Storage battery anomaly detection method and device based on power station, electronic equipment, storage medium and program product

Abstract

The embodiment of the invention provides a storage battery anomaly detection method and device based on a power station, electronic equipment, a storage medium and a program product. The power station comprises at least one storage battery, and the method comprises the steps: obtaining a to-be-detected infrared image of the storage battery; performing comparative analysis on the to-be-detected infrared image and the initialized infrared image, and determining an abnormal region in the to-be-detected infrared image; wherein the initialized infrared image represents the temperature field distribution condition of the storage battery in the power station in a normal working state; according to the abnormal area of the to-be-detected infrared image, determining pixel coordinates of the abnormal area in the to-be-detected infrared image; and according to the pixel coordinates of the abnormal area in the to-be-detected infrared image, in combination with the mapping relation model, obtaining storage battery information of the abnormal area in the to-be-detected infrared image. The method is used for achieving the effect of accurately identifying and positioning the temperature abnormity of the storage battery.

Description

Technical Field

[0001] This application relates to the field of power technologies, and in particular, to a method, device, electronic device, storage medium, and program product for detecting abnormal conditions of storage batteries based on a power station. Background Art

[0002] During the process of large-current charge and discharge of a storage battery, the internal chemical substances will undergo violent chemical reactions, which is accompanied by the absorption and release of heat, resulting in a significant increase in the temperature of the battery body.

[0003] Currently, the method of using an infrared temperature detector to monitor the abnormal state of a storage battery can issue a warning of an abnormality in the storage battery, but it cannot directly provide detailed information about which specific storage battery is abnormal, and subsequent inspections and confirmations still rely on manual work.

[0004] Therefore, there is an urgent need for a solution that can accurately identify and locate abnormal temperature conditions of storage batteries. Summary of the Invention

[0005] Embodiments of this application provide a method, device, electronic device, storage medium, and program product for detecting abnormal conditions of storage batteries based on a power station, so as to achieve the effect of accurately identifying and locating abnormal temperatures of storage batteries.

[0006] In a first aspect, embodiments of this application provide a method for detecting abnormal conditions of storage batteries based on a power station, where the power station includes at least one storage battery, and the method includes:

[0007] Obtain an infrared image to be detected of the storage battery;

[0008] Perform comparative analysis on the infrared image to be detected and an initialized infrared image to determine an abnormal area in the infrared image to be detected; where the initialized infrared image represents the temperature field distribution of the storage battery in the power station under normal operating conditions;

[0009] Determine the pixel coordinates of the abnormal area in the infrared image to be detected according to the abnormal area of the infrared image to be detected;

[0010] Obtain storage battery information of the abnormal area in the infrared image to be detected according to the pixel coordinates of the abnormal area in the infrared image to be detected and in combination with a mapping relationship model.

[0011] In a possible implementation manner, performing comparative analysis on the infrared image to be detected and an initialized infrared image to determine an abnormal area in the infrared image to be detected includes:

[0012] Perform comparative analysis on the infrared image to be detected and the initialized infrared image;

[0013] When part of the area in the infrared image to be detected is inconsistent with the corresponding area information in the initialized infrared image, it is determined that the part of the area in the infrared image to be detected is an abnormal area.

[0014] In a possible implementation manner, the method further includes:

[0015] Perform temperature contrast analysis on the infrared image to be detected and the initialized infrared image;

[0016] When the temperature of part of the area in the infrared image to be detected is greater than the preset temperature threshold of the corresponding area in the initialized infrared image, it is determined that the part of the area in the infrared image to be detected is an abnormal area.

[0017] In a possible implementation manner, the method further includes:

[0018] Perform pixel contrast analysis on the infrared image to be detected and the initialized infrared image;

[0019] When the object pixels of part of the area in the infrared image to be detected are inconsistent with the corresponding object pixels in the initialized infrared image, it is determined that the object in the part of the area in the infrared image to be detected is a foreign object;

[0020] Perform marking processing on the foreign object area in the infrared image to be detected.

[0021] In a possible implementation manner, the method further includes:

[0022] Obtain the physical coordinates of the storage battery in the power station, and obtain the initialized infrared image of the storage battery in the power station;

[0023] According to the image processing algorithm, determine the pixel coordinates of the storage battery in the initialized infrared image corresponding to the physical coordinates of the storage battery in the power station;

[0024] According to the physical coordinates of the storage battery and the pixel coordinates of the storage battery, obtain a mapping relationship model.

[0025] In a possible implementation manner, obtaining a mapping relationship model according to the physical coordinates of the storage battery and the pixel coordinates of the storage battery includes:

[0026] According to the physical coordinates of the storage battery and the pixel coordinates of the storage battery, establish a similarity transformation equation for the storage battery coordinates; wherein, the similarity transformation equation for the storage battery coordinates includes coordinate parameters to be solved;

[0027] Perform a solution process on the similarity transformation equation for the storage battery coordinates using the least squares method to obtain the coordinate parameters;

[0028] Determine a mapping relationship model according to the similarity transformation equation of the storage battery coordinates and the coordinate parameters.

[0029] In a possible implementation manner, according to the pixel coordinates of the abnormal area in the infrared image to be detected, combined with the mapping relationship model, obtain the storage battery information of the abnormal area in the infrared image to be detected, including:

[0030] According to the pixel coordinates of the abnormal area in the infrared image to be detected, combined with the mapping relationship model, determine the physical coordinates of the abnormal area in the infrared image to be detected;

[0031] According to the physical coordinates of the abnormal area in the infrared image to be detected, determine the storage battery information of the abnormal area in the infrared image to be detected.

[0032] In a second aspect, an embodiment of the present application provides a storage battery abnormality detection device based on a power station. The power station includes at least one storage battery. The device includes:

[0033] A first acquisition module, configured to acquire an infrared image to be detected of a storage battery;

[0034] An analysis module, configured to perform a comparative analysis on the infrared image to be detected and an initialized infrared image to determine an abnormal area in the infrared image to be detected; wherein, the initialized infrared image represents the temperature field distribution of the storage battery in the power station under normal working conditions;

[0035] A first determination module, configured to determine the pixel coordinates of the abnormal area in the infrared image to be detected according to the abnormal area of the infrared image to be detected;

[0036] A second determination module, configured to obtain the storage battery information of the abnormal area in the infrared image to be detected according to the pixel coordinates of the abnormal area in the infrared image to be detected, in combination with the mapping relationship model.

[0037] In a possible implementation manner, the analysis module is specifically configured to:

[0038] Perform a comparative analysis on the infrared image to be detected and the initialized infrared image;

[0039] When part of the area in the infrared image to be detected is inconsistent with the corresponding area information in the initialized infrared image, determine that part of the area in the infrared image to be detected is an abnormal area.

[0040] In a possible implementation manner, the device further includes:

[0041] Perform a temperature comparative analysis on the infrared image to be detected and the initialized infrared image;

[0042] When the temperature of a partial area in the infrared image to be detected is greater than the preset temperature threshold of the corresponding area in the initialized infrared image, it is determined that the partial area in the infrared image to be detected is an abnormal area.

[0043] In a possible implementation manner, the device further includes:

[0044] Perform pixel comparison and analysis on the infrared image to be detected and the initialized infrared image;

[0045] When the object pixels in a partial area of the infrared image to be detected are inconsistent with the corresponding object pixels in the initialized infrared image, it is determined that the object in the partial area of the infrared image to be detected is a foreign object;

[0046] Mark the foreign object area in the infrared image to be detected.

[0047] In a possible implementation manner, the device further includes:

[0048] A second acquisition module, configured to acquire the physical coordinates of the storage battery in the power station and acquire the initialized infrared image of the storage battery in the power station;

[0049] A third determination module, configured to determine the pixel coordinates of the storage battery in the initialized infrared image corresponding to the physical coordinates of the storage battery in the power station according to an image processing algorithm;

[0050] A fourth determination module, configured to obtain a mapping relationship model according to the physical coordinates of the storage battery and the pixel coordinates of the storage battery.

[0051] In a possible implementation manner, the fourth determination module is specifically configured to:

[0052] Establish a similarity transformation equation of the storage battery coordinates according to the physical coordinates of the storage battery and the pixel coordinates of the storage battery; wherein, the similarity transformation equation of the storage battery coordinates includes coordinate parameters to be solved;

[0053] Solve the similarity transformation equation of the storage battery coordinates using the least squares method to obtain the coordinate parameters;

[0054] Determine a mapping relationship model according to the similarity transformation equation of the storage battery coordinates and the coordinate parameters.

[0055] In a possible implementation manner, the second determination module is specifically configured to:

[0056] Determine the physical coordinates of the abnormal area in the infrared image to be detected according to the pixel coordinates of the abnormal area in the infrared image to be detected and in combination with the mapping relationship model;

[0057] Determine the battery information of the abnormal area in the infrared image to be detected according to the physical coordinates of the abnormal area in the infrared image to be detected.

[0058] In a third aspect, an embodiment of the present application provides an electronic device, including: a memory and a processor;

[0059] The memory stores computer-executable instructions;

[0060] The processor executes the computer-executable instructions stored in the memory, so that the processor executes the above first aspect and / or various possible implementation manners of the first aspect.

[0061] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, in which computer-executable instructions are stored, and when the computer-executable instructions are executed by a processor, they are used to implement the above first aspect and / or various possible implementation manners of the first aspect.

[0062] In a fifth aspect, an embodiment of the present application provides a computer program product, including a computer program, and when the computer program is executed by a processor, it implements the above first aspect and / or various possible implementation manners of the first aspect.

[0063] An abnormal battery detection method, device, electronic device, storage medium and program product based on a power station provided by an embodiment of the present application obtain an infrared image to be detected of a battery and compare and analyze it with an initialized infrared image, so as to determine an abnormal area in the infrared image to be detected. The initialized infrared image represents the temperature field distribution of the battery in the power station under normal working conditions. Further, according to the abnormal area of the infrared image to be detected, determine the pixel coordinates of the abnormal area, and in combination with the mapping relationship model, obtain the battery information corresponding to the abnormal area. By the above means, accurate positioning and rapid detection of the abnormal area of the battery are realized, and the operation and maintenance efficiency and safety of the power station are effectively improved. Description of the Drawings

[0064] The accompanying drawings here are incorporated into the specification and constitute a part of this specification, showing embodiments consistent with the present application, and are used together with the specification to explain the principles of the present application.

[0065] Figure 1a It is the main view of the scene of an abnormal battery detection method based on a power station provided by an embodiment of the present application;

[0066] Figure 1bIt is a top view of the scene of a battery anomaly detection method based on a power station provided by an embodiment of the present application;

[0067] Figure 2 It is a first schematic flowchart of a battery anomaly detection method based on a power station provided by an embodiment of the present application;

[0068] Figure 3 It is a schematic flowchart of a battery anomaly detection method based on a power station provided by an embodiment of the present application Figure 2 ;

[0069] Figure 4 It is a first schematic structural diagram of a battery anomaly detection device based on a power station provided by an embodiment of the present application;

[0070] Figure 5 It is a schematic structural diagram of a battery anomaly detection device based on a power station provided by an embodiment of the present application Figure 2 ;

[0071] Figure 6 It is a schematic structural diagram of an electronic device provided by an embodiment of the present application.

[0072] Through the above-mentioned drawings, specific embodiments of the present application have been shown, and there will be more detailed descriptions hereinafter. These drawings and textual descriptions are not intended to limit the scope of the concept of the present application in any way, but to illustrate the concept of the present application to those skilled in the art by referring to specific embodiments. Specific Embodiments

[0073] Here, exemplary embodiments will be described in detail, and examples thereof are shown in the drawings. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. On the contrary, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0074] Figure 1a It is a front view of the scene of a battery anomaly detection method based on a power station provided by an embodiment of the present application. As Figure 1a shown, the power station includes a thermal imaging infrared temperature measurement camera 101, a battery inspection instrument 102, a battery rack 103, and a battery 104. Figure 1b It is a top view of the scene of a battery anomaly detection method based on a power station provided by an embodiment of the present application. As Figure 1bAs shown in the figure, the power station includes a thermal imaging infrared temperature measuring camera 101, a storage battery inspection instrument 102, a storage battery rack 103, storage batteries 104, and storage battery interconnection lines 105. The thermal imaging infrared temperature measuring camera 101 is installed in the power station to collect infrared temperature measurement data of various equipment in the power station. The types of equipment that need to collect temperature data include: the body temperature of the storage battery 104, the temperature of the storage battery inspection instrument 102, the temperature of the storage battery interconnection lines 105, etc. The dotted line is an example of the range that the thermal imaging infrared temperature measuring camera can capture.

[0075] In the existing solution that uses an infrared temperature detector to monitor the abnormal state of storage batteries, although the infrared temperature detector can capture the phenomenon that the temperature of the storage battery significantly increases during the large-current charge and discharge process and issue an abnormal warning, there are significant problems. Specifically, although the infrared temperature detector can detect abnormal temperature changes, due to the lack of accurate positioning means, it cannot directly provide detailed information about which specific storage battery or which specific position in the storage battery pack has an abnormality. This results in the need to rely on manual labor for cumbersome subsequent inspections and confirmations after receiving the abnormal warning, which is not only inefficient but also may lead to misjudgments or omissions due to human factors.

[0076] Therefore, a method, device, equipment, storage medium, and program product for detecting abnormal storage batteries based on a power station provided by this application can solve the above problems.

[0077] The following uses specific embodiments to elaborate in detail on the technical solution of this application and how the technical solution of this application solves the above technical problems. These several specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of this application will be described below with reference to the accompanying drawings.

[0078] Figure 2 This is a first flow diagram of a method for detecting abnormal storage batteries based on a power station provided by an embodiment of this application. As Figure 2 shown, the power station contains at least one storage battery, and the method includes:

[0079] S201. Obtain the infrared image to be detected of the storage battery.

[0080] Exemplarily, the infrared image to be detected is a temperature distribution image of the storage battery captured in real time using a thermal imaging infrared temperature measuring camera.

[0081] S202. Compare and analyze the infrared image to be detected and the initialized infrared image to determine the abnormal area in the infrared image to be detected; wherein, the initialized infrared image represents the temperature field distribution of the storage battery in the power station under normal working conditions.

[0082] Exemplarily, an infrared image is initialized and defined as a reference map of the temperature field distribution of the battery under standard operating conditions. Usually, after the battery system is installed and operates stably for a period of time, and ensuring a fault-free state, it is accurately captured through thermal imaging technology. This image serves as a reference standard for subsequent analysis and is crucial for evaluating the health status of the battery. Before the comparative analysis, necessary preprocessing steps are performed on the infrared image to be detected and the initialized infrared image, aiming to optimize the image quality and improve the analysis accuracy. The preprocessing process covers key technologies such as removing image noise and enhancing contrast to ensure the accuracy and integrity of the image information. Subsequently, the preprocessed infrared image to be detected is carefully compared with the initialized infrared image, with the focus on comparing the temperature distribution characteristics at the same spatial positions of the two. By accurately measuring and comparing the temperature values of each corresponding point, the areas with abnormal temperatures can be effectively identified. Further, based on the established temperature threshold range or temperature change trend pattern, the abnormal areas in the infrared image to be detected are determined. These abnormal areas usually show that the temperature values deviate significantly (higher or lower) from the temperature levels of the surrounding normal areas, or the temperature distribution pattern shows a significant difference compared with the initialized image.

[0083] S203. Determine the pixel coordinates of the abnormal area in the infrared image to be detected according to the abnormal area of the infrared image to be detected.

[0084] Exemplarily, after determining the abnormal area, an edge detection algorithm (such as Canny edge detection) and a contour search algorithm are used to determine the exact boundary of the abnormal area. Each pixel point in the infrared image to be detected is traversed to check whether it belongs to the abnormal area. For the pixel points belonging to the abnormal area, record their coordinates (u, v) in the image.

[0085] S204. Obtain the battery information of the abnormal area in the infrared image to be detected according to the pixel coordinates of the abnormal area in the infrared image to be detected and in combination with the mapping relationship model.

[0086] Exemplarily, in the operation and maintenance management of a power station, first, a unique identifier, such as a specific ID or precise position coordinates, should be assigned to each battery or battery group on the power station layout diagram or the battery bank arrangement diagram. Next, a mapping relationship model is constructed, which can associate the above identifiers with the corresponding relationship of each pixel coordinate in the infrared image. When an abnormal area is detected and its pixel coordinates are determined, the pre-established mapping relationship model is used to convert these coordinates from the infrared image space to the actual physical coordinate system of the power station layout or battery arrangement. Through this conversion, the specific information of the battery corresponding to the abnormal area can be accurately located, including but not limited to its ID, exact location, model and other detailed parameters. Finally, the information of the battery with abnormal conditions identified is sorted into an easy-to-understand form, such as a detailed report document, an intuitive chart display or a structured database record, and provided to the operation and maintenance management personnel of the power station. This not only helps to quickly locate and handle problems, but also provides data support for subsequent analysis and preventive measures, thereby improving the safety and efficiency of the power station operation.

[0087] A method for detecting battery anomalies based on a power station provided by an embodiment of the present application determines an abnormal area in a to-be-detected infrared image of a battery by obtaining the to-be-detected infrared image of the battery and comparing and analyzing it with an initialized infrared image. The initialized infrared image represents the temperature field distribution of the battery in the power station under normal operating conditions. Further, according to the abnormal area of the to-be-detected infrared image, the pixel coordinates of the abnormal area are determined, and in combination with the mapping relationship model, the battery information corresponding to the abnormal area is obtained. Through the above means, the accurate positioning and rapid detection of the abnormal area of the battery are realized, effectively improving the operation and maintenance efficiency and safety of the power station.

[0088] Figure 3 It is a flow schematic of a method for detecting battery anomalies based on a power station provided by an embodiment of the present application Figure 2 , such as Figure 3 shown. On the basis of the Figure 2 embodiment, a method for detecting battery anomalies based on a power station is described in detail. The method includes:

[0089] S301. Obtain the physical coordinates of the batteries in the power station, and obtain the initialized infrared image of the batteries in the power station; according to the image processing algorithm, determine the pixel coordinates of the batteries in the initialized infrared image corresponding to the physical coordinates of the batteries in the power station; according to the physical coordinates of the batteries and the pixel coordinates of the batteries, obtain the mapping relationship model.

[0090] Exemplarily, obtain the layout diagram or layout information of the battery pack, including key information such as the arrangement of the batteries, the physical location of each battery (such as row and column numbers, coordinates, etc.), type, capacity, etc. Associate the physical coordinates of each battery with the battery information. Use a thermal imaging infrared temperature measurement camera to take infrared images of the batteries in the power station after the batteries are installed and operate stably for a period of time. Ensure that the environment is interference-free during shooting, the image is clear, and all battery areas are covered. Save this image as the initial infrared image for subsequent comparative analysis. Next, use image processing algorithms to extract the feature points of the battery area on the initial infrared image and record the pixel coordinates of these feature points. At the same time, obtain the physical coordinates of the batteries corresponding to the feature points according to the power station layout diagram or on-site measurement. Through a matching algorithm, such as RANSAC, a correspondence relationship between the physical coordinates and the pixel coordinates can be established. After establishing the correspondence relationship, a linear mapping model (such as an affine transformation matrix) can be selected to describe the relationship between the physical coordinates and the pixel coordinates. Optimization algorithms such as the least squares method and the gradient descent method can be used to solve the parameters of the mapping relationship model, and methods such as cross-validation and residual analysis can be used to verify the accuracy and robustness of the model.

[0091] In one example, according to the physical coordinates of the battery and the pixel coordinates of the battery, establish a similarity transformation equation for the battery coordinates; wherein, the similarity transformation equation for the battery coordinates includes the coordinate parameters to be solved; perform a solution process on the similarity transformation equation for the battery coordinates using the least squares method to obtain the coordinate parameters; determine the mapping relationship model according to the similarity transformation equation for the battery coordinates and the coordinate parameters.

[0092] Exemplarily, according to the physical coordinates (x, y) of the battery and the pixel coordinates (u, v) of the battery, establish a similarity transformation equation for the battery coordinates. The similarity transformation equation is as follows:

[0093]

[0094] where s represents the scaling factor, represents the rotation angle, represents the translation amount along the x-axis, represents the translation amount along the y-axis.

[0095] Perform a solution process on the parameters to be solved in the similarity transformation equation for the battery coordinates using the least squares method. The objective function E is shown as follows:

[0096]

[0097] where s, , and are the parameters to be solved.

[0098] By minimizing the objective function E, the optimal solutions of s, , and are determined, thereby establishing a mapping relationship model from physical coordinates to pixel coordinates.

[0099] S302. Obtain the infrared image to be detected of the storage battery.

[0100] Exemplarily, obtain the infrared image to be detected of the storage battery.

[0101] S303. Conduct a comparative analysis on the infrared image to be detected and the initialized infrared image; when the information of some regions in the infrared image to be detected is inconsistent with the information of the corresponding regions in the initialized infrared image, determine that some regions in the infrared image to be detected are abnormal regions.

[0102] Exemplarily, conduct a comparative analysis on the infrared image to be detected and the initialized infrared image. Divide the two images into the same regions or grids for area-by-area comparative analysis. Extract information for each region, such as temperature, brightness, etc. These information can be selected and quantified according to actual needs. Compare the information of each region to determine whether the region in the infrared image to be detected is consistent with the information of the corresponding region in the initialized infrared image. If the information of a certain region in the infrared image to be detected is inconsistent with the information of the corresponding region in the initialized infrared image, and this inconsistency exceeds the preset threshold or range, determine that this region is an abnormal region. The preset threshold or range can be set according to actual needs, such as temperature difference, brightness difference, etc.

[0103] In one example, conduct a temperature comparative analysis on the infrared image to be detected and the initialized infrared image; when the temperature of some regions in the infrared image to be detected is greater than the preset temperature threshold of the corresponding regions in the initialized infrared image, determine that some regions in the infrared image to be detected are abnormal regions.

[0104] Exemplarily, conduct a temperature comparative analysis on the infrared image to be detected and the initialized infrared image. The temperature thresholds of different types of devices (such as storage batteries and storage battery inspection instruments) in the initialized infrared image and the storage battery under different working states are different, so it is necessary to set corresponding temperature thresholds according to the specific device type and working state. Traverse each pixel point in the infrared image to be detected to obtain its temperature value. Corresponding to the pixel point at the same position in the initialized infrared image, obtain its normal temperature range. Determine the preset temperature threshold at this position according to the device type and working state. If the temperature of a certain region in the infrared image to be detected exceeds its preset temperature threshold range, determine that this region is an abnormal region. Mark all the regions determined to be abnormal on the infrared image.

[0105] In one example, pixel-by-pixel comparison and analysis are performed on the infrared image to be detected and the initialized infrared image. When the pixels of the object in a partial area of the infrared image to be detected are inconsistent with the pixels of the corresponding object in the initialized infrared image, it is determined that the object in the partial area of the infrared image to be detected is a foreign object. Marking processing is performed on the foreign object area in the infrared image to be detected.

[0106] Exemplarily, pixel-by-pixel comparison and analysis are performed on the infrared image to be detected and the initialized infrared image. Image processing techniques (such as edge detection, region segmentation, feature extraction, etc.) are used to identify the objects in the image. The positions and ranges of the objects in each image are marked. The pixel values of the corresponding objects in the infrared image to be detected and the initialized infrared image are compared. The inconsistency of the pixel values may be manifested as differences in color, brightness, or temperature. A threshold or range is set to determine whether the inconsistency of the pixel values is significant. If the pixels of an object in the infrared image to be detected are inconsistent with the pixels of the corresponding object in the initialized infrared image, and this inconsistency exceeds the preset threshold or range, it is determined that the object is a foreign object. All areas determined to be foreign objects are marked on the infrared image to be detected. The marking information should include the position, size, and shape of the foreign object, etc. The marking can be a border, highlighting, or other visual indications so that the user can intuitively identify the foreign object area. At the same time, the marking information (such as position, size, shape, etc.) can also be stored in the corresponding data structure for subsequent analysis.

[0107] S304. According to the pixel coordinates of the abnormal area in the infrared image to be detected and in combination with the mapping relationship model, determine the physical coordinates of the abnormal area in the infrared image to be detected; according to the physical coordinates of the abnormal area in the infrared image to be detected, determine the battery information of the abnormal area in the infrared image to be detected.

[0108] Exemplarily, according to the mapping relationship model established in step S301 and the pixel coordinates (u, v) of the abnormal area in the infrared image to be detected, the physical coordinates (x, y) of the abnormal area in the infrared image to be detected can be determined. The calculation formula is as follows:

[0109]

[0110] where s represents the scaling factor, represents the rotation angle, represents the translation amount along the x-axis, represents the translation amount along the y-axis.

[0111] Use the physical coordinates of the abnormal area in the infrared image to be detected to query the battery information at that location, including but not limited to the battery ID, type, model, installation date, etc. If there are detailed database records in the power station management system, relevant information can be directly obtained by querying the database. Once the battery information corresponding to the abnormal area is determined, specific alarm information needs to be generated. The alarm information includes the following contents: abnormal type, battery ID, specific location, suggested measures, timestamp, and other information.

[0112] A battery abnormal detection method based on a power station proposed in an embodiment of the present application. This method first collects the physical coordinates of the batteries in the power station and their initial infrared images under normal working conditions, and then uses image processing technology to determine the corresponding pixel coordinates of these batteries in the initial infrared images, and accordingly constructs a mapping relationship model between the physical coordinates and pixel coordinates of the batteries. During the detection process, by comparing the infrared image to be detected with the initial infrared image, abnormal areas with inconsistent information in the image can be quickly identified. Using the previously established mapping relationship model, the pixel coordinates of these abnormal areas can be accurately converted into physical coordinates, and then combined with the layout information of the power station batteries, the specific locations and related information of the abnormal batteries can be accurately locked. The effect of accurately detecting battery abnormalities is achieved.

[0113] Figure 4 FIG. 1 is a schematic structural diagram of a battery abnormal detection device based on a power station provided in an embodiment of the present application. There is at least one battery in the power station, such as Figure 4 As shown, a battery abnormal detection device 40 provided in this embodiment includes:

[0114] A first acquisition module 401, configured to acquire the infrared image to be detected of the battery;

[0115] An analysis module 402, configured to perform a comparative analysis on the infrared image to be detected and the initial infrared image to determine the abnormal area in the infrared image to be detected; wherein, the initial infrared image represents the temperature field distribution of the batteries in the power station under normal working conditions;

[0116] A first determination module 403, configured to determine the pixel coordinates of the abnormal area in the infrared image to be detected according to the abnormal area of the infrared image to be detected;

[0117] A second determination module 404, configured to obtain the battery information of the abnormal area in the infrared image to be detected according to the pixel coordinates of the abnormal area in the infrared image to be detected and in combination with the mapping relationship model.

[0118] The battery abnormal detection device based on a power station provided in this embodiment can execute the method provided in the above method embodiment, and its implementation principle and technical effects are similar, which will not be elaborated here in this embodiment.

[0119] Figure 5 Structural schematic of a battery anomaly detection device based on a power station provided by an embodiment of the present application Figure 2 , the power station includes at least one battery, such as Figure 5 As shown, a battery anomaly detection device 50 based on a power station provided in this embodiment includes:

[0120] A first acquisition module 501, configured to acquire an infrared image to be detected of the battery;

[0121] An analysis module 502, configured to perform comparative analysis on the infrared image to be detected and the initialized infrared image to determine an abnormal area in the infrared image to be detected; wherein, the initialized infrared image represents the temperature field distribution of the battery in the power station under normal working conditions;

[0122] A first determination module 503, configured to determine the pixel coordinates of the abnormal area in the infrared image to be detected according to the abnormal area of the infrared image to be detected;

[0123] A second determination module 504, configured to obtain battery information of the abnormal area in the infrared image to be detected according to the pixel coordinates of the abnormal area in the infrared image to be detected and in combination with a mapping relationship model.

[0124] In one example, the analysis module 502 is specifically configured to:

[0125] Perform comparative analysis on the infrared image to be detected and the initialized infrared image;

[0126] When part of the area in the infrared image to be detected is inconsistent with the corresponding area information in the initialized infrared image, then determine that part of the area in the infrared image to be detected is an abnormal area.

[0127] In one example, the device 50 further includes:

[0128] Perform temperature comparative analysis on the infrared image to be detected and the initialized infrared image;

[0129] When the temperature of part of the area in the infrared image to be detected is greater than the preset temperature threshold of the corresponding area in the initialized infrared image, then determine that part of the area in the infrared image to be detected is an abnormal area.

[0130] In one example, the device 50 further includes:

[0131] Perform pixel comparative analysis on the infrared image to be detected and the initialized infrared image;

[0132] When the object pixels in a partial area of the infrared image to be detected are inconsistent with the corresponding object pixels in the initialized infrared image, it is determined that the object in the partial area of the infrared image to be detected is a foreign object;

[0133] For the foreign object area in the infrared image to be detected, perform a marking process.

[0134] In one example, the apparatus 50 further includes:

[0135] A second acquisition module 505, configured to acquire the physical coordinates of the storage battery in the power station and acquire the initialized infrared image of the storage battery in the power station;

[0136] A third determination module 506, configured to determine the pixel coordinates of the storage battery in the initialized infrared image corresponding to the physical coordinates of the storage battery in the power station according to an image processing algorithm;

[0137] A fourth determination module 507, configured to obtain a mapping relationship model according to the physical coordinates of the storage battery and the pixel coordinates of the storage battery.

[0138] In one example, the fourth determination module 507 is specifically configured to:

[0139] Establish a similarity transformation equation of the storage battery coordinates according to the physical coordinates of the storage battery and the pixel coordinates of the storage battery; wherein, the similarity transformation equation of the storage battery coordinates includes the coordinate parameters to be solved;

[0140] Perform a solution process on the similarity transformation equation of the storage battery coordinates using the least squares method to obtain the coordinate parameters;

[0141] Determine a mapping relationship model according to the similarity transformation equation of the storage battery coordinates and the coordinate parameters.

[0142] In one example, the second determination module 504 is specifically configured to:

[0143] Determine the physical coordinates of the abnormal area in the infrared image to be detected according to the pixel coordinates of the abnormal area in the infrared image to be detected and in combination with the mapping relationship model;

[0144] Determine the storage battery information of the abnormal area in the infrared image to be detected according to the physical coordinates of the abnormal area in the infrared image to be detected.

[0145] A storage battery abnormality detection device based on a power station provided in this embodiment can execute the method provided in the above method embodiment, and its implementation principle and technical effect are similar, and will not be elaborated here in this embodiment.

[0146] Figure 6 It is a schematic structural diagram of an electronic device provided in an embodiment of the present application. As Figure 6As shown in the figure, the electronic device 60 provided in this embodiment includes: at least one processor 601 and a memory 602. Optionally, the device 60 further includes a communication component 603. Among them, the processor 601, the memory 602, and the communication component 603 are connected through a bus 604.

[0147] In a specific implementation process, at least one processor 601 executes the computer-executable instructions stored in the memory 602, so that at least one processor 601 executes the above-mentioned method.

[0148] For the specific implementation process of the processor 601, reference can be made to the above method embodiment. Their implementation principles and technical effects are similar, and will not be elaborated here in this embodiment.

[0149] In the above embodiment, it should be understood that the processor may be a central processing unit (English: Central Processing Unit, abbreviated as: CPU), or other general-purpose processors, digital signal processors (English: Digital Signal Processor, abbreviated as: DSP), application specific integrated circuits (English: Application Specific Integrated Circuit, abbreviated as: ASIC), etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The steps of the method disclosed in combination with the invention can be directly embodied as being executed by a hardware processor, or executed by a combination of hardware and software modules in the processor.

[0150] The memory may include a high-speed memory (Random Access Memory, RAM), and may also include a non-volatile memory (Non-volatile Memory, NVM), such as at least one disk memory.

[0151] The bus may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For the convenience of representation, the bus in the drawings of this application is not limited to only one bus or one type of bus.

[0152] This application also provides a computer program product, including a computer program, which implements the above-mentioned method when executed by a processor.

[0153] The present application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the above method.

[0154] The above-readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, a magnetic disk or an optical disc. The readable storage medium can be any available medium accessible by a general-purpose or special-purpose computer.

[0155] An exemplary readable storage medium is coupled to the processor so that the processor can read information from the readable storage medium and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can be located in an application specific integrated circuit (ASIC). Of course, the processor and the readable storage medium can also exist as discrete components in a device.

[0156] The division of units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be indirect couplings or communication connections through some interfaces, devices or units, and can be in electrical, mechanical or other forms.

[0157] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0158] In addition, in each embodiment of the present invention, the functional units can be integrated in a processing unit, or each unit can physically exist alone, or two or more units can be integrated in one unit.

[0159] If a function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods of the various embodiments of the present invention. The foregoing storage medium includes: USB flash drives, mobile hard disks, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical discs, etc., all kinds of media that can store program codes.

[0160] Those of ordinary skill in the art can understand that all or part of the steps of implementing the above method embodiments can be completed by hardware related to program instructions. The foregoing program can be stored in a computer-readable storage medium. When this program is executed, it executes the steps including the above method embodiments; and the foregoing storage medium includes: ROMs, RAMs, magnetic disks, or optical discs, etc., all kinds of media that can store program codes.

[0161] Finally, it should be noted that: After considering the specification and practicing the invention disclosed herein, those skilled in the art will easily think of other implementation manners of the present invention. The present invention is intended to cover any variations, uses, or adaptations of the present invention. These variations, uses, or adaptations follow the general principles of the present invention and include common general knowledge or conventional technical means in the technical field not disclosed in the present invention. It is not limited to the exact structures described above and shown in the drawings, and various modifications and changes can be made without departing from its scope. The scope of the present invention is only limited by the appended claims.

Claims

1. A battery abnormality detection method based on a power station, characterized in that: The power station comprises at least one storage battery, and the method comprises: Acquire the infrared image of the battery to be inspected; Comparing and analyzing the infrared image to be detected and the initialization infrared image, determining the abnormal area in the infrared image to be detected; wherein the initialization infrared image represents the temperature field distribution of the storage battery in the power station under normal working conditions; Determining pixel coordinates of the abnormal area in the infrared image to be detected according to the abnormal area in the infrared image to be detected; According to the pixel coordinates of the abnormal area in the infrared image to be detected, combined with the mapping relationship model, the battery information of the abnormal area in the infrared image to be detected is obtained.

2. The method according to claim 1, characterized in that Comparing and analyzing the infrared image to be detected and the initialization infrared image to determine the abnormal area in the infrared image to be detected, including: Comparatively analyzing the infrared image to be detected and the initialized infrared image; When the information of the partial area in the infrared image to be detected is inconsistent with the corresponding area in the initialized infrared image, it is determined that the partial area in the infrared image to be detected is an abnormal area.

3. The method according to claim 2, characterized in that The method further comprises: Performing temperature comparison analysis on the infrared image to be detected and the initialized infrared image; When the temperature of a partial area in the infrared image to be detected is greater than a preset temperature threshold of a corresponding area in the initialized infrared image, it is determined that the partial area in the infrared image to be detected is an abnormal area.

4. The method according to claim 2, characterized in that: The method further comprises: Performing pixel comparison analysis on the infrared image to be detected and the initialized infrared image; When the object pixels in the partial area of ​​the infrared image to be detected are inconsistent with the corresponding object pixels in the initialized infrared image, it is determined that the object in the partial area of ​​the infrared image to be detected is a foreign object; The foreign object area in the infrared image to be detected is marked.

5. The method according to claim 1, characterized in that The method further comprises: Acquiring physical coordinates of the storage batteries in the power station, and acquiring an initialized infrared image of the storage batteries in the power station; Determining pixel coordinates of the battery in the initialized infrared image corresponding to the physical coordinates of the battery in the power station according to an image processing algorithm; A mapping relationship model is obtained according to the physical coordinates of the battery and the pixel coordinates of the battery.

6. The method according to claim 5, characterized in that According to the physical coordinates of the battery and the pixel coordinates of the battery, a mapping relationship model is obtained, including: According to the physical coordinates of the battery and the pixel coordinates of the battery, a similarity transformation equation of the battery coordinates is established; wherein the similarity transformation equation of the battery coordinates includes coordinate parameters to be solved; The similarity transformation equation of the battery coordinates is solved by using the least square method to obtain the coordinate parameters; A mapping relationship model is determined according to the similarity transformation equation of the battery coordinates and the coordinate parameters.

7. The method according to any one of claims 1 to 6, characterized in that According to the pixel coordinates of the abnormal area in the infrared image to be detected, combined with the mapping relationship model, the battery information of the abnormal area in the infrared image to be detected is obtained, including: Determine the physical coordinates of the abnormal area in the infrared image to be detected according to the pixel coordinates of the abnormal area in the infrared image to be detected in combination with the mapping relationship model; According to the physical coordinates of the abnormal area in the infrared image to be detected, the battery information of the abnormal area in the infrared image to be detected is determined.

8. A battery abnormality detection device based on a power station, characterized in that: The power station comprises at least one storage battery, and the device comprises: A first acquisition module is used to acquire an infrared image of the battery to be detected; An analysis module is used to compare and analyze the infrared image to be detected and the initialization infrared image to determine the abnormal area in the infrared image to be detected; wherein the initialization infrared image represents the temperature field distribution of the storage battery in the power station under normal working conditions; A first determination module, used to determine the pixel coordinates of the abnormal area in the infrared image to be detected according to the abnormal area in the infrared image to be detected; The second determination module is used to obtain the battery information of the abnormal area in the infrared image to be detected according to the pixel coordinates of the abnormal area in the infrared image to be detected in combination with the mapping relationship model.

9. An electronic device, characterized in that: include: Memory, processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory, so that the processor performs the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, which are used to implement the method according to any one of claims 1 to 7 when executed by a processor.

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