CN115293735A - Unmanned factory industrial internet platform monitoring management method and system - Google Patents
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Abstract
The embodiment of the application provides a monitoring and management method and system for an unmanned factory industrial internet platform, a computer readable medium and electronic equipment. The monitoring and management method for the industrial internet platform of the unmanned factory comprises the following steps: the method comprises the steps of obtaining production data in an industrial production environment through a sensor device, then screening the production data based on a preset data screening mode to obtain standby data, carrying out data analysis on the standby data, detecting whether abnormal data exceeding a normal range exists in the standby data, tracing the abnormal data, determining target equipment related to the abnormal data from industrial equipment, obtaining test data generated by detecting the target equipment, and maintaining the target equipment based on the test data. Through the data acquisition, screening and detection modes, the data processing efficiency and the supervision efficiency in the industrial production process are improved.
Description
Technical Field
The application relates to the technical field of computers, in particular to a monitoring management method and a monitoring management system for an industrial internet platform of an unmanned factory.
Background
In many industrial production environments, production conditions need to be supervised and managed in a manual mode, the mode causes the problems of low industrial production supervision efficiency and production cost waste, and the problems that production supervision is difficult to be carried out in a single manual mode under the conditions of large production environment and more production factors, and further, the production efficiency and the reliability are low are caused.
Disclosure of Invention
The embodiment of the application provides a monitoring and management method and system for an unmanned factory industrial internet platform, a computer readable medium and electronic equipment, and further data processing efficiency and supervision efficiency in an industrial production process can be improved to at least a certain extent.
Other features and advantages of the present application will be apparent from the following detailed description, or may be learned by practice of the application.
According to an aspect of an embodiment of the present application, there is provided a monitoring management method for an industrial internet platform of an unmanned factory, including: acquiring production data in an industrial production environment through a sensor device; screening the production data in a preset data screening mode to obtain standby data; performing data analysis on the standby data, and detecting whether abnormal data beyond a normal range exists in the standby data; tracing the source of the abnormal data, and determining target equipment associated with the abnormal data from industrial equipment; and obtaining test data generated by detecting the target equipment, and maintaining the target equipment based on the test data.
In some embodiments of the present application, based on the foregoing scheme, the screening the production data based on a preset data screening manner to obtain standby data includes:
determining a sampling period based on a target sampling number set in unit time;
and based on the sampling period, sampling and screening the production data to obtain standby data.
In some embodiments of the present application, based on the foregoing scheme, the screening the production data based on a preset data screening manner to obtain standby data includes:
if the data type of the production data is an image, generating image parameters corresponding to the image;
determining a corresponding definition grade of the image based on the image parameter;
and screening the images with the definition level higher than the set level as the standby data.
In an embodiment of the present application, generating image parameters corresponding to the image includes:
generating a gray level histogram corresponding to the image based on the pixel information of the image;
and determining a gray scale interval corresponding to the image and an interval pixel gray value corresponding to each gray scale interval as the image parameters based on the gray histogram and preset gray scale interval parameters.
In an embodiment of the present application, determining, based on the image parameter, a corresponding sharpness level of the image includes:
determining the proportion of each gray scale interval in the total gray value of the image based on the interval pixel gray value corresponding to each gray scale interval;
determining a gray average value of the image based on the total gray value of the image and the gray level of the image;
calculating a gray variance based on the interval pixel gray value corresponding to each gray level interval, the gray mean value and the ratio;
and determining the definition level corresponding to the gray variance based on the set definition level range.
In some embodiments of the present application, based on the foregoing solution, the performing data analysis on the backup data to detect whether there is abnormal data beyond a normal range in the backup data includes:
acquiring a normal range corresponding to the standby data from a database based on the data identification of the standby data;
and detecting whether abnormal data exceeding the normal range exists in the standby data or not based on the numerical value of the standby data and the normal range.
In some embodiments of the present application, based on the foregoing solution, the tracing the abnormal data and determining a target device associated with the abnormal data from an industrial device includes:
and determining the target equipment corresponding to the abnormal data from an industrial equipment database in a character matching mode based on the data identification of the abnormal data.
In some embodiments of the present application, based on the foregoing solution, the method further includes:
when the number of the target devices obtained through tracing is larger than or equal to a set threshold value, determining the association degree between the functional parameters and the abnormal data based on the functional parameters of the target devices;
and determining the target equipment which is most related to the abnormal data through the relevance.
In some embodiments of the present application, based on the foregoing solution, the method further includes:
adding the target equipment into a fault equipment database, and recording the fault times of the target equipment;
and when the failure times of the equipment in the failure equipment database are greater than or equal to a failure threshold value, marking the target equipment as eliminated equipment so as to ensure the reliability of the production equipment participating in production.
In some embodiments of the present application, based on the foregoing solution, the method further includes:
in the industrial production process, recording the production duration of each industrial device;
and when the production time is greater than or equal to a time threshold, suspending the operation of the industrial equipment. The device is used for carrying out warranty and detection on related equipment, and the running reliability of the equipment is improved.
According to an aspect of an embodiment of the present application, there is provided an unmanned factory industry internet platform monitoring management system, including:
an acquisition unit for acquiring production data in an industrial production environment by a sensor device;
the screening unit is used for screening the production data based on a preset data screening mode to obtain standby data;
the analysis unit is used for carrying out data analysis on the standby data and detecting whether abnormal data exceeding a normal range exists in the standby data;
the tracing unit is used for tracing the abnormal data and determining target equipment related to the abnormal data from industrial equipment;
and the maintenance unit is used for acquiring test data generated by detecting the target equipment and maintaining the target equipment based on the test data.
In some embodiments of the present application, based on the foregoing scheme, the screening unit includes:
the period unit is used for determining a sampling period based on the target sampling number set in unit time;
and the sampling unit is used for sampling and screening the production data based on the sampling period to obtain standby data.
In some embodiments of the present application, based on the foregoing scheme, the screening unit includes:
the parameter unit is used for generating image parameters corresponding to the images if the data type of the production data is the images;
the grade unit is used for determining the definition grade corresponding to the image based on the image parameter;
and the data unit is used for screening the images with the definition level higher than the set level as the standby data.
In some embodiments of the present application, based on the foregoing scheme, the image parameters corresponding to the image include:
generating a gray level histogram corresponding to the image based on the pixel information of the image;
and determining a gray scale interval corresponding to the image and an interval pixel gray value corresponding to each gray scale interval as the image parameters based on the gray histogram and preset gray scale interval parameters.
In some embodiments of the present application, based on the foregoing solution, the determining, based on the image parameter, a corresponding sharpness level of the image includes:
determining the proportion of each gray scale interval in the total gray value of the image based on the interval pixel gray value corresponding to each gray scale interval;
determining a gray average value of the image based on the total gray value of the image and the gray level of the image;
calculating a gray variance based on the interval pixel gray value corresponding to each gray scale interval, the gray mean value and the ratio;
and determining the definition level corresponding to the gray variance based on the set definition level range.
In some embodiments of the present application, based on the foregoing solution, the performing data analysis on the backup data to detect whether there is abnormal data beyond a normal range in the backup data includes:
acquiring a normal range corresponding to the standby data from a database based on the data identification of the standby data;
and detecting whether abnormal data exceeding the normal range exists in the standby data or not based on the numerical value of the standby data and the normal range.
In some embodiments of the present application, based on the foregoing solution, the tracing the abnormal data and determining a target device associated with the abnormal data from an industrial device includes:
and determining the target equipment corresponding to the abnormal data from an industrial equipment database in a character matching mode based on the data identification of the abnormal data.
In some embodiments of the present application, based on the foregoing solution, the method further includes:
when the number of the target devices obtained through tracing is larger than or equal to a set threshold value, determining the association degree between the functional parameters and the abnormal data based on the functional parameters of the target devices;
and determining the target equipment with the maximum association with the abnormal data through the association degree.
In some embodiments of the present application, based on the foregoing solution, the method further includes:
adding the target equipment into a fault equipment database, and recording the fault times of the target equipment;
and when the failure times of the equipment in the failure equipment database are greater than or equal to a failure threshold value, marking the target equipment as eliminated equipment so as to ensure the reliability of the production equipment participating in production.
In some embodiments of the present application, based on the foregoing solution, the method further includes:
in the industrial production process, recording the production duration of each industrial device;
and when the production time is greater than or equal to a time threshold, suspending the operation of the industrial equipment. The method and the device have the advantages that the related devices are guaranteed to be repaired and detected, and the running reliability of the devices is improved.
According to an aspect of an embodiment of the present application, there is provided a computer readable medium, on which a computer program is stored, the computer program, when executed by a processor, implements the unmanned factory industry internet platform monitoring management method as described in the above embodiment.
According to an aspect of an embodiment of the present application, there is provided an electronic device including: one or more processors; a storage device for storing one or more programs, which when executed by the one or more processors, cause the one or more processors to implement the unmanned plant industrial internet platform monitoring management method as described in the above embodiments.
According to an aspect of an embodiment of the present application, there is provided a computer program product or a computer program comprising computer instructions stored in a computer readable storage medium. The processor of the computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the unmanned factory industry internet platform monitoring and management method provided in the above-mentioned various optional implementation modes.
In the technical scheme provided by some embodiments of the application, production data in an industrial production environment is obtained through a sensor device, then, based on a preset data screening mode, the production data is screened to obtain standby data, data analysis is performed on the standby data, whether abnormal data exceeding a normal range exists in the standby data is detected, the abnormal data is traced, target equipment associated with the abnormal data is determined from the industrial equipment, test data generated by detecting the target equipment is obtained, and the target equipment is maintained based on the test data. Through the data acquisition, screening and detection modes, the data processing efficiency and the supervision efficiency in the industrial production process are improved.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the application.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and together with the description, serve to explain the principles of the application. It is obvious that the drawings in the following description are only some embodiments of the application, and that for a person skilled in the art, other drawings can be derived from them without inventive effort.
FIG. 1 schematically illustrates a flow diagram of a method for unmanned factory industry Internet platform monitoring management according to an embodiment of the present application;
FIG. 2 schematically shows a flow diagram for obtaining backup data according to an embodiment of the present application;
FIG. 3 schematically illustrates a block diagram of an unmanned factory industrial Internet platform monitoring management system according to an embodiment of the present application;
FIG. 4 illustrates a schematic structural diagram of a computer system suitable for use in implementing the electronic device of an embodiment of the present application.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. Example embodiments may, however, be embodied in many different forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of example embodiments to those skilled in the art.
Furthermore, the described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of embodiments of the application. One skilled in the relevant art will recognize, however, that the subject matter of the present application can be practiced without one or more of the specific details, or with other methods, components, devices, steps, and so forth. In other instances, well-known methods and systems, implementations, or operations have not been shown or described in detail to avoid obscuring aspects of the application.
The block diagrams shown in the figures are functional entities only and do not necessarily correspond to physically separate entities. I.e. these functional entities may be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor means and/or microcontroller means.
The flow charts shown in the drawings are merely illustrative and do not necessarily include all of the contents and operations/steps, nor do they necessarily have to be performed in the order described. For example, some operations/steps may be decomposed, and some operations/steps may be combined or partially combined, so that the actual execution sequence may be changed according to the actual situation.
The implementation details of the technical solution of the embodiment of the present application are set forth in detail below:
fig. 1 illustrates a flowchart of an unmanned plant industrial internet platform monitoring management method, which may be performed by a server, according to an embodiment of the present application. Referring to fig. 1, the method for monitoring and managing an industrial internet platform of an unmanned factory includes at least steps S110 to S150, which are described in detail as follows:
in step S110, production data in an industrial production environment is acquired by a sensor device.
In one embodiment of the present application, the sensor is previously arranged in the production environment, and the sensor in this embodiment may include a temperature, humidity or camera device or the like type sensor, so as to obtain the production data in the industrial production environment through the sensor device.
Optionally, the production data in this implementation may be acquired by a sensor in real time and through a wireless network.
In step S120, the production data is screened based on a preset data screening manner to obtain standby data.
In an embodiment of the present application, after the production data is obtained, because the number and the type are large and the data amount is large, the production data is screened based on a preset data screening manner in this embodiment, for example, the more important data is selected and used as the standby data to perform the subsequent data analysis processing.
In an embodiment of the present application, screening the production data based on a preset data screening manner to obtain spare data includes:
determining a sampling period based on a target sampling number set in unit time;
and based on the sampling period, sampling and screening the production data to obtain standby data.
In an embodiment of the present application, a target sampling number corresponding to a unit time is preset, for example, the target sampling number to be sampled within one hour is 5. And then determining the sampling period Per _ sam as follows according to the quotient of the data volume acquired in the unit time t and the target sampling number Per _ qua:
wherein, the first and the second end of the pipe are connected with each other,
indicating a preset sampling parameter, and Dat _ s indicating the amount of data acquired at the s-th time. By the method, the corresponding sampling period can be obtained, then sampling screening is carried out on the production data based on the sampling period to obtain the standby data, the data processing amount is reduced, and the detection and processing efficiency of the production data is improved.
In an embodiment of the present application, as shown in fig. 2, the screening the production data based on a preset data screening manner to obtain spare data includes:
s210, if the data type of the production data is an image, generating image parameters corresponding to the image;
s220, determining the definition level corresponding to the image based on the image parameter;
and S230, screening the image with the definition level higher than the set level as the standby data.
When the production data is an image, the definition of the image is measured in this embodiment, so that the image with higher definition is selected as the spare data. For example, an image with a large data amount may be selected as the spare data to improve the accuracy of image recognition.
In an embodiment of the present application, generating image parameters corresponding to the image includes:
generating a gray level histogram corresponding to the image based on the pixel information of the image;
and determining a gray scale interval corresponding to the image and an interval pixel gray value corresponding to each gray scale interval as the image parameters based on the gray histogram and preset gray scale interval parameters.
Specifically, the image parameters in this embodiment include data information such as a total gray level value of the image, a gray level interval, and an interval pixel gray level value corresponding to each gray level interval. The total image gray value represents the gray value of the whole image, the image gray level represents the number of gray levels defined based on a set gray level interval, the gray level interval represents each gray level interval defined based on the gray level interval, and the interval pixel gray value represents the sum of the gray values of each interval.
Firstly, gray level conversion is carried out according to pixel information of an image, and a gray level histogram corresponding to the image is generated. And then determining a gray level interval based on preset gray level interval parameters, determining the gray level corresponding to the image as the quotient of the total gray level number and the gray level interval parameters as the gray level of the image, and adding the pixel gray values in each gray level to obtain the interval pixel gray value corresponding to each gray level. For example, if the gray scale interval parameter is 3, 3 adjacent gray scales are set as one gray scale interval. And then adding the gray values corresponding to the 3 gray levels in the gray level interval to obtain the interval pixel gray value corresponding to the gray level interval.
In an embodiment of the present application, determining, based on the image parameter, a corresponding sharpness level of the image includes:
determining the proportion of each gray scale interval in the total gray value of the image based on the interval pixel gray value corresponding to each gray scale interval;
determining a gray average value of the image based on the total gray value of the image and the gray level of the image;
calculating a gray variance based on the interval pixel gray value corresponding to each gray scale interval, the gray mean value and the ratio;
and determining the definition level corresponding to the gray variance based on the set definition level range.
Specifically, in this embodiment, based on the interval pixel gray value Gra _ i corresponding to each gray level interval, it is determined that the proportion Pro _ i of each gray level in the total image gray value Gra _ tae is:
wherein i represents an identifier corresponding to a gray scale interval, and the maximum value of the identifier corresponding to the gray scale interval is k, namely the gray scale of the image.
Determining the gray average value Gra _ ave of the image as follows based on the total gray value and the gray level of the image:
based on the interval pixel gray value corresponding to each gray level, the gray mean value and the ratio, the gray variance Var is calculated as:
in the embodiment, the definition degree of the image is evaluated by calculating the variance of the image, where the variance is used to represent the degree of the difference between the gray value corresponding to each gray scale interval and the image gray mean value, and a higher variance represents a higher difference, thereby representing a higher difference between pixels of the image, i.e. a sharper image. The definition level range corresponding to each definition level is preset, and after the gray variance is obtained through calculation, the corresponding definition level is determined based on the gray variance and the definition level range. And screening the images with the definition levels higher than the set level as the standby data. So as to improve the image quality and further improve the accuracy of data analysis.
In step S130, data analysis is performed on the spare data, and it is detected whether there is abnormal data exceeding a normal range in the spare data.
In one embodiment of the present application, after the backup data is determined, the backup data is analyzed, for example, by a pre-trained analytical model, to detect abnormal data therein that is outside of a normal range.
In an embodiment of the present application, performing data analysis on the backup data to detect whether there is abnormal data beyond a normal range in the backup data includes:
acquiring a normal range corresponding to the standby data from a database based on the data identification of the standby data;
and detecting whether abnormal data exceeding the normal range exists in the standby data or not based on the numerical value of the standby data and the normal range.
Specifically, a corresponding normal range of the backup data is determined from the database according to the data identifier of the backup data, and whether abnormal data exceeding the normal range exists in the backup data is judged by comparing the value of the backup data with the normal range.
In step S140, tracing the abnormal data, and determining a target device associated with the abnormal data from the industrial devices.
In one embodiment of the present application, after the abnormal data is detected, the target device associated with the abnormal data is determined from the industrial device by identification based on the pre-stored information of the industrial device.
In an embodiment of the present application, tracing the source of the abnormal data and determining a target device associated with the abnormal data from an industrial device includes:
and determining the target equipment corresponding to the abnormal data from an industrial equipment database in a character matching mode based on the data identification of the abnormal data.
Specifically, based on the data identifier of the abnormal data and the data identifier in the industrial equipment database, the abnormal data and the data identifier in the industrial equipment database are subjected to character matching, so that the target equipment corresponding to the abnormal data is determined from the industrial equipment database.
Further, this embodiment further includes:
when the quantity of the target equipment obtained by tracing is larger than or equal to a set threshold value, determining the association degree between the functional parameters and the abnormal data based on the functional parameters of the target equipment;
and determining the target equipment with the maximum association with the abnormal data through the association degree.
Specifically, the probability of influence and generation of abnormal data is determined based on analysis of functional parameters of the target device, and is used as the association degree, so that the target device which is most associated with the abnormal data is determined through the association degree, and the pertinence and the accuracy of abnormal detection are improved.
In step S150, test data generated by detecting the target device is acquired, and the target device is maintained based on the test data.
In one embodiment of the application, after the target device is determined, alignment is detected to generate test data, and maintenance is performed on the target device based on the test data.
Further, this embodiment further includes:
adding the target equipment into a fault equipment database, and recording the fault times of the target equipment;
and when the failure times of the equipment in the failure equipment database are greater than or equal to a failure threshold value, marking the target equipment as obsolete equipment so as to ensure the reliability of the production equipment participating in production.
Further, the present embodiment further includes:
in the industrial production process, recording the production duration of each industrial device;
and when the production time is greater than or equal to a time threshold, the industrial equipment is paused. The device is used for carrying out warranty and detection on related equipment, and the running reliability of the equipment is improved.
In the technical scheme provided by some embodiments of the application, production data in an industrial production environment is obtained through a sensor device, then, based on a preset data screening mode, the production data is screened to obtain standby data, data analysis is performed on the standby data, whether abnormal data exceeding a normal range exists in the standby data is detected, the abnormal data is traced, target equipment associated with the abnormal data is determined from the industrial equipment, test data generated by detecting the target equipment is obtained, and the target equipment is maintained based on the test data. Through the data acquisition, screening and detection modes, the data processing efficiency and the supervision efficiency in the industrial production process are improved.
The following describes embodiments of the apparatus of the present application, which may be used to implement the method for monitoring and managing an unmanned factory industrial internet platform in the above embodiments of the present application. It will be appreciated that the apparatus may be a computer program (comprising program code) running on a computer device, for example an application software; the apparatus may be used to perform the corresponding steps in the methods provided by the embodiments of the present application. For details not disclosed in the embodiments of the apparatus of the present application, please refer to the embodiments of the monitoring and management method for the unmanned factory industrial internet platform described above.
FIG. 3 illustrates a block diagram of an unmanned factory industrial Internet platform monitoring management system according to an embodiment of the application.
Referring to fig. 3, an unmanned factory industrial internet platform monitoring management system 300 according to an embodiment of the present application includes:
an acquisition unit 310 for acquiring production data in an industrial production environment by means of a sensor device;
a screening unit 320, configured to screen the production data based on a preset data screening manner to obtain standby data;
an analysis unit 330, configured to perform data analysis on the spare data, and detect whether there is abnormal data that exceeds a normal range in the spare data;
a tracing unit 340, configured to trace a source of the abnormal data, and determine a target device associated with the abnormal data from the industrial device;
a maintenance unit 350, configured to acquire test data generated by detecting the target device, and maintain the target device based on the test data.
In some embodiments of the present application, based on the foregoing scheme, the screening unit 320 includes:
the period unit is used for determining a sampling period based on the target sampling number set in unit time;
and the sampling unit is used for sampling and screening the production data based on the sampling period to obtain standby data.
In some embodiments of the present application, based on the foregoing scheme, the screening unit 320 includes:
the parameter unit is used for generating image parameters corresponding to the image if the data type of the production data is the image;
the grade unit is used for determining the definition grade corresponding to the image based on the image parameter;
and the data unit is used for screening the images with the definition level higher than the set level as the standby data.
In some embodiments of the present application, based on the foregoing solution, the image parameters corresponding to the image include:
generating a gray level histogram corresponding to the image based on the pixel information of the image;
and determining a gray scale interval corresponding to the image and an interval pixel gray value corresponding to each gray scale interval as the image parameters based on the gray histogram and preset gray scale interval parameters.
In some embodiments of the present application, based on the foregoing solution, the determining, based on the image parameter, a corresponding sharpness level of the image includes:
determining the proportion of each gray scale interval in the total gray value of the image based on the interval pixel gray value corresponding to each gray scale interval;
determining a gray average value of the image based on the total gray value of the image and the gray level of the image;
calculating a gray variance based on the interval pixel gray value corresponding to each gray level interval, the gray mean value and the ratio;
and determining the definition level corresponding to the gray variance based on the set definition level range.
In some embodiments of the present application, based on the foregoing solution, the performing data analysis on the backup data to detect whether there is abnormal data beyond a normal range in the backup data includes:
acquiring a normal range corresponding to the standby data from a database based on the data identification of the standby data;
and detecting whether abnormal data exceeding the normal range exists in the standby data or not based on the numerical value of the standby data and the normal range.
In some embodiments of the present application, based on the foregoing solution, the tracing the abnormal data and determining a target device associated with the abnormal data from an industrial device includes:
and determining the target equipment corresponding to the abnormal data from an industrial equipment database in a character matching mode based on the data identification of the abnormal data.
In some embodiments of the present application, based on the foregoing solution, the method further includes:
when the number of the target devices obtained through tracing is larger than or equal to a set threshold value, determining the association degree between the functional parameters and the abnormal data based on the functional parameters of the target devices;
and determining the target equipment which is most related to the abnormal data through the relevance.
In some embodiments of the present application, based on the foregoing solution, the method further includes:
adding the target equipment into a fault equipment database, and recording the fault times of the target equipment;
and when the failure times of the equipment in the failure equipment database are greater than or equal to a failure threshold value, marking the target equipment as obsolete equipment so as to ensure the reliability of the production equipment participating in production.
In some embodiments of the present application, based on the foregoing solution, the method further includes:
in the industrial production process, recording the production duration of each industrial device;
and when the production time is greater than or equal to a time threshold, the industrial equipment is paused. The method and the device have the advantages that the related devices are guaranteed to be repaired and detected, and the running reliability of the devices is improved.
FIG. 4 illustrates a schematic structural diagram of a computer system suitable for use in implementing the electronic device of an embodiment of the present application.
It should be noted that the computer system 400 of the electronic device shown in fig. 4 is only an example, and should not bring any limitation to the functions and the scope of the application of the embodiments.
As shown in fig. 4, the computer system 400 includes a Central Processing Unit (CPU) 401, which can execute various appropriate actions and processes, such as executing the method described in the above embodiments, according to a program stored in a Read-Only Memory (ROM) 402 or a program loaded from a storage portion 408 into a Random Access Memory (RAM) 403. In the RAM 403, various programs and data necessary for system operation are also stored. The CPU 401, ROM 402, and RAM 403 are connected to each other via a bus 404. An Input/Output (I/O) interface 405 is also connected to the bus 404.
The following components are connected to the I/O interface 405: an input section 406 including a keyboard, a mouse, and the like; an output section 407 including a Display such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and a speaker; a storage section 408 including a hard disk and the like; and a communication section 409 including a Network interface card such as a LAN (Local Area Network) card, a modem, or the like. The communication section 409 performs communication processing via a network such as the internet. A drive 410 is also connected to the I/O interface 405 as needed. A removable medium 411 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 410 as necessary, so that a computer program read out therefrom is mounted into the storage section 408 as necessary.
In particular, according to embodiments of the present application, the processes described above with reference to the flow diagrams may be implemented as computer software programs. For example, embodiments of the present application include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising a computer program for performing the method illustrated by the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network through the communication section 409, and/or installed from the removable medium 411. The computer program executes various functions defined in the system of the present application when executed by a Central Processing Unit (CPU) 401.
It should be noted that the computer readable medium shown in the embodiments of the present application may be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a Read-Only Memory (ROM), an Erasable Programmable Read-Only Memory (EPROM), a flash Memory, an optical fiber, a portable Compact Disc Read-Only Memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this application, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In this application, however, a computer-readable signal medium may include a propagated data signal with a computer program embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. The computer program embodied on the computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wired, etc., or any suitable combination of the foregoing.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. Each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units described in the embodiments of the present application may be implemented by software or hardware, and the described units may also be disposed in a processor. Wherein the names of the elements do not in some way constitute a limitation on the elements themselves.
According to an aspect of the application, there is provided a computer program product or computer program comprising computer instructions stored in a computer readable storage medium. The processor of the computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions to cause the computer device to perform the method provided in the various alternative implementations described above.
As another aspect, the present application also provides a computer-readable medium, which may be contained in the electronic device described in the above embodiment; or may exist separately without being assembled into the electronic device. The computer readable medium carries one or more programs, which when executed by one of the electronic devices, cause the electronic device to implement the method described in the above embodiments.
It should be noted that although in the above detailed description several modules or units of the device for action execution are mentioned, such a division is not mandatory. Indeed, the features and functions of two or more modules or units described above may be embodied in one module or unit according to embodiments of the application. Conversely, the features and functions of one module or unit described above may be further divided into embodiments by a plurality of modules or units.
Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein may be implemented by software, and may also be implemented by software in combination with necessary hardware. Therefore, the technical solution according to the embodiments of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which can be a personal computer, a server, a touch terminal, or a network device, etc.) to execute the method according to the embodiments of the present application.
Other embodiments of the present application will be apparent to those skilled in the art from consideration of the specification and practice of the embodiments disclosed herein. This application is intended to cover any variations, uses, or adaptations of the invention following, in general, the principles of the application and including such departures from the present disclosure as come within known or customary practice within the art to which the invention pertains.
It will be understood that the present application is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the application is limited only by the appended claims.
Claims (10)
1. A monitoring and management method for an industrial Internet platform of an unmanned factory is characterized by comprising the following steps:
acquiring production data in an industrial production environment through a sensor device;
screening the production data based on a preset data screening mode to obtain standby data;
performing data analysis on the standby data, and detecting whether abnormal data beyond a normal range exists in the standby data;
tracing the abnormal data, and determining target equipment associated with the abnormal data from industrial equipment;
and obtaining test data generated by detecting the target equipment, and maintaining the target equipment based on the test data.
2. The method of claim 1, wherein screening the production data to obtain backup data based on a predetermined data screening manner comprises:
determining a sampling period based on a target sampling number set in unit time;
and based on the sampling period, sampling and screening the production data to obtain standby data.
3. The method of claim 1, wherein screening the production data to obtain backup data based on a predetermined data screening manner comprises:
if the data type of the production data is an image, generating image parameters corresponding to the image;
determining a corresponding definition grade of the image based on the image parameter;
and screening the images with the definition level higher than the set level as the standby data.
4. The method of claim 1, wherein performing data analysis on the backup data to detect whether abnormal data beyond a normal range exists in the backup data comprises:
acquiring a normal range corresponding to the standby data from a database based on the data identification of the standby data;
and detecting whether abnormal data beyond the normal range exists in the standby data or not based on the numerical value of the standby data and the normal range.
5. The method of claim 1, wherein tracing the anomaly data to identify a target device associated with the anomaly data from among the industrial devices comprises:
and determining the target equipment corresponding to the abnormal data from an industrial equipment database in a character matching mode based on the data identification of the abnormal data.
6. The utility model provides an unmanned factory industry internet platform monitoring management system which characterized in that includes:
an acquisition unit for acquiring production data in an industrial production environment by a sensor device;
the screening unit is used for screening the production data based on a preset data screening mode to obtain standby data;
the analysis unit is used for carrying out data analysis on the standby data and detecting whether abnormal data exceeding a normal range exists in the standby data;
the tracing unit is used for tracing the abnormal data and determining target equipment related to the abnormal data from industrial equipment;
and the maintenance unit is used for acquiring test data generated by detecting the target equipment and maintaining the target equipment based on the test data.
7. The system of claim 6, wherein the screening unit comprises:
the period unit is used for determining a sampling period based on the target sampling number set in unit time;
and the sampling unit is used for sampling and screening the production data based on the sampling period to obtain standby data.
8. The system of claim 6, wherein the screening unit comprises:
the parameter unit is used for generating image parameters corresponding to the images if the data type of the production data is the images;
the grade unit is used for determining the definition grade corresponding to the image based on the image parameter;
and the data unit is used for screening the images with the definition level higher than the set level as the standby data.
9. A computer-readable medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the unmanned factory industrial internet platform monitoring management method according to any one of claims 1 to 5.
10. An electronic device, comprising:
one or more processors;
a storage device to store one or more programs that, when executed by the one or more processors, cause the one or more processors to implement the unmanned plant industrial internet platform monitoring management method of any of claims 1-5.
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| CN117311295A (en) * | 2023-11-28 | 2023-12-29 | 深圳百通玄武技术有限公司 | Production quality improving method and system based on wireless network equipment |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117311295A (en) * | 2023-11-28 | 2023-12-29 | 深圳百通玄武技术有限公司 | Production quality improving method and system based on wireless network equipment |
| CN117311295B (en) * | 2023-11-28 | 2024-01-30 | 深圳百通玄武技术有限公司 | Production quality improving method and system based on wireless network equipment |
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