claims 1-6.
9. A computer storage medium having stored therein computer executable instructions which when executed by a processor are adapted to carry out the data upload method according to any one of claims 1-6.
10. A data upload system, comprising: production equipment, intelligent gateway and AI model;
the intelligent gateway is used for collecting data of the production equipment and uploading the data to the AI model, and the intelligent gateway is used for realizing the data uploading method according to any one of claims 1-6 when the intelligent gateway performs data uploading.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310186182.5A CN116319875A (en) | 2023-03-01 | 2023-03-01 | Data uploading method, device, equipment, computer storage medium and system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310186182.5A CN116319875A (en) | 2023-03-01 | 2023-03-01 | Data uploading method, device, equipment, computer storage medium and system |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN116319875A true CN116319875A (en) | 2023-06-23 |
Family
ID=86828132
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310186182.5A Pending CN116319875A (en) | 2023-03-01 | 2023-03-01 | Data uploading method, device, equipment, computer storage medium and system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116319875A (en) |
-
2023
- 2023-03-01 CN CN202310186182.5A patent/CN116319875A/en active Pending
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US11158143B2 (en) | Vehicle diagnostic method and device, and computer readable storage medium | |
| EP3745271A1 (en) | Abnormality determination device, abnormality detection model creation server, and program | |
| CN112434809B (en) | Active learning-based model training method and device and server | |
| JP2021515498A (en) | Attribute-based policies for integrity monitoring and network intrusion detection | |
| CN113258995A (en) | Fault prediction method, device and computer readable storage medium | |
| CN110221581B (en) | Industrial control network monitoring device and method | |
| CN113988495A (en) | Building engineering monitoring method and system based on BIM and monitoring server | |
| CN112860676B (en) | Data cleaning method applied to big data mining and business analysis and cloud server | |
| CN109445820A (en) | A kind of intelligence contract upgrade method, system, equipment and computer storage medium | |
| US20190129392A1 (en) | Method, device, and computer program for configuring an intelligent electronic device | |
| CN110557292A (en) | Topological graph obtaining method, device and equipment based on distributed routing Internet of things | |
| CN111523782B (en) | Industrial control asset management method, device, equipment and storage medium | |
| CN116319875A (en) | Data uploading method, device, equipment, computer storage medium and system | |
| CN113259425A (en) | 61850 standard-based data conversion method, transformer substation gateway and storage medium | |
| CN113271439B (en) | Construction site safety monitoring configuration processing method and system | |
| CN112953786B (en) | Information processing method, device, system and computer readable storage medium | |
| CN112788153B (en) | Internet of things equipment upgrading management method, device, equipment and storage medium | |
| CN111950448B (en) | High-voltage isolating switch fault state detection method and device based on machine vision | |
| CN112039696B (en) | Method, device, equipment and medium for generating network topology structure | |
| CN114266288A (en) | Network element detection method and related device | |
| CN113961222A (en) | Rail transit software remote maintenance method and device and storage medium | |
| CN106603206B (en) | Wireless data processing and transmitting method | |
| CN115185780B (en) | Data acquisition method and system based on industrial Internet | |
| CN113746735B (en) | Method, device and equipment for detecting controller and computer storage medium | |
| CN114297029A (en) | Equipment asset management 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 |