Abnormality detection method and system for photovoltaic module

Abstract

According to the anomaly detection method and system for the photovoltaic module provided by the invention, the same abnormal operation data needing to be processed corresponding to the target operation state description data contained in the operation data of the solar cell module and the detection mode corresponding to the abnormal operation data needing to be processed are obtained; the method comprises the following steps: classifying the operation data of the solar cell module, classifying the operation state characteristics of each target operation state description data contained in the operation data of the solar cell module, and detecting the abnormal operation data needing to be processed according to the operation state characteristics of each target operation state description data and a detection mode. The detection result can be obtained more accurately, so that the fault position or the damage position and the like can be accurately obtained, and the problem that the efficiency of the photovoltaic module is reduced is effectively avoided.

Description

Technical Field

[0001] The present application relates to the technical field of abnormal data detection, and in particular to a method and system for detecting abnormalities in photovoltaic modules. Background Art

[0002] Photovoltaic modules, also known as solar cell modules, are key devices for converting solar energy into electrical energy. ‌Since the output voltage of a single solar cell is low and unpackaged cells are easily affected by the environment and cause the electrodes to fall off, multiple single cells need to be sealed into modules in series and parallel to improve their reliability and service life. Photovoltaic modules play a core role in solar power generation systems, converting solar energy into electrical energy through the photovoltaic effect, which can be used directly for power supply or stored in batteries for subsequent use. ‌

[0003] As time goes by, photovoltaic modules may be damaged or have low operating efficiency. This requires the detection of the transmission data in the photovoltaic modules. However, how to detect accurately is a technical problem that is difficult to solve at present. Summary of the invention

[0004] In view of this, the present application provides a method and system for detecting abnormalities in a photovoltaic module.

[0005] In a first aspect, a method for detecting abnormality of a photovoltaic module is provided, comprising: Obtaining solar cell module operation data and abnormal operation data that needs to be processed corresponding to a target operation status description data set covered in the solar cell module operation data, wherein the target operation status description data set covers at least one target operation status description data covered in the solar cell module operation data; Obtaining a detection method corresponding to the abnormal operation data that needs to be processed; Classifying the solar cell assembly operation data to classify the operation status characteristics of the target operation status description data set, wherein the operation status characteristics of the target operation status description data set include the operation status characteristics of at least one target operation status description data; In combination with the operating status characteristics of the target operating status description data set and the detection method, the abnormal operating data that needs to be processed is detected to obtain a component abnormal operation detection result.

[0006] Furthermore, the method further comprises at least one of the following: Obtain target abnormal operation data of a target solar cell assembly event corresponding to the target operation status description data set, and determine that the difference between the abnormal operation data to be processed and the target abnormal operation data is not greater than a first predetermined difference; Obtain hidden danger identification results of target solar cell component items corresponding to the target operating status description data set, and based on the hidden danger identification results and the detection method, determine that there is an abnormal information directory in the abnormal operating data that needs to be processed, and the difference between the data in the abnormal information directory and the abnormal operating data that needs to be processed is not greater than a second set difference.

[0007] Further, the target operating state description data includes important description content in the hidden danger identification result, the operating state characteristics include hidden danger identification result operation data, and the abnormal operating data that needs to be processed is detected in combination with the operating state characteristics of the target operating state description data set and the detection method to obtain the component abnormal operation detection result, including: Determine a final identification method corresponding to the target running state description data set in combination with the running state characteristics of the target running state description data set and the detection method; The abnormal operation data that needs to be processed is detected based on the last identification method to obtain a component abnormal operation detection result.

[0008] Further, the combining the operating state characteristics of the target operating state description data set and the detection method to determine the final identification method corresponding to the target operating state description data set includes: If the hidden danger identification result operation data of the target operating status description data set covers a feedback operation, combined with the detection method, the one that meets the following requirements will be determined as the final identification method corresponding to the target operating status description data set: referring to the abnormal operating data that needs to be processed, adjacent to the abnormal operating data that needs to be processed and associated with the feedback operation.

[0009] Further, the combining the operating state characteristics of the target operating state description data set and the detection method to determine the final identification method corresponding to the target operating state description data set includes: If the hidden danger identification result operation data of the target operating state description data set only includes a first correction operation, then in combination with the detection method, determining a correction that refers to the abnormal operating data that needs to be processed and is adjacent to the abnormal operating data that needs to be processed, wherein the first correction operation includes a compensation operation or a filtering operation; The correction is determined as the final identification mode of the target operating status description data.

[0010] Further, the combining the operating state characteristics of the target operating state description data set and the detection method to determine the final identification method corresponding to the target operating state description data set includes: If the hidden danger identification result operation data of the target operating status description data set covers the second correction operation, then in combination with the detection method, the one that meets the following requirements will be determined as the final identification method corresponding to the target operating status description data set: Refer to the abnormal operating data that needs to be processed, the abnormal operating data that is adjacent to the abnormal operating data that needs to be processed and before correction corresponding to the second correction operation; wherein the second correction operation includes at least two of a compensation operation, a filtering operation and an optimization operation.

[0011] Further, the combining the operating state characteristics of the target operating state description data set and the detection method to determine the final identification method corresponding to the target operating state description data set includes: If the hidden danger identification result operation data of the target operation status description data set only covers optimization operations, in combination with the detection method, determine an abnormal information directory that refers to the abnormal operation data that needs to be processed and is adjacent to the abnormal operation data that needs to be processed; If the abnormal information catalog is an abnormal information catalog corresponding to more than one item in the feedback or correction, the target solar cell component item corresponding to the target operating status description data set is determined, and the hidden danger identification result is consistent with the hidden danger identification result of the target solar cell component item and refers to the sample indication of the abnormal information catalog, and is determined as the final identification method corresponding to the target operating status description data set.

[0012] Furthermore, the method further comprises: Obtaining a target solar cell assembly item corresponding to the target operating status description data set; Obtaining a first data transmission channel covered by the solar cell assembly operation data and a second data transmission channel of the target solar cell assembly event; In combination with the first data transmission channel and the second data transmission channel, the target solar cell component event is detected.

[0013] In a second aspect, a photovoltaic component abnormality detection system is provided, comprising a processor and a memory communicating with each other, wherein the processor is used to read a computer program from the memory and execute the computer program to implement the above method.

[0014] In a third aspect, a cloud platform includes: a memory for storing a computer program; and a processor connected to the memory, for executing the computer program stored in the memory to implement the above method.

[0015] A photovoltaic module abnormality detection method and system provided in an embodiment of the present application obtains the same abnormal operation data to be processed corresponding to each target operation status description data covered in the solar cell module operation data, and the detection method corresponding to the abnormal operation data to be processed. By classifying the solar cell module operation data, the operation status characteristics of each target operation status description data covered in the solar cell module operation data are classified, and the abnormal operation data to be processed are detected according to the operation status characteristics and detection method of each target operation status description data, so as to obtain the detection result more accurately. In this way, the fault location or damage location can be accurately obtained, and the problem of reduced efficiency of photovoltaic modules can be effectively avoided. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0017] Figure 1 A flowchart of a photovoltaic module abnormality detection method provided in an embodiment of the present application.

[0018] Figure 2 A block diagram of a device for constructing user data based on big data provided in an embodiment of the present application.

[0019] Figure 3 An architectural diagram of an abnormality detection system for a photovoltaic module provided in an embodiment of the present application. DETAILED DESCRIPTION

[0020] In order to better understand the above technical scheme, the technical scheme of the present application is described in detail below through the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present application and the specific features in the embodiments are detailed descriptions of the technical scheme of the present application, rather than limitations on the technical scheme of the present application. In the absence of conflict, the embodiments of the present application and the technical features in the embodiments can be combined with each other.

[0021] See also Figure 1 , shows a method for detecting abnormalities of a photovoltaic component, which may include the technical solutions described in the following steps 100 to 400.

[0022] Step 100, obtaining solar cell module operation data and abnormal operation data that needs to be processed corresponding to a target operation status description data set covered in the solar cell module operation data, wherein the target operation status description data set covers at least one target operation status description data covered in the solar cell module operation data.

[0023] Exemplarily, the target operation state description data set represents a set of people or things in motion during the data operation process.

[0024] Step 200: obtaining a detection method corresponding to the abnormal operation data that needs to be processed.

[0025] Step 300, classifying the solar cell module operation data to obtain the operation status characteristics of the target operation status description data set, wherein the operation status characteristics of the target operation status description data set include the operation status characteristics of at least one target operation status description data.

[0026] It should be understood that the operating state feature represents a key feature of data anomalies in the target operating state description data set.

[0027] Step 400 , combining the operating state characteristics of the target operating state description data set and the detection method, detect the abnormal operating data that needs to be processed to obtain a component abnormal operation detection result.

[0028] It should be understood that when executing the technical solution described in the above steps 100 to 400, the same abnormal operating data to be processed corresponding to each target operating status description data covered in the solar cell module operating data and the detection method corresponding to the abnormal operating data to be processed are obtained. By classifying the solar cell module operating data, the operating status characteristics of each target operating status description data covered in the solar cell module operating data are classified, and the abnormal operating data to be processed are detected according to the operating status characteristics and detection methods of each target operating status description data, so that the detection results can be obtained more accurately. In this way, the fault location or damage location can be accurately obtained, and the problem of reduced efficiency of photovoltaic modules can be effectively avoided.

[0029] Based on the above foundation, the technical solutions described in the following steps q1 and q2 are also included.

[0030] Step q1, obtaining target abnormal operation data of a target solar cell component event corresponding to the target operation status description data set, and determining that the difference between the abnormal operation data to be processed and the target abnormal operation data is not greater than a first predetermined difference.

[0031] Step q2, obtain the hidden danger identification result of the target solar cell component item corresponding to the target operating status description data set, and based on the hidden danger identification result and the detection method, determine that there is an abnormal information directory in the abnormal operating data that needs to be processed, and the difference between the data in the abnormal information directory and the abnormal operating data that needs to be processed is not greater than the second set difference.

[0032] It should be understood that when executing the technical solutions described in the above steps q1 and q2, by distinguishing between the abnormal operation data to be processed and the target abnormal operation data, it is possible to accurately determine whether the abnormal operation data to be processed has an abnormal information directory.

[0033] On the one hand, in a possible implementation example, the inventors found that the target operating status description data includes important description content in the hidden danger identification result, the operating status characteristics include hidden danger identification result operation data, and the abnormal operating data that needs to be processed is detected in combination with the operating status characteristics of the target operating status description data set and the detection method. There is a technical problem of inaccurate operating status characteristics and detection methods, which makes it difficult to accurately obtain the abnormal operation detection result of the component. In order to improve the above technical problems, the target operating status description data described in step 400 includes important description content in the hidden danger identification result, the operating status characteristics include hidden danger identification result operation data, and the abnormal operating data that needs to be processed is detected in combination with the operating status characteristics of the target operating status description data set and the detection method. The step of obtaining the abnormal operation detection result of the component can specifically include the technical solutions described in the following steps s11 and s12.

[0034] Step s11, combining the operating status characteristics of the target operating status description data set and the detection method, determining the final identification method corresponding to the target operating status description data set.

[0035] Step s12: detecting the abnormal operation data that needs to be processed based on the last identification method to obtain a component abnormal operation detection result.

[0036] It should be understood that when executing the technical solutions described in the above steps s11 and s12, the target operating status description data includes important description content in the hidden danger identification results, and the operating status characteristics include the hidden danger identification result operation data. The operating status characteristics of the target operating status description data set and the detection method are combined to detect the abnormal operating data that needs to be processed, avoiding technical problems such as inaccurate operating status characteristics and detection methods, so as to accurately obtain the abnormal operation detection results of the components.

[0037] On the one hand, in a possible implementation embodiment, the inventors found that, in combination with the operating status characteristics of the target operating status description data set and the detection method, there is a problem of inaccurate operation data of the hidden danger identification result, which makes it difficult to accurately determine the final identification method corresponding to the target operating status description data set. In order to improve the above technical problems, the step described in step s11 of determining the final identification method corresponding to the target operating status description data set in combination with the operating status characteristics of the target operating status description data set and the detection method can specifically include the technical solution described in the following step s11a1.

[0038] Step s11a1, if the hidden danger identification result operation data of the target operating status description data set covers the feedback operation, combined with the detection method, the one that meets the following requirements is determined as the final identification method corresponding to the target operating status description data set: referring to the abnormal operating data that needs to be processed, adjacent to the abnormal operating data that needs to be processed and associated with the feedback operation.

[0039] It should be understood that when executing the technical solution described in the above step s11a1, the operating status characteristics of the target operating status description data set and the detection method are combined to avoid the problem of inaccurate operating data of the hidden danger identification results, so that the final identification method corresponding to the target operating status description data set can be accurately determined.

[0040] On the one hand, in a possible implementation embodiment, the inventors found that, in combination with the operating status characteristics of the target operating status description data set and the detection method, there is a technical problem of related data defects, which makes it difficult to accurately determine the final identification method corresponding to the target operating status description data set. In order to improve the above technical problem, the step described in step s11 of determining the final identification method corresponding to the target operating status description data set in combination with the operating status characteristics of the target operating status description data set and the detection method can specifically include the technical solutions described in the following steps s11b1 and s11b2.

[0041] Step s11b1, if the hidden danger identification result operation data of the target operating status description data set only covers the first correction operation, then in combination with the detection method, determine the correction adjacent to the abnormal operating data that needs to be processed and distinguish the abnormal operating data that needs to be processed, and the first correction operation includes a compensating operation or a filtering operation.

[0042] Step s11b2, determining the correction as the final identification method of the target operating status description data.

[0043] It should be understood that when executing the technical solutions described in the above steps s11b1 and s11b2, the operating status characteristics of the target operating status description data set and the detection method are combined to avoid technical problems of related data defects, so that the final identification method corresponding to the target operating status description data set can be accurately determined.

[0044] On the one hand, in a possible implementation embodiment, the inventors found that there is a technical problem of inadequate correction operation in combination with the operating status characteristics of the target operating status description data set and the detection method, making it difficult to accurately determine the final identification method corresponding to the target operating status description data set. In order to improve the above technical problem, the step described in step s11 of determining the final identification method corresponding to the target operating status description data set in combination with the operating status characteristics of the target operating status description data set and the detection method can specifically include the technical solution described in the following step s11c1.

[0045] Step s11c1, if the hidden danger identification result operation data of the target operating status description data set covers the second correction operation, then in combination with the detection method, the one that meets the following requirements will be determined as the final identification method corresponding to the target operating status description data set: refer to the abnormal operating data that needs to be processed, the abnormal operating data that is adjacent to the abnormal operating data that needs to be processed and before correction corresponding to the second correction operation.

[0046] Exemplarily, the second correction operation includes at least two of a compensation operation, a filtering operation, and an optimization operation.

[0047] It should be understood that when executing the technical solution described in the above step s11c1, the operating status characteristics of the target operating status description data set and the detection method are combined to avoid technical problems of inadequate correction operations, so that the final identification method corresponding to the target operating status description data set can be accurately determined.

[0048] On the one hand, in a possible implementation embodiment, the inventors discovered that, in combination with the operating status characteristics of the target operating status description data set and the detection method, there is a technical problem of inaccurate abnormal information directory adjacent to the abnormal operating data that needs to be processed, making it difficult to accurately determine the final identification method corresponding to the target operating status description data set. In order to improve the above technical problem, the step described in step s11 of determining the final identification method corresponding to the target operating status description data set in combination with the operating status characteristics of the target operating status description data set and the detection method can specifically include the technical solutions described in the following steps s11d1 and s11d2.

[0049] Step s11d1, if the hidden danger identification result operation data of the target operating status description data set only covers optimization operations, combined with the detection method, determine the abnormal information directory that refers to the abnormal operating data that needs to be processed and is adjacent to the abnormal operating data that needs to be processed.

[0050] Step s11d2, if the abnormal information catalog is an abnormal information catalog corresponding to more than one item in the feedback or correction, then determine the target solar cell component item corresponding to the target operating status description data set, and determine the hidden danger identification result that is consistent with the hidden danger identification result of the target solar cell component item and refer to the sample indication of the abnormal information catalog as the final identification method corresponding to the target operating status description data set.

[0051] It should be understood that when executing the technical solutions described in the above steps s11d1 and s11d2, the operating status characteristics of the target operating status description data set and the detection method are combined to avoid the technical problem of inaccurate abnormal information directories adjacent to the abnormal operating data that needs to be processed, thereby being able to accurately determine the final identification method corresponding to the target operating status description data set.

[0052] Based on the above foundation, the technical solution described in the following steps w1 to w3 may also be included.

[0053] Step w1, obtaining the target solar cell component items corresponding to the target operating status description data set.

[0054] Step w2, obtaining the first data transmission channel covered by the solar cell assembly operation data and the second data transmission channel of the target solar cell assembly event.

[0055] Step w3, combining the first data transmission channel and the second data transmission channel to detect the target solar cell component event.

[0056] It should be understood that when executing the technical solutions described in the above steps w1 to w3, the target solar cell module events are accurately detected through a multi-dimensional data transmission channel.

[0057] On the one hand, in a possible implementation embodiment, the inventors found that there is a problem of inaccurate matching coefficients in combination with the first data transmission channel and the second data transmission channel, making it difficult to accurately detect the target solar cell component matters. In order to improve the above technical problems, the step described in step w3 of detecting the target solar cell component matters in combination with the first data transmission channel and the second data transmission channel can specifically include the technical solutions described in the following steps w3a1-step w3a3.

[0058] Step w3a1, if the matching coefficient between the first data transmission channel and the second data transmission channel is not less than a first set value, then in combination with the detection method, the main data transmission channel with the smallest difference between the hidden danger identification result and the hidden danger identification result of the target solar cell component matter is determined as the final identification method of the target operating status description data.

[0059] Step w3a2, if the matching coefficient between the first data transmission channel and the second data transmission channel is not greater than the second set value, then in combination with the detection method, the secondary data transmission channel with the smallest difference between the hidden danger identification result and the hidden danger identification result of the target solar cell component matter is determined as the final identification method of the target operating status description data, and the second set value is less than the first set value.

[0060] Step w3a3, if the matching coefficient between the first data transmission channel and the second data transmission channel is greater than the second set value and less than the first set value, the target solar cell component event is determined as the final identification method of the target operating status description data.

[0061] It should be understood that when executing the technical solution described in the above steps w2a1 to w2a3, the problem of inaccurate matching coefficients is avoided according to the first data transmission channel and the second data transmission channel, so that the target solar cell component can be accurately detected.

[0062] Based on the above foundation, the solar cell module operation data is any solar cell module operation data in the solar cell module operation data sequence, and the solar cell module operation data sequence is obtained by a solar cell module operation data acquisition device, and can also include the technical solutions described in the following steps r1 to r3.

[0063] Step r1, obtaining the target solar cell component event corresponding to the target operating status description data set, and determining the hidden danger identification result of the target solar cell component event.

[0064] Step r2, obtaining the processing corresponding to the acquisition trajectory of the solar cell assembly operation data sequence corresponding to the solar cell assembly operation data.

[0065] Step r3, combining the processing corresponding to the obtained trajectory and the hidden danger identification result, detecting the target solar cell component event.

[0066] It should be understood that when executing the technical solutions described in the above steps r1 to r3, the target solar cell component issues can be accurately detected by improving the accuracy of the hidden danger identification results of the target solar cell component issues.

[0067] On the one hand, in a possible implementation embodiment, the inventors found that there is a problem of inconsistent hidden danger identification results in combination with the processing corresponding to the obtained trajectory and the hidden danger identification results, which makes it difficult to accurately detect the target solar cell component matters. In order to improve the above technical problems, the step described in step r3 of detecting the target solar cell component matters in combination with the processing corresponding to the obtained trajectory and the hidden danger identification results can specifically include the technical solution described in the following step r3a1.

[0068] Step r3a1, if the processing corresponding to the obtained trajectory is inconsistent with the hidden danger identification result, then the reverse direction corresponding to the target solar cell component event is found in combination with the detection method, and it is determined as the identification after detection; if the processing corresponding to the obtained trajectory is consistent with the hidden danger identification result, the target solar cell component event is determined as after detection.

[0069] It should be understood that when executing the technical solution described in the above step r3a1, the processing corresponding to the obtained trajectory and the hidden danger identification result are combined to avoid the problem of inconsistent hidden danger identification results, so that the target solar cell component matters can be accurately detected.

[0070] Based on the above, please refer to Figure 2 , a device 200 for constructing user data based on big data is provided, which is applied to a cloud platform, and the device comprises: The abnormal operation data acquisition module 210 is used to obtain the solar cell module operation data and the abnormal operation data that needs to be processed corresponding to the target operation state description data set covered by the solar cell module operation data, wherein the target operation state description data set covers at least one target operation state description data covered by the solar cell module operation data; A detection method acquisition module 220 is used to obtain a detection method corresponding to the abnormal operation data that needs to be processed; An operation status feature classification module 230 is used to classify the operation data of the solar cell assembly to classify the operation status features of the target operation status description data set, wherein the operation status features of the target operation status description data set include the operation status features of at least one target operation status description data set; The abnormal operation data detection module 240 is used to detect the abnormal operation data that needs to be processed in combination with the operation status characteristics of the target operation status description data set and the detection method, and obtain the component abnormal operation detection result.

[0071] Based on the above, please refer to Figure 3 , shows a photovoltaic component abnormality detection system 300, including a processor 310 and a memory 320 that communicate with each other, and the processor 310 is used to read and execute a computer program from the memory 320 to implement the above method.

[0072] The present application provides a cloud platform, comprising: a memory for storing a computer program; a processor connected to the memory, for executing the computer program stored in the memory to implement the above method.

[0073] In summary, based on the above scheme, the same abnormal operation data that needs to be processed corresponding to each target operation status description data covered in the solar cell module operation data and the detection method corresponding to the abnormal operation data that needs to be processed are obtained. By classifying the solar cell module operation data, the operation status characteristics of each target operation status description data covered in the solar cell module operation data are classified, and the abnormal operation data that needs to be processed is detected according to the operation status characteristics and detection method of each target operation status description data. The detection result can be obtained more accurately. In this way, the fault location or damage location can be accurately obtained, and the problem of reduced efficiency of photovoltaic modules can be effectively avoided.

[0074] It should be understood that the system and its modules shown above can be implemented in various ways. For example, in some embodiments, the system and its modules can be implemented by hardware, software, or a combination of software and hardware. Among them, the hardware part can be implemented using dedicated logic; the software part can be stored in a memory and executed by an appropriate instruction execution system, such as a microprocessor or a dedicated design hardware. It should be understood by those skilled in the art that the above methods and systems can be implemented using computer executable instructions and / or included in processor control codes, such as on a carrier medium such as a disk, CD or DVD-ROM, a programmable memory such as a read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. Such code is provided. The system and its modules of the present application can not only be implemented by hardware circuits such as ultra-large-scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., but can also be implemented by software such as executed by various types of processors, and can also be implemented by a combination of the above hardware circuits and software (e.g., firmware).

[0075] It should be noted that different embodiments may produce different beneficial effects. In different embodiments, the beneficial effects that may be produced may be any one or a combination of the above, or any other beneficial effects that may be obtained.

[0076] The basic concepts have been described above. Obviously, for those skilled in the art, the above detailed disclosure is only determined as an example and does not constitute a limitation of the present application. Although not explicitly stated herein, those skilled in the art may make various modifications, improvements and amendments to the present application. Such modifications, improvements and amendments are suggested in the present application, so such modifications, improvements and amendments still belong to the spirit and scope of the exemplary embodiments of the present application.

[0077] At the same time, the present application uses specific words to describe the embodiments of the present application. For example, "one embodiment", "an embodiment", and / or "some embodiments" refer to a certain feature, structure or characteristic related to no less than one embodiment of the present application. Therefore, it should be emphasized and noted that "one embodiment" or "an embodiment" or "an alternative embodiment" mentioned twice or more in different positions in this specification does not necessarily refer to the same embodiment. In addition, some features, structures or characteristics in one or more embodiments of the present application can be appropriately combined.

[0078] In addition, it should be understood by those skilled in the art that various aspects of the present application can be illustrated and described by a number of patentable categories or situations, including any new and useful process, machine, product or combination of substances, or any new and useful improvements thereto. Accordingly, various aspects of the present application can be performed entirely by hardware, entirely by software (including firmware, resident software, microcode, etc.), or by a combination of hardware and software. The above hardware or software may all be referred to as "data blocks", "modules", "engines", "units", "components" or "systems". In addition, various aspects of the present application may be represented as a computer product located in one or more computer-readable media, which includes computer-readable program code.

[0079] Computer storage media may include a propagated data signal containing computer program code, such as in baseband or as part of a carrier wave. The propagated signal may be in a variety of forms, including electromagnetic, optical, etc., or a suitable combination. Computer storage media can be any computer-readable medium other than a computer-readable storage medium, which can be connected to an instruction execution system, device or equipment to communicate, propagate or transmit the program for use. The program code on the computer storage medium can be transmitted via any suitable medium, including radio, cable, fiber optic cable, RF, or similar media, or any combination of the above media.

[0080] The computer program codes required for the operation of each part of the present application can be written in any one or more programming languages, including object-oriented programming languages ​​such as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C++, C#, VB.NET, Python, etc., conventional procedural programming languages ​​such as C language, Visual Basic, Fortran 2003, Perl, COBOL 2002, PHP, ABAP, dynamic programming languages ​​such as Python, Ruby and Groovy, or other programming languages, etc. The program code can be run entirely on the user's computer, or determined as an independent software package to run on the user's computer, or partially run on the user's computer and partially run on a remote computer, or run entirely on a remote computer or server. In the latter case, the remote computer can be connected to the user's computer through any network form, such as a local area network (LAN) or a wide area network (WAN), or connected to an external computer (e.g., via the Internet), or in a cloud computing environment, or determined as a service such as software as a service (SaaS).

[0081] In addition, unless explicitly stated in the claims, the order of the processing elements and sequences described in this application, the use of alphanumeric characters, or the use of other names are not intended to limit the order of the processes and methods of this application. Although the above disclosure discusses some invention embodiments that are currently considered useful through various examples, it should be understood that such details are only for illustrative purposes, and the attached claims are not limited to the disclosed embodiments. On the contrary, the claims are intended to cover all modifications and equivalent combinations that meet the essence and scope of the embodiments of this application. For example, although the system components described above can be implemented by hardware devices, they can also be implemented only by software solutions, such as installing the described system on an existing server or mobile device.

[0082] Similarly, it should be noted that in order to simplify the description of the disclosure of this application and thus help understand one or more embodiments of the invention, in the above description of the embodiments of this application, multiple features are sometimes combined into one embodiment, figure or description thereof. However, this disclosure method does not mean that the features required by the object of this application are more than the features mentioned in the claims. In fact, the features of the embodiments are less than all the features of the single embodiment disclosed above.

[0083] In some embodiments, numbers describing the number of components and attributes are used. It should be understood that such numbers used for the description of the embodiments are modified by the modifiers "about", "approximately" or "substantially" in some examples. Unless otherwise specified, "about", "approximately" or "substantially" indicate that the numbers allow adaptive changes. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximate values, which can be changed according to the required characteristics of individual embodiments. In some embodiments, the numerical parameters should take into account the specified significant digits and adopt the general method of retaining the digits. Although the numerical domains and parameters used to confirm the breadth of the range in some embodiments of the present application are approximate values, in specific embodiments, the setting of such numerical values ​​is as accurate as possible within the feasible range.

[0084] Each patent, patent application, patent application disclosure, and other materials, such as articles, books, instructions, publications, documents, etc., cited in this application are hereby incorporated by reference in their entirety. Except for application history documents that are inconsistent with or conflicting with the content of this application, documents that limit the broadest scope of the claims of this application (currently or later attached to this application) are also excluded. It should be noted that if the description, definition, and / or use of terms in the attached materials of this application are inconsistent or conflicting with the content described in this application, the description, definition, and / or use of terms in this application shall prevail.

[0085] Finally, it should be understood that the embodiments described in this application are only used to illustrate the principles of the embodiments of the present application. Other variations may also fall within the scope of the present application. Therefore, the alternative configurations of the embodiments of the present application, which are determined as examples rather than limitations, may be considered consistent with the teachings of the present application. Accordingly, the embodiments of the present application are not limited to the embodiments explicitly introduced and described in the present application.

[0086] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included within the scope of the claims of the present application.

Claims

1. A method for detecting abnormality of a photovoltaic module, characterized in that: The method comprises: Obtaining solar cell module operation data and abnormal operation data that needs to be processed corresponding to a target operation status description data set covered in the solar cell module operation data, wherein the target operation status description data set covers at least one target operation status description data covered in the solar cell module operation data; Obtaining a detection method corresponding to the abnormal operation data that needs to be processed; Classifying the solar cell assembly operation data to classify the operation status characteristics of the target operation status description data set, wherein the operation status characteristics of the target operation status description data set include the operation status characteristics of at least one target operation status description data; In combination with the operating status characteristics of the target operating status description data set and the detection method, the abnormal operating data that needs to be processed is detected to obtain a component abnormal operation detection result.

2. The method according to claim 1, characterized in that The method further comprises at least one of the following: Obtain target abnormal operation data of a target solar cell assembly event corresponding to the target operation status description data set, and determine that the difference between the abnormal operation data to be processed and the target abnormal operation data is not greater than a first predetermined difference; Obtain hidden danger identification results of target solar cell component items corresponding to the target operating status description data set, and based on the hidden danger identification results and the detection method, determine that there is an abnormal information directory in the abnormal operating data that needs to be processed, and the difference between the data in the abnormal information directory and the abnormal operating data that needs to be processed is not greater than a second set difference.

3. The method according to claim 1, characterized in that The target operation state description data includes important description content in the hidden danger identification result, the operation state characteristics include hidden danger identification result operation data, and the abnormal operation data to be processed are detected in combination with the operation state characteristics of the target operation state description data set and the detection method to obtain the component abnormal operation detection result, including: Determine a final identification method corresponding to the target running state description data set in combination with the running state characteristics of the target running state description data set and the detection method; The abnormal operation data that needs to be processed is detected based on the last identification method to obtain a component abnormal operation detection result.

4. The method according to claim 3, characterized in that The determining the final identification method corresponding to the target running state description data set by combining the running state characteristics of the target running state description data set and the detection method includes: If the hidden danger identification result operation data of the target operating status description data set covers a feedback operation, combined with the detection method, the one that meets the following requirements will be determined as the final identification method corresponding to the target operating status description data set: referring to the abnormal operating data that needs to be processed, adjacent to the abnormal operating data that needs to be processed and associated with the feedback operation.

5. The method according to claim 3, characterized in that: The determining the final identification method corresponding to the target running state description data set by combining the running state characteristics of the target running state description data set and the detection method includes: If the hidden danger identification result operation data of the target operating state description data set only includes a first correction operation, then in combination with the detection method, determining a correction that refers to the abnormal operating data that needs to be processed and is adjacent to the abnormal operating data that needs to be processed, wherein the first correction operation includes a compensation operation or a filtering operation; The correction is determined as the final identification mode of the target operating status description data.

6. The method according to claim 3, characterized in that The determining the final identification method corresponding to the target running state description data set by combining the running state characteristics of the target running state description data set and the detection method includes: If the hidden danger identification result operation data of the target operating status description data set covers the second correction operation, then in combination with the detection method, the one that meets the following requirements will be determined as the final identification method corresponding to the target operating status description data set: Refer to the abnormal operating data that needs to be processed, the abnormal operating data that is adjacent to the abnormal operating data that needs to be processed and before correction corresponding to the second correction operation; wherein the second correction operation includes at least two of a compensation operation, a filtering operation and an optimization operation.

7. The method according to claim 3, characterized in that The determining the final identification method corresponding to the target running state description data set by combining the running state characteristics of the target running state description data set and the detection method includes: If the hidden danger identification result operation data of the target operation status description data set only covers optimization operations, in combination with the detection method, determine an abnormal information directory that refers to the abnormal operation data that needs to be processed and is adjacent to the abnormal operation data that needs to be processed; If the abnormal information catalog is an abnormal information catalog corresponding to more than one item in the feedback or correction, the target solar cell component item corresponding to the target operating status description data set is determined, and the hidden danger identification result is consistent with the hidden danger identification result of the target solar cell component item and refers to the sample indication of the abnormal information catalog, and is determined as the final identification method corresponding to the target operating status description data set.

8. The method according to any one of claims 2 to 7, characterized in that: The method further comprises: Obtaining a target solar cell assembly item corresponding to the target operating status description data set; Obtaining a first data transmission channel covered by the solar cell assembly operation data and a second data transmission channel of the target solar cell assembly event; In combination with the first data transmission channel and the second data transmission channel, the target solar cell component event is detected.

9. A photovoltaic module abnormality detection system, characterized in that: The invention comprises a processor and a memory communicating with each other, wherein the processor is used to read a computer program from the memory and execute the computer program to implement the method according to any one of claims 1 to 8.

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