CN116488578A - Photovoltaic equipment analysis method based on AI visual assistance - Google Patents
Photovoltaic equipment analysis method based on AI visual assistance Download PDFInfo
- Publication number
- CN116488578A CN116488578A CN202310652536.0A CN202310652536A CN116488578A CN 116488578 A CN116488578 A CN 116488578A CN 202310652536 A CN202310652536 A CN 202310652536A CN 116488578 A CN116488578 A CN 116488578A
- Authority
- CN
- China
- Prior art keywords
- real
- module
- curve
- image
- function management
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000004458 analytical method Methods 0.000 title claims abstract description 54
- 230000000007 visual effect Effects 0.000 title abstract description 8
- 238000011156 evaluation Methods 0.000 claims abstract description 46
- 238000007726 management method Methods 0.000 claims description 71
- 238000004891 communication Methods 0.000 claims description 22
- 238000000034 method Methods 0.000 claims description 22
- 239000000428 dust Substances 0.000 claims description 14
- 238000013500 data storage Methods 0.000 claims description 12
- 238000005070 sampling Methods 0.000 claims description 12
- 230000005611 electricity Effects 0.000 claims 2
- 238000001514 detection method Methods 0.000 abstract description 6
- 238000012544 monitoring process Methods 0.000 abstract description 6
- 230000005856 abnormality Effects 0.000 abstract description 3
- 230000002159 abnormal effect Effects 0.000 description 8
- 238000010586 diagram Methods 0.000 description 8
- 238000010248 power generation Methods 0.000 description 4
- 238000004140 cleaning Methods 0.000 description 1
- 230000002349 favourable effect Effects 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000000737 periodic effect Effects 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Classifications
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02S—GENERATION OF ELECTRIC POWER BY CONVERSION OF INFRARED RADIATION, VISIBLE LIGHT OR ULTRAVIOLET LIGHT, e.g. USING PHOTOVOLTAIC [PV] MODULES
- H02S50/00—Monitoring or testing of PV systems, e.g. load balancing or fault identification
- H02S50/10—Testing of PV devices, e.g. of PV modules or single PV cells
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02S—GENERATION OF ELECTRIC POWER BY CONVERSION OF INFRARED RADIATION, VISIBLE LIGHT OR ULTRAVIOLET LIGHT, e.g. USING PHOTOVOLTAIC [PV] MODULES
- H02S40/00—Components or accessories in combination with PV modules, not provided for in groups H02S10/00 - H02S30/00
- H02S40/30—Electrical components
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E10/00—Energy generation through renewable energy sources
- Y02E10/50—Photovoltaic [PV] energy
Landscapes
- Photovoltaic Devices (AREA)
Abstract
The invention discloses a photovoltaic equipment analysis method based on AI visual assistance, which is applied to an analysis system, wherein the photovoltaic equipment comprises a photovoltaic module, and the analysis system comprises an output parameter acquisition module, a curve generation module, a curve comparison module, an analysis result output module and a function management module. The function management module controls the curve generating module to generate a real-time output parameter curve according to the real-time output parameter, the function management module controls the curve comparing module to determine a real-time output parameter curve evaluation index according to the real-time output parameter curve and a predetermined priori output parameter curve, and determines that the photovoltaic module runs normally or abnormally according to the real-time output parameter curve evaluation index, so that continuous monitoring of working parameters of the photovoltaic module, namely a real-time output voltage value and a real-time output current value is realized, and the risk of missed detection of the condition of the instantaneously occurring working parameter abnormality is reduced.
Description
Technical Field
The invention relates to the technical field of photovoltaic equipment, in particular to a photovoltaic equipment analysis method based on AI visual assistance.
Background
Patent CN108696249B discloses a method for rapidly diagnosing faults of a photovoltaic module, which comprises the following steps: collecting one or more different types of working parameters of each photovoltaic module in the battery pack string at least within a preset time period; comparing the set of the specified type of working parameters of each photovoltaic module in the battery string within a preset time period with the specified type of working parameters of each photovoltaic module in the battery string within the preset time period; and judging whether each photovoltaic module in the battery pack string generates abnormal power or not at least based on the comparison result.
However, the above-mentioned technical solution performs periodic monitoring on the working parameters of the photovoltaic module, which is essentially to perform discontinuous abnormal condition monitoring on the operation state of the photovoltaic module, and there is a large risk of missed detection for the case of the instantaneously occurring abnormal working parameters.
Therefore, how to continuously monitor the working parameters of the photovoltaic module, so as to reduce the risk of missed detection of the condition of instantaneous abnormal working parameters is a technical problem to be solved.
Disclosure of Invention
The technical problem to be solved by the invention is to provide the photovoltaic equipment analysis method based on the AI vision assistance, which is favorable for continuously monitoring the working parameters of the photovoltaic module so as to reduce the risk of missed detection on the condition of abnormal working parameters which occur instantaneously.
In order to solve the technical problems, the invention discloses a photovoltaic device analysis method based on AI vision assistance, which is applied to an analysis system, wherein the photovoltaic device comprises a photovoltaic module, the analysis system comprises an output parameter acquisition module, a curve generation module, a curve comparison module, an analysis result output module and a function management module, the output parameter acquisition module, the curve generation module, the analysis result output module and the curve comparison module are respectively and electrically connected with the function management module, and the output parameter acquisition module is also electrically connected with the output end of the photovoltaic module, and the method comprises the following steps:
the function management module acquires real-time output parameters of the photovoltaic module from the output parameter acquisition module, wherein the real-time output parameters comprise real-time output current values and real-time output voltage values of the photovoltaic module;
the function management module controls the curve generation module to generate a real-time output parameter curve according to the real-time output parameter, wherein the real-time output parameter curve is a curve representing the change of the real-time output current value along with the change of the real-time output voltage value;
the function management module controls the curve comparison module to determine a real-time output parameter curve evaluation index according to the real-time output parameter curve and a predetermined prior output parameter curve, wherein the predetermined prior output parameter curve is a curve which represents that the prior output current value of a photovoltaic module running in a normal state changes along with the change of an output voltage value;
the function management module controls the curve comparison module to judge whether the real-time output parameter curve evaluation index is larger than or equal to a predetermined curve evaluation index threshold;
if yes, the function management module controls the analysis result output module to output first result information representing that the photovoltaic module operates normally, and if not, the function management module controls the analysis result output module to output second result information representing that the photovoltaic module operates abnormally.
Therefore, in the photovoltaic equipment analysis method based on the AI vision assistance, the function management module control curve generating module generates the real-time output parameter curve according to the real-time output parameter, the function management module control curve comparing module determines the real-time output parameter curve evaluation index according to the real-time output parameter curve and the predetermined prior output parameter curve, and determines the normal operation or abnormal operation of the photovoltaic module according to the real-time output parameter curve evaluation index, so that the continuous monitoring of the working parameters of the photovoltaic module, namely the real-time output voltage value and the real-time output current value is realized, and the risk of missed detection on the condition of the instantaneously occurring working parameter abnormality is reduced.
As an optional implementation manner, in the present invention, the analysis system further includes an image comparison module and a camera module that are electrically connected to the function management module, and the method further includes:
the function management module controls the camera module to shoot a real-time image of the photovoltaic module;
the function management module controls the image comparison module to determine a real-time image evaluation index according to the real-time image and a predetermined prior image, wherein the prior image is an image with dust covered on the surface of the photovoltaic module;
the function management module controls the image comparison module to judge whether the real-time image evaluation index is larger than or equal to a predetermined image evaluation index threshold;
if yes, the function management module controls the analysis result output module to output third result information indicating that the surface of the photovoltaic module is covered with dust, and if not, the function management module controls the analysis result output module to output fourth result information indicating that the surface of the photovoltaic module is not covered with dust.
In an alternative embodiment, the function management module controls the curve comparison module to determine the real-time output parameter curve evaluation index according to the real-time output parameter curve and the predetermined prior output parameter curve, and the adopted formula is as follows:
wherein P represents the real-time output parametric curve evaluation index, t represents the number of samples input with the output voltage and output with the real-time output current value or the prior output current value, k represents the sequence number of the kth sample in the t samples, y k Representing the real-time output current value, y, corresponding to the kth sampling output voltage k ' represents the a priori output current value corresponding to the kth sampled output voltage,is y k Mean value of->Is y k 'mean value'.
In an alternative embodiment, the function management module controls the image comparison module to determine the real-time image evaluation index according to the real-time image and the predetermined prior image, and the formula is as follows:
wherein T represents a real-time image evaluation index, m represents the number of lines of image units obtained by gridding a real-time image or a priori image, n represents the number of columns of image units obtained by gridding the real-time image or the priori image, and I i，j Representing the characteristic value of the image unit positioned in the ith row and the jth column in the real-time image, I' i，j Representing the eigenvalues of the image elements located in the ith row and jth column of the prior image,representation I i，j Mean value of->Is represented by I' i，j Is a mean value of (c).
In an alternative embodiment, the characteristic value is a brightness value, a contrast value or a gray value.
As an alternative embodiment, in the present invention, the analysis system further includes a communication module electrically connected to the function management module, and before the function management module controls the curve comparison module to determine the real-time output parameter curve evaluation index according to the real-time output parameter curve and the predetermined prior output parameter curve, the method further includes:
the function management module controls the communication module to establish communication connection with an external data storage platform;
the function management module controls the communication module to acquire data about a priori output parameter curve from the data storage platform;
and before the function management module controls the image comparison module to determine a real-time image evaluation index based on the real-time image and a predetermined prior image, the method further comprises:
the function management module controls the communication module to establish communication connection with an external data storage platform;
the function management module controls the communication module to acquire data about a priori images from the data storage platform.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic diagram of an analysis system according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart of a photovoltaic device analysis method based on AI visual assistance according to an embodiment of the invention;
FIG. 3 is a schematic diagram of the structure of an independent photovoltaic power generation system according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of a grid-connected photovoltaic power generation system according to an embodiment of the present invention;
FIG. 5 is a schematic diagram of a real-time output parametric curve for an embodiment of the present invention;
FIG. 6 is a schematic diagram of a priori output parametric curve of an embodiment of the present invention;
FIG. 7 is a schematic flow chart of another photovoltaic device analysis method based on AI visual aids according to an embodiment of the invention;
fig. 8 is a schematic diagram of a real-time output parametric curve and a priori output parametric curve in the same coordinate system according to an embodiment of the present invention.
Detailed Description
In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The terms first, second and the like in the description and in the claims, are used for distinguishing between different objects and not for describing a particular sequential order. Furthermore, the terms "comprise" and "have," as well as any variations thereof, are intended to cover a non-exclusive inclusion. For example, a process, method, apparatus, article, or device that comprises a list of steps or elements is not limited to the list of steps or elements but may, in the alternative, include other steps or elements not expressly listed or inherent to such process, method, article, or device.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the invention. The appearances of such phrases in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those of skill in the art will explicitly and implicitly appreciate that the embodiments described herein may be combined with other embodiments.
The invention discloses a photovoltaic equipment analysis method based on AI visual assistance, which is applied to an analysis system, wherein photovoltaic equipment comprises a photovoltaic module. As shown in fig. 1, the analysis system includes an output parameter acquisition module, a curve generation module, a curve comparison module, an analysis result output module and a function management module, and the output parameter acquisition module, the curve generation module, the analysis result output module and the curve comparison module are respectively and electrically connected with the function management module. The output parameter acquisition module is also electrically connected with the output end of the photovoltaic module.
Wherein, as shown in fig. 2, the method comprises the following steps:
s101, the function management module acquires real-time output parameters of the photovoltaic module from the output parameter acquisition module. The real-time output parameters comprise a real-time output current value and a real-time output voltage value of the photovoltaic module. The schematic structural diagram of the independent photovoltaic power generation system may be shown in fig. 3, and the schematic structural diagram of the grid-connected photovoltaic power generation system may be shown in fig. 4. It is understood that in step S101, the real-time output current value of the photovoltaic module (or the photovoltaic array) may be determined by detecting the real-time current value of the connection end (i.e. the point a in fig. 3 or the point B in fig. 4) of the controller and the photovoltaic module, and the real-time output voltage value of the photovoltaic module may be determined by detecting the real-time voltage value of the connection end (i.e. the point a in fig. 3 or the point B in fig. 4) of the controller and the photovoltaic module.
S102, the function management module controls the curve generating module to generate a real-time output parameter curve according to the real-time output parameter. The real-time output parameter curve is a curve showing the change of the real-time output current value along with the change of the real-time output voltage value. Specifically, as shown in fig. 5, the real-time output parameter curve may be a curve in a coordinate system constructed with a real-time output voltage value as an abscissa and a real-time output current value as an ordinate.
And S103, the function management module controls the curve comparison module to determine the real-time output parameter curve evaluation index according to the real-time output parameter curve and the predetermined prior output parameter curve. The predetermined prior output parameter curve is a curve which represents that the prior output current value of the photovoltaic module running in a normal state changes along with the change of the output voltage value. Specifically, as shown in fig. 6, the a priori output parameter curve may be a curve in a coordinate system constructed with a priori output voltage value as an abscissa and a priori output current value as an ordinate.
S104, the function management module controls the curve comparison module to judge whether the real-time output parameter curve evaluation index is larger than or equal to a predetermined curve evaluation index threshold, if yes, the step S105a is executed, and if not, the step S105b is executed.
S105a, the function management module controls the analysis result output module to output first result information which indicates that the photovoltaic module operates normally.
S105b, the function management module controls the analysis result output module to output second result information representing abnormal operation of the photovoltaic module.
Therefore, in the photovoltaic equipment analysis method based on the AI vision assistance, the function management module control curve generating module generates the real-time output parameter curve according to the real-time output parameter, the function management module control curve comparing module determines the real-time output parameter curve evaluation index according to the real-time output parameter curve and the predetermined prior output parameter curve, and determines the normal operation or abnormal operation of the photovoltaic module according to the real-time output parameter curve evaluation index, so that the continuous monitoring of the working parameters of the photovoltaic module, namely the real-time output voltage value and the real-time output current value is realized, and the risk of missed detection on the condition of the instantaneously occurring working parameter abnormality is reduced.
It is well known to those skilled in the art that under the same conditions, the output power of a photovoltaic module with dust covered surface is smaller than that of a photovoltaic module without dust covered surface. Then, the dust covered on the surface of the photovoltaic module can be found in time, and convenience can be brought to a manager for cleaning the dust in time. The AI vision assistance technique application facilitates enriching the functionality of the analysis system, alternatively, the technique can be used to make a determination as to whether the surface of the photovoltaic module is covered with dust. Specifically, the analysis system further comprises an image comparison module and a camera module which are respectively and electrically connected with the function management module. And, as shown in fig. 7, the method further comprises the following operations:
s201, the function management module controls the camera module to shoot real-time images of the photovoltaic module.
S202, the function management module controls the image comparison module to determine the real-time image evaluation index according to the real-time image and the predetermined prior image. The prior image is an image of the surface of the photovoltaic module covered with dust.
S203, the function management module controls the image comparison module to judge whether the real-time image evaluation index is larger than or equal to a predetermined image evaluation index threshold, if so, the step S204a is executed, and if not, the step S204b is executed.
S204a, the function management module controls the analysis result output module to output third result information which indicates that the surface of the photovoltaic module is covered with dust.
S204b, the function management module controls the analysis result output module to output fourth result information which indicates that the surface of the photovoltaic module is not covered with dust.
In order to increase the efficiency of determining the evaluation index of the real-time output parameter curve, the points on the implemented output parameter curve and the points on the prior output parameter curve can be sampled and compared respectively. Specifically, a plurality of specific output voltage values may be selected as sampling basis, and a plurality of first sampling points and a plurality of second sampling points on a real-time output parameter curve corresponding to the output voltage values are determined, where the number of the first sampling points is the same as the number of the second sampling points, and the number of the first sampling points is the number of the specific output voltage values, for example, the output voltage values as sampling basis are respectively 0.5V, 1V, 1.2V and 1.5V, and 4 total values are obtained, and then the number of the first sampling points and the number of the second sampling points are both 4. Further, the following formula may be adopted in the process of determining the real-time output parameter curve evaluation index in step S103:
wherein P represents the real-time output parametric curve evaluation index, t represents the number of samples input with the output voltage and output with the real-time output current value or the prior output current value, k represents the sequence number of the kth sample in the t samples, y k Representing the real-time output current value corresponding to the kth sampling output voltage，y k ' represents the a priori output current value corresponding to the kth sampled output voltage,is y k Mean value of->Is y k ' mean value, wherein FIG. 8 clearly shows the real-time output parameter curve, the a priori output parameter curve, y k Y k ′。
In order to improve the efficiency of determining the real-time image evaluation index, the real-time image and the prior image may be respectively subjected to gridding processing, so that the real-time image and the prior image are respectively divided into a plurality of image units, and the real-time image evaluation index is determined based on the characteristic values of the corresponding image units. In order to improve the convenience in the process of obtaining the real-time image evaluation index, the number of the image units of the real-time image is the same as that of the image units of the prior image, and the number of the image units of the real-time image is the same as that of the image units of the prior image. Specifically, the determination of the real-time image evaluation index in step S202 may employ the following formula:
wherein T represents a real-time image evaluation index, m represents the number of lines of image units obtained by gridding the real-time image or the prior image, n represents the number of columns of image units obtained by gridding the real-time image or the prior image, and I i,j Characteristic values representing image units located in the ith row and jth column in a real-time image, I' i,j Representing the eigenvalues of the image elements in the prior image at the j-th row and j-th column,representation I i,j Mean value of->Is represented by I' i,j Is a mean value of (c). In particular, the characteristic value of the image unit may be a brightness value, a contrast value or a gray value of the image unit.
The analysis system can be provided with a communication module electrically connected with the function management module, so that the analysis system can realize data interaction with the outside through the communication module, and the prior output parameter curve and the prior image can be updated conveniently. Specifically, before the function management module controls the curve comparison module to determine the real-time output parameter curve evaluation index according to the real-time output parameter curve and the predetermined prior output parameter curve, the method further comprises the following operations:
the function management module controls the communication module to establish communication connection with an external data storage platform;
the function management module controls the communication module to acquire data about the a priori output parametric curve from the data storage platform.
And before the function management module controls the image comparison module to determine the real-time image evaluation index based on the real-time image and the predetermined prior image, the method further comprises the operations of:
the function management module controls the communication module to establish communication connection with an external data storage platform.
The function management module controls the communication module to acquire data about the prior image from the data storage platform.
Finally, it should be noted that: in the photovoltaic equipment analysis method based on AI visual assistance disclosed in the embodiment of the invention, the disclosure is only a preferred embodiment of the invention, and is only used for illustrating the technical scheme of the invention, but not limiting the technical scheme; although the invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art will understand that; the technical scheme described in the foregoing embodiments can be modified or some of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the corresponding technical solutions.
Claims (6)
1. The utility model provides a photovoltaic equipment analysis method based on AI vision is characterized in that, the method is applied to in the analysis system, photovoltaic equipment includes photovoltaic module, analysis system includes output parameter acquisition module, curve generation module, curve comparison module, analysis result output module and function management module, output parameter acquisition module, curve generation module, analysis result output module and curve comparison module respectively with the function management module electricity is connected, output parameter acquisition module still with photovoltaic module's output electricity is connected, the method includes:
the function management module acquires real-time output parameters of the photovoltaic module from the output parameter acquisition module, wherein the real-time output parameters comprise real-time output current values and real-time output voltage values of the photovoltaic module;
the function management module controls the curve generation module to generate a real-time output parameter curve according to the real-time output parameter, wherein the real-time output parameter curve is a curve representing the change of the real-time output current value along with the change of the real-time output voltage value;
the function management module controls the curve comparison module to determine a real-time output parameter curve evaluation index according to the real-time output parameter curve and a predetermined prior output parameter curve, wherein the predetermined prior output parameter curve is a curve which represents that the prior output current value of a photovoltaic module running in a normal state changes along with the change of an output voltage value;
the function management module controls the curve comparison module to judge whether the real-time output parameter curve evaluation index is larger than or equal to a predetermined curve evaluation index threshold;
if yes, the function management module controls the analysis result output module to output first result information representing that the photovoltaic module operates normally, and if not, the function management module controls the analysis result output module to output second result information representing that the photovoltaic module operates abnormally.
2. The AI-vision-aided-based photovoltaic device analysis method of claim 1, wherein the analysis system further comprises an image comparison module and a camera module that are respectively electrically connected to the function management module, the method further comprising:
the function management module controls the camera module to shoot a real-time image of the photovoltaic module;
the function management module controls the image comparison module to determine a real-time image evaluation index according to the real-time image and a predetermined prior image, wherein the prior image is an image with dust covered on the surface of the photovoltaic module;
the function management module controls the image comparison module to judge whether the real-time image evaluation index is larger than or equal to a predetermined image evaluation index threshold;
if yes, the function management module controls the analysis result output module to output third result information indicating that the surface of the photovoltaic module is covered with dust, and if not, the function management module controls the analysis result output module to output fourth result information indicating that the surface of the photovoltaic module is not covered with dust.
3. The AI-vision-aided-based photovoltaic apparatus analysis method of claim 2, wherein the function management module controls the curve comparison module to determine a real-time output parameter curve evaluation index from the real-time output parameter curve and a predetermined prior output parameter curve using the following formula:
where P represents the real-time output parametric curve evaluation index, t represents the number of samples with the output voltage as input and the real-time output current value or a priori output current value as output, and k represents one of the t samplesSequence number of kth sample, y k Representing the real-time output current value, y, corresponding to the kth sampling output voltage k ' represents the a priori output current value corresponding to the kth sampled output voltage,is y k Mean value of->Is y k 'mean value'.
4. The AI-vision-aided-based photovoltaic device analysis method of claim 3, wherein the function management module controls the image comparison module to determine a real-time image evaluation index from the real-time image and a predetermined prior image using the following formula:
wherein T represents a real-time image evaluation index, m represents the number of lines of image units obtained by gridding a real-time image or a priori image, n represents the number of columns of image units obtained by gridding the real-time image or the priori image, and I i,j Representing the characteristic value of the image unit positioned in the ith row and the jth column in the real-time image, I' i,j Representing the eigenvalues of the image elements located in the ith row and jth column of the prior image,representation I i,j Mean value of->Is represented by I' i,j Is a mean value of (c).
5. The AI-vision-aided-based photovoltaic device analysis method of claim 4, wherein the feature value is a brightness value, a contrast value, or a grayscale value.
6. The AI-vision-aided-based photovoltaic device analysis method of claim 5, wherein the analysis system further comprises a communication module electrically connected to the function management module, the method further comprising, prior to the function management module controlling the curve comparison module to determine a real-time output parameter curve evaluation index from the real-time output parameter curve and a predetermined a priori output parameter curve:
the function management module controls the communication module to establish communication connection with an external data storage platform;
the function management module controls the communication module to acquire data about a priori output parameter curve from the data storage platform;
and before the function management module controls the image comparison module to determine a real-time image evaluation index based on the real-time image and a predetermined prior image, the method further comprises:
the function management module controls the communication module to establish communication connection with an external data storage platform;
the function management module controls the communication module to acquire data about a priori images from the data storage platform.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310652536.0A CN116488578A (en) | 2023-06-02 | 2023-06-02 | Photovoltaic equipment analysis method based on AI visual assistance |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310652536.0A CN116488578A (en) | 2023-06-02 | 2023-06-02 | Photovoltaic equipment analysis method based on AI visual assistance |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN116488578A true CN116488578A (en) | 2023-07-25 |
Family
ID=87215898
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310652536.0A Pending CN116488578A (en) | 2023-06-02 | 2023-06-02 | Photovoltaic equipment analysis method based on AI visual assistance |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116488578A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117270382A (en) * | 2023-11-20 | 2023-12-22 | 众芯汉创(江苏)科技有限公司 | Unmanned aerial vehicle intelligent equipment management and control system |
-
2023
- 2023-06-02 CN CN202310652536.0A patent/CN116488578A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117270382A (en) * | 2023-11-20 | 2023-12-22 | 众芯汉创(江苏)科技有限公司 | Unmanned aerial vehicle intelligent equipment management and control system |
| CN117270382B (en) * | 2023-11-20 | 2024-01-30 | 众芯汉创(江苏)科技有限公司 | Unmanned aerial vehicle intelligent equipment management and control system |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111967620B (en) | Photovoltaic module diagnosis method, device, equipment and readable storage medium | |
| CN116488578A (en) | Photovoltaic equipment analysis method based on AI visual assistance | |
| CN112731184B (en) | Battery service life detection method and system | |
| CN116424096B (en) | New energy automobile battery acquisition assembly method and system for dynamic resource optimization configuration | |
| CN117054887A (en) | Internal fault diagnosis method for lithium ion battery system | |
| CN115940809A (en) | Solar panel fault detection method based on power data and visual analysis | |
| US11380036B2 (en) | Method of establishing visual images of models of battery status | |
| CN117129815B (en) | Comprehensive detection method and system for multi-degradation insulator based on Internet of things | |
| NO347531B1 (en) | A method for using an anode rod's equidistant voltage drop to predict anode power | |
| CN112910035A (en) | Method and device for detecting battery cell charging, electronic equipment and storage medium | |
| CN116923188A (en) | Charging protection system of new energy vehicle | |
| CN116363984A (en) | Display screen flicker correction method and correction system | |
| CN116345677A (en) | Low-power consumption power supply monitoring method and system based on artificial intelligence | |
| CN115374211A (en) | Workshop equipment operation monitoring method and device based on Internet of things and computer equipment | |
| CN112083340B (en) | Storage battery performance detection method and device, storage medium and terminal | |
| US11454243B2 (en) | Artificial intelligence training method for a target model based on the label matrix | |
| CN110866652B (en) | Online PMU data error correction method and system based on LSTM model | |
| CN117405968B (en) | Equipment energy consumption detection method and system based on big data | |
| CN116028782B (en) | Fuel cell data visualization system and method | |
| CN116125325A (en) | Method and device for detecting consistency of battery cells, vehicle and storage medium | |
| CN116559710A (en) | Battery state detection method, device and system, electronic equipment and storage medium | |
| CN114298495A (en) | Power grid reliability evaluation method based on fine-grained data driving | |
| CN116412027A (en) | Range extender generation power following control method and device, electronic equipment and medium | |
| CN117559634A (en) | Substation power supply operation supervision system based on data analysis | |
| CN114298083A (en) | Inverter three-phase current imbalance identification method and system based on VAE |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |