Battery replacement process execution method, electronic equipment and computer storage medium

Abstract

The invention discloses a battery replacement process execution method, electronic equipment and a computer storage medium, and the method comprises the steps: obtaining first image data of a power battery loaded on a vehicle in response to entering a battery replacement process, and judging whether the power battery has a preset type of surface defect or not according to the first image data; if the power battery has the surface defect, acquiring corresponding second image data of the power battery in the previous battery replacement process; judging whether the power battery has newly added surface defects according to the first image data and the second image data; if yes, stopping executing the battery replacement process; and if not, continuing to execute the battery replacement process. According to the method, the first image data of the power battery are obtained after the vehicle enters the battery replacement process, the corresponding second image data of the power battery in the previous battery replacement process are obtained when the power battery has surface defects, and whether the battery replacement process continues to be executed or not is judged by combining the first image data and the second image data; and the problem that the misdiagnosed power battery is repeatedly misdiagnosed is effectively solved.

Description

Technical Field

[0001] The present application relates to the field of vehicle battery replacement technology, and specifically to a battery replacement process execution method, electronic equipment and computer storage medium. Background Art

[0002] At present, when a vehicle enters a battery swap station for battery swapping, the detection module based on the artificial intelligence visual model will detect the surface of the power battery to determine whether the power battery is intact. However, the effectiveness of the artificial intelligence model detection is based on the sample input amount during model learning, but the damage of the power battery pack occurs randomly and the shape is uncertain, so it is easy to misdiagnose the use marks that do not affect normal use as surface defects.

[0003] The above-mentioned misdiagnosed power batteries can continue to be used in battery swap stations after being confirmed to be able to be used normally. However, there is a phenomenon that the artificial intelligence visual model cannot quickly optimize the misdiagnosis in real time. The misdiagnosed power batteries may be repeatedly misdiagnosed during the circulation of various battery swap stations during the period when the model is not optimized and updated, which significantly affects the vehicle's battery swap efficiency and reduces the user experience. Summary of the invention

[0004] In response to the above technical problems, the present application provides a battery replacement process execution method, an electronic device and a computer storage medium, aiming to solve the problem that the power battery is repeatedly misdiagnosed during its circulation in various battery replacement stations due to the inability of the artificial intelligence visual model to optimize the misdiagnosis in real time.

[0005] In order to solve the above technical problems, the present application proposes a method for executing a battery replacement process, the method comprising: In response to entering a battery replacement process, acquiring first image data of a power battery loaded on the vehicle, and determining whether the power battery has a surface defect of a preset type according to the first image data; If the power battery has the surface defect, obtaining the second image data corresponding to the power battery in the previous battery replacement process; determining whether the power battery has a new surface defect according to the first image data and the second image data; If yes, terminate the battery replacement process; If not, continue to perform the battery replacement process.

[0006] Optionally, before judging whether the power battery has a surface defect of a preset type according to the first image data, the method further includes: Determining, according to the first image data, whether the state of the power battery is marked as a misdiagnosed state in a previous round of battery replacement process; The method further comprises: If the state of the power battery is marked as a misdiagnosed state in the previous round of battery replacement process, skip the step of determining whether the power battery has a preset type of surface defect according to the first image data, and proceed to the step of obtaining the second image data corresponding to the power battery in the previous round of battery replacement process; If the state of the power battery has not been marked as a misdiagnosed state in the previous round of battery replacement process, the step of determining whether the power battery has a preset type of surface defect based on the first image data is entered.

[0007] Optionally, after terminating the battery replacement process, the method further includes: Reviewing the power battery according to preset qualification standards; When the power battery meets the qualification standard, the power battery is marked as the misdiagnosis state.

[0008] Optionally, after marking the power battery as being in the misdiagnosed state, the method further includes: Storing the order data corresponding to the power battery in the misdiagnosed state in a preset cloud server; Among them, the order data includes at least one of the battery replacement order number, vehicle information, battery code, battery replacement time, and image data.

[0009] Optionally, judging whether the power battery has a newly added surface defect according to the first image data and the second image data includes: Acquire the number and / or position of the surface defects according to the first image data and the second image data; Whether the power battery has new surface defects is determined based on the number and / or location of the surface defects.

[0010] Optionally, judging whether the power battery has new surface defects according to the number and / or position of the surface defects includes: Acquire a first number and a second number of the surface defects from the first image data and the second image data, respectively; When the first number is greater than the second number, determining that the power battery has newly added surface defects; When the first number is less than or equal to the second number, respectively acquiring a first target frame and a second target frame for selecting the surface defects from the first image data and the second image data; It is determined whether the power battery has new surface defects according to the first target frame and the second target frame.

[0011] Optionally, judging whether the power battery has a newly added surface defect according to the first target frame and the second target frame includes: Acquire the overlap degree of the surface defect between the first image data and the second image data according to the first target frame and the second target frame; When the overlap degree is greater than a preset overlap degree threshold, it is determined that there is no new surface defect in the power battery; When the overlap degree is less than a preset overlap degree threshold, it is determined that the power battery has a newly added surface defect.

[0012] Optionally, judging whether the power battery has new surface defects according to the number and / or position of the surface defects includes: Acquire a first position and a second position of the surface defect from the first image data and the second image data respectively; When the first position matches the second position, it is determined that the power battery has no new surface defects. The present application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the battery replacement process execution method as described above when executing the computer program.

[0013] The present application also provides a computer storage medium, which stores a computer program. When the computer program is executed by the processor, the steps of the battery replacement process execution method as described above are implemented.

[0014] In the present application, the first image data of the power battery is obtained after the vehicle enters the battery replacement process, and the second image data corresponding to the power battery in the previous round of battery replacement process is obtained when there are surface defects in the power battery. By combining the two to determine whether to continue the battery replacement process, the problem of misdiagnosed power batteries being repeatedly misdiagnosed is effectively solved. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] 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.

[0016] Figure 1 This is a schematic diagram of a method for executing a battery replacement process shown in an embodiment of the present application. Figure 1 .

[0017] Figure 2 This is a schematic diagram of a method for executing a battery replacement process shown in an embodiment of the present application. Figure 2 .

[0018] Figure 3 It is a module diagram of a cloud server shown in an embodiment of the present application.

[0019] Figure 4 It is a structural schematic diagram of an electronic device shown in an embodiment of the present application.

[0020] Description of Figure Numbers: 310. Processor; 311. Memory; 312. Network interface; 313. Bus system. DETAILED DESCRIPTION

[0021] The following is an explanation of the implementation of the present application by means of specific embodiments. People familiar with the art can easily understand other advantages and effects of the present application from the contents disclosed in this specification.

[0022] In the following description, reference is made to the accompanying drawings, which describe several embodiments of the present application. It should be understood that other embodiments may be used and that mechanical, structural, electrical, and operational changes may be made without departing from the spirit and scope of the present application. The following detailed description should not be considered limiting, and the terms used herein are only for the purpose of describing specific embodiments and are not intended to limit the present application.

[0023] Although the terms first, second, etc. are used herein to describe various elements in some instances, these elements should not be limited by these terms. These terms are only used to distinguish one element from another element.

[0024] Furthermore, as used in this article, the singular forms "one", "an" and "the" are intended to include plural forms as well, unless there is an indication to the contrary in the context. It should be further understood that the terms "comprise", "include" indicate the presence of the described features, steps, operations, elements, components, projects, kinds, and / or groups, but do not exclude the presence, occurrence or addition of one or more other features, steps, operations, elements, components, projects, kinds, and / or groups. The terms "or" and "and / or" used herein are interpreted as inclusive, or mean any one or any combination. Therefore, "A, B or C" or "A, B and / or C" means "any of the following: A; B; C; A and B; A and C; B and C; A, B and C". Exceptions to this definition will only occur when the combination of elements, functions, steps or operations is inherently mutually exclusive in some way.

[0025] In theory, there are two solutions to the technical problems described in the background art.

[0026] Solution 1: By exploring online learning technology, the AI ​​vision model can learn and adjust in real time during operation to adapt to new data distribution and reduce false positives. In combination with incremental learning methods, the model can be updated based on new data without re-reading the entire data set. However, this solution requires the edge to have sufficient resources for model training iterations, and the station side needs to optimize the model separately. R&D engineers need to devote a lot of manpower to focus on the optimization of each battery swap station, which is very costly.

[0027] Solution 2: Use 3D cameras, vision combined with structured light and other sensors for fusion judgment to reduce false alarms. Although this solution can reduce the number of misdiagnoses to a certain extent, the problem of repeated misdiagnosis still exists, and the cost is high.

[0028] Therefore, in contrast to the above two solutions which are high in cost and difficult to promote and use, the present application proposes a method for executing a battery replacement process, which can effectively solve the technical problems described in the background technology without the need for adding other hardware equipment.

[0029] Please refer to Figure 1 , Figure 1 A schematic flow chart of a method for executing a battery replacement process is shown, the method comprising: S10. In response to entering the battery swapping process, the first image data of the power battery loaded on the vehicle is obtained, and based on the first image data, it is determined whether the power battery has a preset type of surface defect. Specifically, in this embodiment, when the vehicle enters the battery swapping station and performs the battery swapping process, the shooting module installed inside the battery swapping station will begin to obtain the first image data of the power battery loaded on the vehicle, and transmit the first image data to a preset artificial intelligence visual model. The artificial intelligence visual model detects and analyzes the first image data to determine whether the power battery has a preset type of surface defect. Among them, the preset type of surface defect can be battery bulging, battery perforation, battery flanging, battery notch, battery crack, battery scratch, battery notch, battery trauma and battery depression, etc., which are not limited here.

[0030] S20. If the power battery has surface defects, obtain the second image data corresponding to the power battery in the previous round of battery replacement process. Specifically, in this embodiment, if the artificial intelligence visual model detects that the power battery has surface defects in step S10, the system will automatically obtain the second image data corresponding to the power battery in the previous round of battery replacement process in the cloud. The second image data refers to the image data obtained by the camera module and stored in the cloud when the power battery is loaded on other vehicles and enters the battery replacement process, that is, the historical image data of the power battery.

[0031] S30, judging whether the power battery has new surface defects according to the first image data and the second image data. Specifically, in one embodiment, the system may be provided with a comparison module, and the comparison module compares the first image data with the second image data to judge whether the power battery has new surface defects in the process of being replaced by the current vehicle.

[0032] S40, if yes, terminate the battery replacement process; if no, continue the battery replacement process. Specifically, in this embodiment, if a new surface defect is found in the power battery during the comparison and detection of the first image data and the second image data, it is proved that the power battery is in a risky state and there is a certain safety risk. At this time, the system will automatically terminate the battery replacement process. On the contrary, it is proved that the power battery is in a healthy state and can continue to be put into circulation and use in the battery replacement station, and the system will automatically continue the battery replacement process.

[0033] Preferably, the setting positions of the camera modules of all battery swap stations are preferably completely consistent, so that the first image data and the second image data obtained at different battery swap stations are highly comparable, thereby improving the accuracy of diagnosis. The first image data and the second image data may include 360° images or videos taken by the camera module of the power battery being removed from the vehicle and loaded on the vehicle.

[0034] Please refer to Figure 2 , by executing the above steps S10~S40, in the context that the artificial intelligence visual model cannot be optimized in real time for misdiagnosis, for the power battery that was misdiagnosed in the previous round of battery replacement process, when it enters the battery replacement process with the vehicle entering other battery replacement stations, by comparing and analyzing the first image data obtained in this round of battery replacement process with the second image data obtained in the previous round of battery replacement process, when the first image data is the same as the second image data, it can be determined that the power battery does not have any new surface defects. In this way, the power battery that was misdiagnosed in the previous round of battery replacement process will no longer be intercepted by the artificial intelligence visual model that has not been updated and optimized in time in this round of battery replacement process for the same reason, which significantly improves the battery replacement efficiency and user experience.

[0035] Exemplarily, assume that there are two drivers, Zhang San and Li Si, who drive different vehicles, and a first battery swap station and a second battery swap station at different geographical locations. When driver Zhang San drives his vehicle into the first battery swap station, the use traces on the power battery A loaded on his vehicle are misdiagnosed by the artificial intelligence visual model as a preset type of surface defect. After the station staff confirms that it is a case of misdiagnosis by the artificial intelligence model, the power battery A will continue to be put into use and will be recharged at the first battery swap station. Driver Li Si drives the vehicle into the first battery swap station to replace the depleted power battery B loaded on the vehicle with a fully charged power battery A. Then, when power battery A is depleted, Li Si drives the vehicle into the second battery swap station. At this time, the second battery swap station will obtain the current image data of power battery A, that is, the first image data, and when it is found that power battery A has a preset type of surface defect, it will obtain the image data of power battery A obtained by the first battery swap station from the cloud, that is, the second image data. When the first image data is the same as the second image data, it proves that the artificial intelligence vision model of the second battery swap station also misdiagnoses the usage traces of power battery A as a preset type of surface defect. At this time, the second battery swap station will no longer terminate the battery swap process for Li Si's vehicle as the first battery swap station does for Zhang San's vehicle, but will automatically continue the battery swap process, significantly improving the battery swap efficiency.

[0036] According to the above examples, it can be understood that the current round of battery swapping process refers to the battery swapping process in which Li Si drives the vehicle into the second battery swapping station for battery swapping, and the first image data is the image data of power battery A obtained by the second battery swapping station. The previous round of battery swapping process refers to the battery swapping process in which Zhang San drives the vehicle into the first battery swapping station for battery swapping, and the second image data is the image data of power battery A obtained by the first battery swapping station.

[0037] Optionally, before determining whether the power battery has a surface defect of a preset type according to the first image data in step S10, the method further includes: It is determined based on the first image data whether the state of the power battery has been marked as a misdiagnosed state in the previous round of battery replacement process. Specifically, in this embodiment, the first image data includes relevant information of the power battery. By obtaining relevant information of the battery and performing relevant retrieval and matching in the cloud, it is possible to know whether the state of the power battery has been marked as a misdiagnosed state in the previous round of battery replacement process. Among them, the misdiagnosed state refers to the misdiagnosis of usage marks that do not affect normal use as surface defects. After subsequent review and confirmation that it is the result of misdiagnosis by the artificial intelligence visual model, the power battery is marked as a misdiagnosed state.

[0038] Before executing the step of judging whether the power battery has a preset type of surface defect according to the first image data, on the one hand, if the state of the power battery is marked as a misdiagnosed state in the previous round of battery replacement process, the step of judging whether the power battery has a preset type of surface defect according to the first image data in step S10 is skipped, and the step of obtaining the second image data corresponding to the power battery in the previous round of battery replacement process is directly executed. On the other hand, if the state of the power battery is not marked as a misdiagnosed state in the previous round of battery replacement process, it is necessary to execute the step of judging whether the power battery has a preset type of surface defect according to the first image data in step S10. In real life, the probability of a power battery forming surface defects during normal driving of a vehicle is extremely small. Therefore, in this embodiment, the power battery can be marked as a misdiagnosed state, and the steps can be adaptively skipped to further improve the battery replacement efficiency.

[0039] Optionally, after the battery replacement process is terminated in step S40, the method further includes: S41. Review the power battery according to the preset qualified standards; S42. When the power battery meets the qualification standard, mark the power battery as a misdiagnosed state.

[0040] Specifically, in this embodiment, when the power battery is determined by the artificial intelligence visual model to have a preset type of surface defect, the system will terminate the battery replacement process and arrange for relevant station staff to go for manual review. In actual implementation, after the battery swap station lifts the vehicle, the station staff will review the area identified as a surface defect by the artificial intelligence visual model according to the preset qualification standards. When the judgment result obtained by at least one station staff member is that the power battery meets the qualification standards, it is determined that the power battery has been misdiagnosed by the artificial intelligence visual model. At this time, the station staff will mark the power battery as a misdiagnosed state.

[0041] Furthermore, after the power battery is marked as misdiagnosed, the station staff can use the authority to control the battery swap station to continue the battery swap process to remove the misdiagnosed power battery from the vehicle and transport it to the charging station at the battery swap station for normal charging.

[0042] Optionally, after marking the power battery as a misdiagnosed state in step S42, the method further includes: S421. Store the order data corresponding to the power battery in the misdiagnosed state to the preset cloud server. Specifically, in this embodiment, by creating a cloud server (data warehouse), only the order data corresponding to the power battery in the misdiagnosed state is stored, and the order data corresponding to the power battery in the normal state can be selectively stored, thereby achieving the purpose of saving and maximizing the effective utilization of the cloud server storage space. Furthermore, the function of querying the battery image data at a specified location can also be realized through the development interface. Among them, the order data includes at least one of the battery replacement order number, vehicle information, battery code, battery replacement time, and image data.

[0043] Please refer to Figure 3 In one implementation, a cloud server can be built through the following system to enable all battery swap stations to upload, download, share and interconnect data.

[0044] 1. Data upload and storage system 1.1 Data collection module, including: 1.1.1 Battery swap order information unit: used to obtain battery swap order number, vehicle information, battery code, battery swap time, image data, etc.

[0045] 1.1.2 Battery image data unit: used to obtain first image data, second image data, ... Nth image data.

[0046] 1.2 Upload mechanism module, including: 1.2.1 Data format unit: Based on standardized formats (such as JSON, XML, JPG), it is used to ensure the standardization of data.

[0047] 1.2.2 Secure transmission unit: Encrypted data transmission based on HTTPS protocol to ensure data security.

[0048] 1.2.3 Data interface unit: Based on API interface, it is used to upload data to the cloud, supporting real-time or batch uploading.

[0049] 1.3 Cloud storage module, including: 1.3.1 Service selection unit: used to automatically select a suitable cloud service platform (such as AWS, Azure, Google Cloud) and automatically determine the storage solution (such as object storage, database).

[0050] 1.3.2 Data structure unit: used to design data tables and storage structures for efficient data management and retrieval.

[0051] 2. Query interface development system 2.1 Interface design module, including: 2.1.1 Query condition unit: used to query information related to surface defects through conditions such as battery replacement order number, vehicle information, battery code, battery replacement time, image data, etc.

[0052] 2.1.2 Return result unit: used for the interface to return the battery image and related information (such as image URL, shooting time, battery code information, etc.) at the specified point.

[0053] 3. Implementation and Test System 3.1 API implementation module: used to adaptively select the appropriate technology stack (such as Flask, Spring Boot, Express, etc.) to develop the interface according to actual needs.

[0054] 3.2 Test module: used to conduct comprehensive tests to ensure the stability and accuracy of the interface.

[0055] By establishing a cloud server and developing a query interface, and combining the edge AI visual model to detect and compare the first image data of the current order with the second image data of the historical order, the damage of the power battery can be accurately determined, effectively avoiding the recurrence of misdiagnosis, and improving the efficiency and accuracy of battery replacement. In addition, based on the development and use of this cloud server, not only the accuracy of data management and damage detection is improved, but also the progress of the battery replacement process is optimized through intelligent comparison and decision-making.

[0056] Optionally, judging whether there is a new surface defect in the power battery according to the first image data and the second image data in step S30 includes: S31, acquiring the number and / or positions of surface defects according to the first image data and the second image data; S32. Determine whether the power battery has new surface defects based on the number and / or location of the surface defects.

[0057] Specifically, in this embodiment, the number and / or position of the surface defects in the first image data and the second image data are captured respectively, so that the system will automatically compare and analyze the number and / or position of the surface defects in the two images. When the results are completely consistent, it can be determined that there are no new surface defects in the power battery. Otherwise, there are new surface defects.

[0058] Optionally, judging whether the power battery has new surface defects according to the number and / or position of the surface defects in step S32 includes: S321, respectively obtaining a first number and a second number of surface defects from the first image data and the second image data. Specifically, in this embodiment, it is determined whether the power battery has new surface defects according to the number of surface defects. Wherein, when the first number is greater than the second number, it means that the number of surface defects of the power battery in the current driving cycle is greater than the number of surface defects of the power battery in the previous driving cycle, and it can be directly determined that in the current driving cycle, the power battery has been newly damaged by the current driver, and at this time, the artificial intelligence visual model will determine that the power battery has new surface defects.

[0059] S322: When the first number is less than or equal to the second number, obtain a first target frame and a second target frame for selecting surface defects from the first image data and the second image data, respectively. Specifically, in this embodiment, when the first number is less than or equal to the second number, it means that the number of surface defects of the power battery in the current driving cycle is less than or equal to the number of surface defects of the power battery in the previous driving cycle. At this time, it is still impossible to determine whether the power battery has been newly damaged by the current driver in the current driving cycle. Based on this, the system will obtain a first target frame and a second target frame for selecting surface defects from the first image data and the second image data, respectively.

[0060] Among them, before the misdiagnosed power battery re-enters the charging station of the battery swap station, the maintenance personnel will refurbish or repair the area of ​​the power battery that was misdiagnosed as a surface defect by the artificial intelligence visual model to a certain extent. Based on this, when the misdiagnosed power battery enters another battery swap station with the vehicle for the battery swap process, there is a situation where the number of surface defects of the power battery in the current driving cycle obtained by the battery swap station is less than or equal to the number of surface defects of the power battery in the previous driving cycle.

[0061] For example, before the power battery A in the misdiagnosed state re-enters the charging station of the battery swap station, it has 5 usage marks that do not affect normal use. After the maintenance personnel refurbish and eliminate 3 of the usage marks (2 usage marks remain), the power battery A is put into the charging station of the battery swap station for charging. The fully charged power battery A is loaded on the vehicle and becomes depleted after a driving cycle. At this time, the driver drives the vehicle into another battery swap station for the battery swap process. When the first number is less than the second number, there is: Case 1: In this round of driving cycle, the current driver has not caused any additional damage to the power battery A. At this time, the first number (remaining 2 usage marks) is less than the second number (5 misdiagnosed usage marks that do not affect normal use); Case 2: In this round of driving cycle, the current driver caused 2 new surface defects to the power battery A. At this time, the first number (2 new surface defects + remaining 2 usage marks) is still less than the second number (5 misdiagnosed usage marks that do not affect normal use). Based on the above example, when the first number is smaller than the second number, it is impossible to determine whether the power battery has been newly damaged by the current driver in the current driving cycle.

[0062] Based on the above example, on the one hand, if the maintenance personnel did not refurbish the power battery A in the misdiagnosed state before re-entering the charging station of the battery swap station, and directly put the power battery A into the charging station of the battery swap station for charging, when the first number (5 use traces that do not affect normal use) is equal to the second number (5 use traces that do not affect normal use), it can be obtained that in this round of driving cycle, the current driver did not cause new damage to the power battery A. On the other hand, if the maintenance personnel refurbished the 5 use traces that do not affect normal use before the power battery A in the misdiagnosed state re-entered the charging station of the battery swap station and eliminated 3 of the use traces, when the first number (remaining 2 use traces + 3 newly added damages) is equal to the second number (5 use traces that do not affect normal use), it can be obtained that in this round of driving cycle, the current driver caused 3 new damages to the power battery A. Based on the above example, when the first number is equal to the second number, it is also impossible to determine whether the power battery has been newly damaged by the current driver in this round of driving cycle.

[0063] S323, judging whether there are new surface defects in the power battery according to the first target frame and the second target frame. Specifically, based on the above example, when the first number is less than or equal to the second number, the system will further judge whether there are new surface defects in the power battery according to the first target frame and the second target frame to improve the accuracy of the detection result.

[0064] Optionally, judging whether there is a newly added surface defect in the power battery according to the first target frame and the second target frame in step S323 includes: The overlap degree of the surface defects between the first image data and the second image data is obtained according to the first target frame and the second target frame. Specifically, the system will use the first target frame to select the surface defects appearing in the first image data, and use the second target frame to select the surface defects appearing in the second image data. In this embodiment, the overlap degree is calculated according to the following formula: IoU = (Aint / Auni); Among them, Aint is the area of ​​the first target frame and the subset of the second target frame; Auni is the area of ​​the union of the first target frame and the second target frame.

[0065] It can be understood that based on the above calculation formula, the higher the overlap, the higher the overlap between the first target frame and the second target frame. When the overlap is greater than the preset overlap threshold, the system will automatically determine that there are no new surface defects in the power battery. Otherwise, it is determined that there are new surface defects in the power battery.

[0066] Optionally, judging whether the power battery has new surface defects according to the number and / or position of the surface defects in step S32 includes: S321', respectively obtain a first position and a second position of the surface defect from the first image data and the second image data. Specifically, in this embodiment, it is determined whether the power battery has a new surface defect according to the position of the surface defect. The first position and the second position are preferably coordinate information.

[0067] S322', when the first position matches the second position, it is determined that the power battery does not have any new surface defects. Specifically, in this embodiment, when the first position matches the second position, it can be determined that the power battery does not have any new surface defects regardless of whether the state of the power battery is a misdiagnosis state.

[0068] Based on the same inventive concept as the above embodiments, an embodiment of the present invention provides an electronic device, such as Figure 4 As shown, the device includes: a processor 310 and a memory 311 storing a computer program; wherein, Figure 4 The processor 310 shown in the figure is not used to indicate that the number of the processor 310 is one, but is only used to indicate the positional relationship of the processor 310 relative to other devices. In actual applications, the number of the processor 310 may be one or more; similarly, Figure 4The memory 311 shown in the figure has the same meaning, that is, it is only used to refer to the position relationship of the memory 311 relative to other devices. In practical applications, the number of memories 311 can be one or more. When the processor 310 runs the computer program, the method applied to the above device is implemented.

[0069] The device may also include: at least one network interface 312. The various components in the device are coupled together via a bus system 313. It is understood that the bus system 313 is used to achieve connection and communication between these components. In addition to the data bus, the bus system 313 also includes a power bus, a control bus, and a status signal bus. However, for the sake of clarity, Figure 4 Various buses are labeled as bus system 313.

[0070] The memory 311 may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. The non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), a magnetic random access memory (FRAM), a flash memory, a magnetic surface memory, an optical disc, or a compact disc read-only memory (CD-ROM); the magnetic surface memory may be a disk memory or a tape memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of RAM are available, such as static random access memory (SRAM), synchronous static random access memory (SSRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM, SyncLink Dynamic Random Access Memory), and direct RAMbus random access memory (DRRAM, Direct Rambus Random Access Memory).The memory 311 described in the embodiments of the present invention is intended to include but is not limited to these and any other suitable types of memories.

[0071] The memory 311 in the embodiment of the present invention is used to store various types of data to support the operation of the device. Examples of these data include: any computer program used to operate on the device, such as an operating system and an application; contact data; phone book data; messages; pictures; videos, etc. Among them, the operating system includes various system programs, such as a framework layer, a core library layer, a driver layer, etc., which are used to implement various basic services and process hardware-based tasks. The application program may include various applications, such as a media player (Media Player), a browser (Browser), etc., which are used to implement various application services. Here, the program that implements the method of the embodiment of the present invention may be included in the application program.

[0072] Based on the same inventive concept as the above-mentioned embodiment, this embodiment further provides a computer-readable storage medium, in which a computer program is stored. The computer-readable storage medium may be a ferromagnetic random access memory (FRAM), a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), a flash memory, a magnetic surface memory, an optical disc, or a compact disc read-only memory (CD-ROM); it may also be various devices including one or any combination of the above-mentioned memories, such as a mobile phone, a computer, a tablet device, a personal digital assistant, etc. When the computer program stored in the computer-readable storage medium is executed by the processor, the above-mentioned method is implemented. For the specific steps implemented when the computer program is executed by the processor, please refer to Figure 1 The description of the illustrated embodiment will not be repeated here.

[0073] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0074] In this document, the terms "comprises," "comprising," or any other variations thereof, are intended to cover a non-exclusive inclusion of elements other than those listed and may also include additional elements not expressly listed.

[0075] All the above embodiments are only specific implementation methods of the present application, which are used to illustrate the technical solution of the present application rather than to limit it. The protection scope of the present application is not limited thereto. Any equivalent deformation or replacement of the technical solution recorded in the above embodiments by any technician familiar with the technical field is included in the scope defined by the claims of the present application.

Claims

1. A method for executing a battery replacement process, characterized in that: The method comprises: In response to entering a battery replacement process, acquiring first image data of a power battery loaded on the vehicle, and determining whether the power battery has a surface defect of a preset type according to the first image data; If the power battery has the surface defect, obtaining the second image data corresponding to the power battery in the previous battery replacement process; determining whether the power battery has a new surface defect according to the first image data and the second image data; If yes, terminate the battery replacement process; If not, continue to perform the battery replacement process.

2. The method according to claim 1, characterized in that Before determining whether the power battery has a surface defect of a preset type according to the first image data, the method further includes: Determining, according to the first image data, whether the state of the power battery is marked as a misdiagnosed state in a previous round of battery replacement process; The method further comprises: If the state of the power battery is marked as a misdiagnosed state in the previous round of battery replacement process, skip the step of determining whether the power battery has a preset type of surface defect according to the first image data, and proceed to the step of obtaining the second image data corresponding to the power battery in the previous round of battery replacement process; If the state of the power battery has not been marked as a misdiagnosed state in the previous round of battery replacement process, the step of determining whether the power battery has a preset type of surface defect based on the first image data is entered.

3. The method according to claim 2, characterized in that After terminating the battery replacement process, the method further includes: Reviewing the power battery according to preset qualification standards; When the power battery meets the qualification standard, the power battery is marked as the misdiagnosis state.

4. The method according to claim 3, characterized in that After marking the power battery as being in the misdiagnosed state, the method further includes: Storing the order data corresponding to the power battery in the misdiagnosed state in a preset cloud server; Among them, the order data includes at least one of the battery replacement order number, vehicle information, battery code, battery replacement time, and image data.

5. The method according to claim 1, characterized in that The determining whether the power battery has a newly added surface defect according to the first image data and the second image data includes: Acquire the number and / or position of the surface defects according to the first image data and the second image data; Whether the power battery has new surface defects is determined based on the number and / or location of the surface defects.

6. The method according to claim 5, characterized in that The determining whether the power battery has new surface defects according to the number and / or position of the surface defects includes: Acquire a first number and a second number of the surface defects from the first image data and the second image data, respectively; When the first number is greater than the second number, determining that the power battery has newly added surface defects; When the first number is less than or equal to the second number, respectively acquiring a first target frame and a second target frame for selecting the surface defects from the first image data and the second image data; It is determined whether the power battery has new surface defects according to the first target frame and the second target frame.

7. The method according to claim 6, characterized in that The determining, according to the first target frame and the second target frame, whether the power battery has a newly added surface defect includes: Acquire the overlap degree of the surface defect between the first image data and the second image data according to the first target frame and the second target frame; When the overlap degree is greater than a preset overlap degree threshold, it is determined that there is no new surface defect in the power battery; When the overlap degree is less than a preset overlap degree threshold, it is determined that the power battery has a newly added surface defect.

8. The method according to claim 5, characterized in that The determining whether the power battery has new surface defects according to the number and / or position of the surface defects includes: Acquire a first position and a second position of the surface defect from the first image data and the second image data respectively; When the first position matches the second position, it is determined that the power battery has no new surface defects.

9. An electronic device, characterized in that: It includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and when the processor executes the computer program, it implements the steps of the method for executing the battery replacement process as described in any one of claims 1 to 8.

10. A computer storage medium, characterized in that: The computer storage medium stores a computer program, and when the computer program is executed by the processor, the steps of the method for executing the battery replacement process as described in any one of claims 1 to 8 are implemented.

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