CN115062273B - Photoelectric sensor precision control method and system for industrial internet - Google Patents
Photoelectric sensor precision control method and system for industrial internet Download PDFInfo
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Abstract
The embodiment of the application provides a photoelectric sensor precision control method and system for industrial Internet, a computer readable medium and electronic equipment. The method for controlling the accuracy of the photoelectric sensor of the industrial Internet comprises the following steps: arranging a photoelectric sensor in an industrial production environment, and constructing an industrial internet based on the photoelectric sensor, gateway equipment and a processor; the method comprises the steps of acquiring measurement data acquired by a photoelectric sensor based on a preset data acquisition cycle, performing linear fitting on the measurement data based on a preset data model to obtain a fitting curve corresponding to the measurement data, comparing the fitting curve with a preset calibration curve to determine the difference between the two curves, and calibrating the precision of the photoelectric sensor based on the difference. According to the technical scheme of the embodiment of the application, the photoelectric sensor is calibrated through the working data based on the photoelectric sensor, and the working precision and the working efficiency of the photoelectric sensor are improved.
Description
Technical Field
The application relates to the technical field of computers, in particular to a photoelectric sensor precision control method and system for industrial internet, a computer readable medium and electronic equipment.
Background
In industrial production, a plurality of sensor devices are used for acquiring industrial data in the industrial production process, but the problems of inaccurate working data and insufficient working precision are caused because the industrial production environment is complex and the workload is large, and the faults of the sensors cannot be timely and effectively detected in the prior art, so that the working efficiency and the accuracy of the sensors are influenced.
Disclosure of Invention
The embodiment of the application provides a photoelectric sensor precision control method and system of an industrial internet, a computer readable medium and an electronic device, and further the working precision and efficiency of the photoelectric sensor 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 method for controlling accuracy of a photosensor of an industrial internet, including: the method comprises the following steps that a photoelectric sensor is arranged in an industrial production environment, and an industrial internet is constructed on the basis of the photoelectric sensor, gateway equipment and a processor; acquiring measurement data acquired by the photoelectric sensor based on a preset data acquisition period; performing linear fitting on the measurement data based on a preset data model to obtain a fitting curve corresponding to the measurement data; comparing the fitted curve with a preset calibration curve, and determining the difference between the two curves; calibrating the accuracy of the photosensor based on the degree of difference.
In some embodiments of the present application, based on the foregoing scheme, the performing linear fitting on the measurement data based on a preset data model to obtain a fitting curve corresponding to the measurement data includes: based on a set data range, identifying abnormal data in the measurement data, and deleting the abnormal data in the measurement data to obtain remaining standby data; performing linear fitting on the standby data based on a preset data model, and determining parameter values in the data model; determining the fitted curve based on the parameter values.
In some embodiments of the present application, based on the foregoing solution, the performing a linear fit on the standby data based on a preset data model to determine parameter values in the data model includes: and on the basis of a preset data model, performing linear fitting on the standby data through a least square method, and determining parameter values in the data model.
In some embodiments of the present application, based on the foregoing scheme, after the performing linear fitting on the measurement data based on a preset data model to obtain a fitting curve corresponding to the measurement data, the method further includes: calculating a degree of difference between the fitted curve and the measured data based on the fitted curve and the measured data; and if the difference degree is greater than or equal to the set threshold value, fitting the measured data again.
In some embodiments of the present application, based on the foregoing scheme, after the performing linear fitting on the measurement data based on a preset data model to obtain a fitting curve corresponding to the measurement data, the method further includes: calculating a degree of difference between the fitted curve and the measured data based on the fitted curve and the measured data; and if the difference degree is smaller than a set threshold value, judging the fitted curve to be a correct curve.
In some embodiments of the present application, based on the foregoing scheme, the comparing the fitted curve with a preset calibration curve to determine a difference between the two curves includes: calculating a curve difference between the fitted curve and a preset calibration curve; based on the curve difference, a degree of difference between the fitted curve and the calibration curve is determined.
According to an aspect of an embodiment of the present application, there is provided an industrial internet photosensor accuracy control system including:
the system comprises a construction unit, a gateway device and a processor, wherein the construction unit is used for laying a photoelectric sensor in an industrial production environment and constructing an industrial internet based on the photoelectric sensor, the gateway device and the processor;
the acquisition unit is used for acquiring the measurement data acquired by the photoelectric sensor based on a preset data acquisition period;
the fitting unit is used for performing linear fitting on the measured data based on a preset data model to obtain a fitting curve corresponding to the measured data;
the comparison unit is used for comparing the fitted curve with a preset calibration curve and determining the difference between the two curves;
and the calibration unit is used for calibrating the precision of the photoelectric sensor based on the difference degree.
In some embodiments of the present application, based on the foregoing scheme, the fitting unit includes:
the identification unit is used for identifying abnormal data in the measurement data based on a set data range, deleting the abnormal data in the measurement data and obtaining the residual standby data;
the parameter fitting unit is used for performing linear fitting on the standby data based on a preset data model and determining parameter values in the data model;
a curve unit for determining the fitted curve based on the parameter values.
In some embodiments of the present application, based on the foregoing solution, the performing linear fitting on the backup data based on a preset data model to determine a parameter value in the data model includes: and on the basis of a preset data model, performing linear fitting on the standby data through a least square method, and determining parameter values in the data model.
In some embodiments of the present application, based on the foregoing scheme, after the performing linear fitting on the measurement data based on a preset data model to obtain a fitting curve corresponding to the measurement data, the method further includes: calculating a degree of difference between the fitted curve and the measurement data based on the fitted curve and the measurement data; and if the difference degree is greater than or equal to the set threshold value, fitting the measured data again.
In some embodiments of the present application, based on the foregoing scheme, after the performing linear fitting on the measurement data based on a preset data model to obtain a fitting curve corresponding to the measurement data, the method further includes: calculating a degree of difference between the fitted curve and the measured data based on the fitted curve and the measured data; and if the difference degree is smaller than a set threshold value, judging the fitted curve to be a correct curve.
In some embodiments of the present application, based on the foregoing scheme, the comparing the fitted curve with a preset calibration curve to determine a difference between the two curves includes: calculating a curve difference between the fitted curve and a preset calibration curve; based on the curve difference, a degree of difference between the fitted curve and the calibration curve is determined.
According to an aspect of the embodiments 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, implementing the method for controlling the accuracy of the industrial internet photoelectric sensor as described in the above embodiments.
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 method for controlling the accuracy of the industrial internet photoelectric sensor 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 industrial internet photosensor precision control method provided in the above-mentioned various optional implementation modes.
In the technical scheme provided by some embodiments of the application, a photoelectric sensor is arranged in an industrial production environment, and an industrial internet is constructed on the basis of the photoelectric sensor, a gateway device and a processor; the method comprises the steps of acquiring measurement data acquired by a photoelectric sensor based on a preset data acquisition cycle, performing linear fitting on the measurement data based on a preset data model to obtain a fitting curve corresponding to the measurement data, then comparing the fitting curve with a preset calibration curve, determining the difference between the two curves, and calibrating the precision of the photoelectric sensor based on the difference. According to the technical scheme, the photoelectric sensor is calibrated through the working data based on the photoelectric sensor, and the working precision and the working efficiency of the photoelectric sensor 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 shows a flowchart of a photosensor accuracy control method of the industrial internet according to an embodiment of the present application.
Fig. 2 schematically shows a flow chart of generating a fitted curve according to an embodiment of the present application.
Fig. 3 schematically shows a schematic diagram of an industrial internet photosensor accuracy control system according to one 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 a photosensor accuracy control method of an industrial internet, which may be performed by a server, according to one embodiment of the present application. Referring to fig. 1, the method for controlling the accuracy of the industrial internet photoelectric sensor at least comprises steps S110 to S150, which are described in detail as follows:
in step S110, a photosensor is deployed in an industrial production environment, and an industrial internet is constructed based on the photosensor, a gateway device, and a processor.
In one embodiment of the present application, a photosensor is deployed in an industrial production environment for collecting and measuring data. Meanwhile, an industrial internet is constructed on the basis of the photoelectric sensor, the gateway equipment and the processor. So as to realize real-time monitoring of industrial production through the industrial internet.
In an embodiment of the present application, the industrial internet of things in the present solution may include: the gateway device comprises a data sensing layer formed by photoelectric sensors, a network transmission layer communicated by the gateway device and a data processing layer formed by a processor.
In step S120, measurement data acquired by the photoelectric sensor is acquired based on a preset data acquisition period.
In one embodiment of the present application, the measurement data collected by the photosensor is acquired based on a preset data acquisition period. The data acquisition cycle in the present embodiment may be one day, one month, or the like. The working condition of the photoelectric sensor can be well controlled by collecting the measurement data of the photoelectric sensor.
In step S130, the measurement data is linearly fitted based on a preset data model, so as to obtain a fitting curve corresponding to the measurement data.
In an embodiment of the present application, each type or model of the photosensor has its corresponding data model, for example, the data model corresponding to the data output condition of a model of the photosensor is determined in advance, so as to measure the output parameter of the photosensor based on the data model and determine its corresponding data model parameter. In this embodiment, the measured data is linearly fitted based on a preset data model, so as to obtain a fitting curve corresponding to the measured data.
Optionally, we may determine the parameter values in the data model by performing linear fitting on the backup data through a least square method to obtain a fitted curve corresponding to the measured data.
In an embodiment of the present application, performing linear fitting on the measurement data based on a preset data model to obtain a fitting curve corresponding to the measurement data includes:
based on a set data range, identifying abnormal data in the measurement data, and deleting the abnormal data in the measurement data to obtain remaining standby data;
performing linear fitting on the standby data based on a preset data model, and determining parameter values in the data model;
determining the fitted curve based on the parameter values.
Specifically, in this embodiment, abnormal data in the measurement data is determined based on a preset data range, where the abnormal data is data that does not belong to the data range, and the abnormal data is deleted to obtain remaining spare data. The error rate and noise of the data are reduced by deleting abnormal data, and the accuracy and the correctness of curve fitting are improved. And then, performing linear fitting on the standby data based on a preset data model, determining parameter values in the data model, and determining the fitting curve based on the parameter values.
In an embodiment of the present application, after performing linear fitting on the measurement data based on a preset data model to obtain a fitting curve corresponding to the measurement data, the method further includes:
calculating a degree of difference between the fitted curve and the measurement data based on the fitted curve and the measurement data;
and if the difference degree is greater than or equal to the set threshold value, fitting the measured data again.
Specifically, based on the independent variable i, a fitting numerical value Val _ fit _ i corresponding to the independent variable is obtained from the fitting curve, meanwhile, an actual numerical value Val _ ace _ i corresponding to the independent variable is obtained from the measured data, a difference value between the fitting numerical value Val _ fit _ i and the actual numerical value Val _ ace _ i is calculated, and then, based on the difference value Val _ ace _ i-Val _ fit _ i between the numerical values corresponding to the k discrete independent variables, a degree of difference Dre _ di between the fitting curve and the measured data is calculated as:
where i denotes the number of the argument and β denotes the data factor. In the above manner, the difference between the discrete values is calculated to obtain the difference between the fitting curve and the measured data, which is used to measure the consistency between the fitting curve and the measured data, if the difference between the fitting curve and the measured data is higher and the difference is greater than or equal to the set threshold, the fitting curve cannot represent the measured data closely, and if the difference between the fitting curve and the measured data is too large, the measured data needs to be fitted again.
After linear fitting is carried out on the measured data based on a preset data model and a fitting curve corresponding to the measured data is obtained, the method further comprises the following steps:
calculating a degree of difference between the fitted curve and the measurement data based on the fitted curve and the measurement data;
and if the difference degree is smaller than a set threshold value, judging the fitted curve to be a correct curve.
Specifically, after the difference between the fitted curve and the measured data is obtained through calculation, if the difference is smaller than a set threshold, the fitted curve is determined to be a correct curve. Through the mode, the access between the fitting curve and the measured data is detected, and the access between the fitting curve and the measured data is ensured to be in a small state.
In step S140, the fitted curve is compared with a preset calibration curve, and a difference between the two curves is determined.
In one embodiment of the present application, after the fitting curve is generated, the fitting curve is compared with a preset calibration curve to determine a difference therebetween, and the accuracy of the actual measurement data is measured according to the difference.
In an embodiment of the present application, comparing the fitted curve with a preset calibration curve, and determining a difference between the two curves includes:
calculating a curve difference between the fitted curve and a preset calibration curve;
based on the curve difference, a degree of difference between the fitted curve and the calibration curve is determined.
Specifically, the fitted curve is f (x) and the preset calibration curve is g (x), and the curve difference between the fitted curve and the preset calibration curve is calculated to be f (x) -g (x). Then based on the curve difference, determining a degree of difference Deg _ fg between the fitted curve and the calibration curve as:
wherein x represents an independent variable, t represents the length or maximum value of the independent variable, and α represents a preset difference parameter. In the mode, the difference between the fitting curve and the calibration curve is obtained by calculating the curve difference between the fitting curve and the calibration curve, so that the deviation condition of the fitting curve to the calibration curve is measured through the difference, and the deviation condition between the measurement effect and the expected effect of the photoelectric sensor is reflected.
In step S150, the accuracy of the photosensor is calibrated based on the degree of difference.
In one embodiment of the present application, after determining the degree of difference between the fitted curve and the calibration curve, the accuracy of the photosensor is calibrated based on the degree of difference.
For example, when the difference degree is greater than or equal to the difference degree threshold value, calibration is performed. And when the difference degree is smaller than the difference degree threshold value, the default photoelectric sensor works normally and is not calibrated.
In the technical scheme provided by some embodiments of the application, a photoelectric sensor is arranged in an industrial production environment, and an industrial internet is constructed on the basis of the photoelectric sensor, a gateway device and a processor; the method comprises the steps of acquiring measurement data acquired by a photoelectric sensor based on a preset data acquisition cycle, performing linear fitting on the measurement data based on a preset data model to obtain a fitting curve corresponding to the measurement data, comparing the fitting curve with a preset calibration curve to determine the difference between the two curves, and calibrating the precision of the photoelectric sensor based on the difference. According to the technical scheme, the photoelectric sensor is calibrated through the working data based on the photoelectric sensor, and the working precision and the working efficiency of the photoelectric sensor are improved.
The following describes embodiments of the apparatus of the present application, which can be used to implement the method for controlling the accuracy of the photoelectric sensor of the industrial internet 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 configured to perform corresponding steps in the methods provided in the embodiments of the present application. For details that are not disclosed in the embodiments of the apparatus of the present application, please refer to the embodiments of the method for controlling the accuracy of the photoelectric sensor of the industrial internet described above in the present application.
Fig. 3 shows a block diagram of an industrial internet photosensor accuracy control system according to one embodiment of the present application.
Referring to fig. 3, a photosensor accuracy control system 300 of the industrial internet according to an embodiment of the present application includes:
the building unit 310 is used for laying the photoelectric sensor in an industrial production environment and building an industrial internet based on the photoelectric sensor, the gateway equipment and the processor;
an obtaining unit 320, configured to obtain measurement data acquired by the photoelectric sensor based on a preset data obtaining period;
the fitting unit 330 is configured to perform linear fitting on the measurement data based on a preset data model to obtain a fitting curve corresponding to the measurement data;
a comparison unit 340, configured to compare the fitted curve with a preset calibration curve, and determine a difference between the two curves;
a calibration unit 350 for calibrating the accuracy of the photosensor based on the difference.
In some embodiments of the present application, based on the foregoing scheme, the fitting unit 330 includes:
the identification unit is used for identifying abnormal data in the measurement data based on a set data range, deleting the abnormal data in the measurement data and obtaining the residual standby data;
the parameter fitting unit is used for performing linear fitting on the standby data based on a preset data model and determining parameter values in the data model;
a curve unit for determining the fitted curve based on the parameter values.
In some embodiments of the present application, based on the foregoing solution, the performing linear fitting on the backup data based on a preset data model to determine a parameter value in the data model includes: and on the basis of a preset data model, performing linear fitting on the standby data through a least square method, and determining parameter values in the data model.
In some embodiments of the present application, based on the foregoing scheme, after the performing linear fitting on the measurement data based on a preset data model to obtain a fitting curve corresponding to the measurement data, the method further includes: calculating a degree of difference between the fitted curve and the measurement data based on the fitted curve and the measurement data; and if the difference degree is greater than or equal to the set threshold value, fitting the measured data again.
In some embodiments of the present application, based on the foregoing scheme, after the performing linear fitting on the measurement data based on a preset data model to obtain a fitting curve corresponding to the measurement data, the method further includes: calculating a degree of difference between the fitted curve and the measurement data based on the fitted curve and the measurement data; and if the difference degree is smaller than a set threshold value, judging the fitted curve to be a correct curve.
In some embodiments of the present application, based on the foregoing scheme, the comparing the fitted curve with a preset calibration curve to determine a difference between the two curves includes: calculating a curve difference between the fitted curve and a preset calibration curve; based on the curve difference, a degree of difference between the fitted curve and the calibration curve is determined.
In the technical scheme provided by some embodiments of the application, a photoelectric sensor is arranged in an industrial production environment, and an industrial internet is constructed on the basis of the photoelectric sensor, a gateway device and a processor; the method comprises the steps of acquiring measurement data acquired by a photoelectric sensor based on a preset data acquisition cycle, performing linear fitting on the measurement data based on a preset data model to obtain a fitting curve corresponding to the measurement data, then comparing the fitting curve with a preset calibration curve, determining the difference between the two curves, and calibrating the precision of the photoelectric sensor based on the difference. According to the technical scheme of the embodiment of the application, the photoelectric sensor is calibrated through the working data based on the photoelectric sensor, and the working precision and the working efficiency of the photoelectric sensor are 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 driver 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 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. When the computer program is executed by a Central Processing Unit (CPU) 401, various functions defined in the system of the present application are executed.
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 also 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 which 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 may be implemented by 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, a computer program product or computer program is provided, comprising computer instructions, the computer instructions being 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 embodiments; or may exist separately without being assembled into the electronic device. The computer readable medium carries one or more programs which, when executed by an electronic device, 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 functionality 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, or 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 (9)
1. A photoelectric sensor precision control method of industrial Internet is characterized by comprising the following steps:
the method comprises the following steps that a photoelectric sensor is arranged in an industrial production environment, and an industrial internet is constructed on the basis of the photoelectric sensor, gateway equipment and a processor;
acquiring measurement data acquired by the photoelectric sensor based on a preset data acquisition period;
fitting the measurement data based on a preset data model to obtain a fitting curve corresponding to the measurement data;
comparing the fitted curve with a preset calibration curve, and determining the difference between the two curves;
calibrating the accuracy of the photosensor based on the degree of difference;
fitting the measured data based on a preset data model to obtain a fitting curve corresponding to the measured data, and further comprising:
acquiring a fitting numerical value Val _ fit _ i corresponding to an independent variable from the fitting curve based on the independent variable i, acquiring an actual numerical value Val _ ace _ i corresponding to the independent variable from the measurement data, and calculating a difference Dre _ di between the fitting curve and the measurement data based on a difference value between numerical values corresponding to k independent variables:
wherein i represents the number of the independent variable, β represents the data factor, and k represents the number of the independent variable;
and if the difference degree is greater than or equal to the set threshold value, fitting the measured data again.
2. The method of claim 1, wherein fitting the measurement data based on a preset data model to obtain a corresponding fitting curve of the measurement data comprises:
based on a set data range, identifying abnormal data in the measured data, and deleting the abnormal data in the measured data to obtain residual standby data;
fitting the standby data based on a preset data model to determine parameter values in the data model;
determining the fitted curve based on the parameter values.
3. The method of claim 2, wherein fitting the backup data based on a predetermined data model to determine parameter values in the data model comprises:
and fitting the standby data by a least square method based on a preset data model to determine parameter values in the data model.
4. The method according to claim 1, wherein after fitting the measurement data based on a preset data model to obtain a fitting curve corresponding to the measurement data, the method further comprises:
calculating a degree of difference between the fitted curve and the measurement data based on the fitted curve and the measurement data;
and if the difference degree is smaller than a set threshold value, judging the fitted curve to be a correct curve.
5. The method of claim 1, wherein comparing the fitted curve with a preset calibration curve to determine a degree of difference between the two curves comprises:
calculating a curve difference between the fitted curve and a preset calibration curve;
based on the curve difference, a degree of difference between the fitted curve and the calibration curve is determined.
6. The utility model provides an industrial internet's photoelectric sensor accuracy control system which characterized in that includes:
the system comprises a construction unit, a gateway device and a processor, wherein the construction unit is used for laying a photoelectric sensor in an industrial production environment and constructing an industrial internet based on the photoelectric sensor, the gateway device and the processor;
the acquisition unit is used for acquiring the measurement data acquired by the photoelectric sensor based on a preset data acquisition period;
the fitting unit is used for fitting the measured data based on a preset data model to obtain a fitting curve corresponding to the measured data;
the comparison unit is used for comparing the fitted curve with a preset calibration curve and determining the difference between the two curves;
a calibration unit for calibrating the accuracy of the photosensor based on the degree of difference;
fitting the measured data based on a preset data model to obtain a fitting curve corresponding to the measured data, and further comprising:
acquiring a fitting numerical value Val _ fit _ i corresponding to an independent variable from the fitting curve based on the independent variable i, acquiring an actual numerical value Val _ ace _ i corresponding to the independent variable from the measurement data, and calculating a difference Dre _ di between the fitting curve and the measurement data based on a difference value between numerical values corresponding to k independent variables:
wherein i represents the number of the independent variable, β represents the data factor, and k represents the number of the independent variable;
and if the difference degree is greater than or equal to the set threshold value, fitting the measured data again.
7. The system of claim 6, wherein the fitting unit comprises:
the identification unit is used for identifying abnormal data in the measurement data based on a set data range, deleting the abnormal data in the measurement data and obtaining the residual standby data;
the parameter fitting unit is used for fitting the standby data based on a preset data model and determining parameter values in the data model;
a curve unit for determining the fitted curve based on the parameter values.
8. A computer-readable medium on which a computer program is stored, wherein the computer program, when executed by a processor, implements the industrial internet photosensor accuracy control method according to any one of claims 1 to 5.
9. An electronic device, comprising:
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 method of controlling accuracy of the photoelectric sensor of the industrial internet according to any one of claims 1 to 5.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106802183A (en) * | 2016-11-30 | 2017-06-06 | 努比亚技术有限公司 | A kind of photosensitive sensors calibration method and terminal |
| CN109059181A (en) * | 2018-07-31 | 2018-12-21 | 广东美的制冷设备有限公司 | Photoelectric sensor calibration method, device, air conditioner and readable storage medium storing program for executing |
| CN111134573A (en) * | 2018-11-02 | 2020-05-12 | 添可智能科技有限公司 | Method for improving precision of dust detection sensor |
| CN113533641A (en) * | 2021-06-04 | 2021-10-22 | 浙江力夫传感技术有限公司 | Calibration and calibration method and system of gas sensor |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20120078532A1 (en) * | 2010-09-24 | 2012-03-29 | David Edward Forsyth | Non-dispersive infrared sensor measurement system and method |
| US10519770B2 (en) * | 2016-03-22 | 2019-12-31 | Halliburton Energy Services, Inc. | Calibration module for pooled optical sensors in downhole fluid analysis |
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Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106802183A (en) * | 2016-11-30 | 2017-06-06 | 努比亚技术有限公司 | A kind of photosensitive sensors calibration method and terminal |
| CN109059181A (en) * | 2018-07-31 | 2018-12-21 | 广东美的制冷设备有限公司 | Photoelectric sensor calibration method, device, air conditioner and readable storage medium storing program for executing |
| CN111134573A (en) * | 2018-11-02 | 2020-05-12 | 添可智能科技有限公司 | Method for improving precision of dust detection sensor |
| CN113533641A (en) * | 2021-06-04 | 2021-10-22 | 浙江力夫传感技术有限公司 | Calibration and calibration method and system of gas sensor |
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