Multi-module charging system intelligent test method, device and equipment

Abstract

The invention relates to the field of equipment testing, and provides an intelligent testing method, device and equipment for a multi-module charging system, the multi-module charging system comprises at least one charging module, and the method comprises the steps: carrying out the module-level testing of the at least one charging module, and obtaining the module testing parameters corresponding to the charging modules; determining module prediction parameters of the charging module in the multi-module charging system according to the module test parameters; determining a network position parameter corresponding to each module test parameter according to the position of each charging module in the multi-module charging system; determining system prediction parameters for performing system-level testing on the multi-module charging system according to the module prediction parameters and the network position parameters; and analyzing the system prediction parameter based on a preset deep learning network to obtain a target system prediction parameter corresponding to the multi-module charging system.

Description

Technical Field

[0001] The present application relates to the field of equipment testing, and in particular to an intelligent testing method, device and equipment for a multi-module charging system. Background Art

[0002] The multi-module charging system can dynamically allocate power according to the charging needs of different vehicles, realize power sharing and flexible charging, and is therefore favored by more and more users. However, due to the complexity of the multi-module charging system, when testing the multi-module charging system, it is necessary not only to perform module-level testing on each module, but also to perform system-level testing on the multi-module charging system as a whole, resulting in high testing costs for the multi-module charging system. How to simplify the testing process of the intelligent testing of the multi-module charging system has become an urgent problem to be solved. Summary of the invention

[0003] The main purpose of this application is to provide a multi-module charging system intelligent testing method, device and equipment, aiming to simplify the testing process of the multi-module charging system intelligent testing.

[0004] In a first aspect, the present application provides an intelligent testing method for a multi-module charging system, the intelligent testing method for a multi-module charging system comprising the following steps: Performing module-level testing on at least one of the charging modules to obtain module testing parameters corresponding to each of the charging modules; Determining module prediction parameters of the charging module in the multi-module charging system according to the module test parameters; Determine the network location parameter corresponding to each module test parameter according to the position of each charging module in the multi-module charging system; Determining system prediction parameters for performing system-level testing on the multi-module charging system according to the module prediction parameters and the network location parameters; The system prediction parameters are analyzed based on a preset deep learning network to obtain target system prediction parameters corresponding to the multi-module charging system.

[0005] In some embodiments, performing module-level testing on at least one of the charging modules to obtain module test parameters corresponding to each of the charging modules includes: Perform module-level testing on at least one of the charging modules to obtain thermal resistance and thermal capacity corresponding to each of the charging modules.

[0006] In some embodiments, performing module-level testing on at least one of the charging modules to obtain thermal resistance and thermal capacity corresponding to each of the charging modules includes: Controlling the charging module to enter a running mode and testing a temperature change of the charging module; The thermal resistance is determined according to the ratio of the temperature change to a preset thermal power, and the thermal capacity is determined according to the ratio of the thermal power to the temperature change.

[0007] In some embodiments, determining the module prediction parameters of the charging module in the multi-module charging system according to the module test parameters includes: Determine module prediction parameters of the charging module in the multi-module charging system according to the module test parameters and the detected ambient temperature.

[0008] In some embodiments, determining the module prediction parameters of the charging module in the multi-module charging system according to the module test parameters and the detected ambient temperature includes: The module prediction parameters are determined according to the following formula: in, represents the module prediction parameters of the charging module at time t, represents thermal resistance, represents heat capacity, represents the ambient temperature, and Q represents the thermal power of the charging module.

[0009] In some embodiments, determining the system prediction parameters for performing system-level testing on the multi-module charging system according to the module prediction parameters and the network location parameters includes: Determining, according to the network location parameter, an adjacent charging module of the charging module in the multi-module charging system; The system prediction parameters are determined according to the following formula: in, represents the system prediction parameter, represents the module prediction parameter of the charging module at time t, represents the module prediction parameter of the adjacent charging module at time t, represents the thermal resistance between the charging module and the adjacent charging module, represents the thermal resistance of the charging module, Represents the thermal capacity of the charging module.

[0010] In some embodiments, determining, according to the network location parameter, an adjacent charging module of the charging module in the multi-module charging system includes: Determine a candidate charging module that is closest to the charging module as the adjacent charging module; After determining the system prediction parameters of the charging module and the adjacent charging modules, the system consisting of the charging module and the adjacent charging modules is determined as a candidate charging module.

[0011] In some embodiments, the system prediction parameters are analyzed based on a preset deep learning network to obtain target system prediction parameters corresponding to the multi-module charging system, including: Inputting the module test parameter, the network position parameter and the system prediction parameter into the deep learning network to obtain an offset corresponding to the system prediction parameter; Determining a correction result of the system prediction parameter according to the system prediction parameter and the offset; The target system prediction parameters corresponding to the multi-module charging system are determined according to the correction result.

[0012] In a second aspect, the present application further provides an intelligent testing device for a multi-module charging system, wherein the multi-module charging system includes at least one charging module, and the intelligent testing device for the multi-module charging system includes: A module testing module, used to perform a module-level test on at least one of the charging modules to obtain module testing parameters corresponding to each of the charging modules; A first prediction module, used to determine module prediction parameters of the charging module in the multi-module charging system according to the module test parameters; A position determination module, used to determine the network position parameters corresponding to the test parameters of each module according to the position of each charging module in the multi-module charging system; a second prediction module, configured to determine system prediction parameters for performing a system-level test on the multi-module charging system according to the module prediction parameters and the network location parameters; A parameter correction module is used to analyze the system prediction parameters based on a preset deep learning network to obtain target system prediction parameters corresponding to the multi-module charging system.

[0013] In a third aspect, the present application also provides a computer-readable storage medium, on which a computer program is stored, wherein when the computer program is executed by a processor, the intelligent testing method for the multi-module charging system as described above is implemented.

[0014] The present application provides a method, device and equipment for intelligent testing of a multi-module charging system. The present application obtains module test parameters corresponding to each charging module by performing module-level testing on at least one charging module; determines module prediction parameters of the charging module in the multi-module charging system according to the module test parameters; determines network position parameters corresponding to each module test parameter according to the position of each charging module in the multi-module charging system; determines system prediction parameters for system-level testing of the multi-module charging system according to the module prediction parameters and the network position parameters; and analyzes the system prediction parameters based on a preset deep learning network to obtain target system prediction parameters corresponding to the multi-module charging system. Since the system-level test is predicted according to the test results of the module-level test, the test process of the intelligent test of the multi-module charging system is simplified. 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 description of the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0016] Figure 1 A flowchart of an intelligent testing method for a multi-module charging system provided in one embodiment of the present application; Figure 2 A schematic block diagram of an intelligent testing device for a multi-module charging system provided in one embodiment of the present application; Figure 3 The present invention is a block diagram showing the structure of a computer device according to an embodiment of the present application. DETAILED DESCRIPTION

[0017] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0018] The flowcharts shown in the accompanying drawings are only examples and do not necessarily include all the contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps may be decomposed, combined or partially merged, so the actual execution order may change according to actual conditions.

[0019] The embodiments of the present application provide a method, device and equipment for intelligent testing of a multi-module charging system.

[0020] In conjunction with the accompanying drawings, some embodiments of the present application are described in detail below. In the absence of conflict, the following embodiments and features in the embodiments can be combined with each other.

[0021] Please refer to Figure 1 , Figure 1 A flow chart of a multi-module charging system intelligent testing method provided for an embodiment of the present application. The multi-module charging system intelligent testing method can be used in a terminal or a server to predict the test results of the system and the test according to the test results of the modules and the tests, and simplify the test process of the multi-module charging system intelligent test. Among them, the terminal can be an electronic device such as a mobile phone, a tablet computer, a laptop computer, a desktop computer, a personal digital assistant, and a wearable device; the server can be an independent server, a server cluster, or a cloud server that provides cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (Content Delivery Network, CDN), and basic cloud computing services such as big data and artificial intelligence platforms.

[0022] like Figure 1 As shown, the multi-module charging system intelligent testing method includes steps S101 to S105.

[0023] Step S101: Perform module-level testing on at least one of the charging modules to obtain module testing parameters corresponding to each of the charging modules.

[0024] For example, in order to ensure the normal operation of the multi-module charging system, it is necessary to test the multiple charging modules that make up the system separately, and it is also necessary to test the entire multi-module charging system, such as testing the input and output voltage, current, power and other electrical parameters, as well as insulation resistance, leakage current and other safety performance parameters. Since the operation of the charging module will cause the temperature to rise, in order to avoid the potential safety hazards caused by long-term operation, it is also necessary to monitor the temperature of the charging module and the multi-module charging system to ensure that the heat dissipation performance of the multi-module charging system meets the requirements. However, since temperature monitoring needs to be performed when the charging module or the multi-module charging system is running for a long time, it takes a lot of time to test the charging module and the multi-module charging system separately.

[0025] Exemplarily, in order to simplify the test process of a multi-module charging system, the intelligent testing method for a multi-module charging system provided in an embodiment of the present application predicts the overall test results of the multi-module charging system by obtaining module test parameters by testing each charging module separately.

[0026] In some implementations, performing a module-level test on at least one of the charging modules to obtain module test parameters corresponding to each of the charging modules includes: Perform module-level testing on at least one of the charging modules to obtain thermal resistance and thermal capacity corresponding to each of the charging modules.

[0027] Exemplarily, the electrical parameters and safety performance parameters (such as leakage current, etc.) of the multi-module charging system can be calculated based on the circuit connection method (such as series connection, parallel connection, etc.) of multiple charging modules. However, the temperature changes of the multi-module charging system during operation are more complicated. The heat transfer and distribution between the charging modules will affect the overall heat dissipation effect. Therefore, it is necessary to establish a heat transfer model for prediction through the intelligent testing method for the multi-module charging system provided in the embodiment of the present application.

[0028] Among them, thermal resistance and thermal capacitance are used to describe the heat transfer capacity of each node in a multi-module charging system, that is, the heat transfer capacity of each charging module.

[0029] In some embodiments, performing module-level testing on at least one of the charging modules to obtain thermal resistance and thermal capacity corresponding to each of the charging modules includes: Controlling the charging module to enter a running mode and testing a temperature change of the charging module; The thermal resistance is determined according to the ratio of the temperature change to a preset thermal power, and the thermal capacity is determined according to the ratio of the thermal power to the temperature change.

[0030] For example, thermal resistance is a physical quantity that measures the ability of the charging module to block heat transfer, and thermal capacity is a physical quantity that measures the temperature change when the charging module absorbs or releases heat. The greater the thermal resistance, the greater the temperature difference at the same thermal power; the greater the thermal capacity, the smaller the rate of temperature change at the same thermal power. Therefore, the thermal resistance and thermal capacity can be determined based on the temperature change and thermal power during the test.

[0031] Exemplarily, the thermal power may be obtained by testing one or more charging modules, and the average thermal power of multiple charging modules may be pre-tested as a basis for subsequent calculation of thermal resistance and thermal capacity.

[0032] Step S102: Determine module prediction parameters of the charging module in the multi-module charging system according to the module test parameters.

[0033] Exemplarily, the module prediction parameter represents the prediction result of the parameter change of each charging module during the system level test.

[0034] In some embodiments, determining the module prediction parameters of the charging module in the multi-module charging system according to the module test parameters includes: Determine module prediction parameters of the charging module in the multi-module charging system according to the module test parameters and the detected ambient temperature.

[0035] For example, the ambient temperature during the test will affect the heat dissipation capacity of the charging system, so the module prediction parameters are related to the ambient temperature. The ambient temperature can be the actual temperature detected during the test, or it can be determined based on the provisions of the ambient temperature during the test in the relevant standards.

[0036] In some embodiments, determining the module prediction parameter of the charging module in the multi-module charging system according to the module test parameter and the detected ambient temperature includes: The module prediction parameters are determined according to the following formula: in, represents the module prediction parameters of the charging module at time t, represents thermal resistance, represents heat capacity, represents the ambient temperature, and Q represents the thermal power of the charging module.

[0037] Exemplarily, the module prediction parameters of each charging module are determined according to the above formula: , where the module prediction parameter can be the module prediction temperature, and due to the heat accumulation effect during the operation of the charging module, the module prediction temperature is a parameter related to the time t.

[0038] Step S103: determining the network location parameters corresponding to the test parameters of each module according to the location of each charging module in the multi-module charging system.

[0039] Exemplarily, the network position parameter is used to describe the position of each charging module in the multi-module charging system. Since the heat dissipation performance of the system is related to the arrangement of each charging module in the system, the system prediction parameter is also related to the network position parameter.

[0040] Step S104: determining system prediction parameters for performing a system-level test on the multi-module charging system according to the module prediction parameters and the network location parameters.

[0041] Exemplarily, the system prediction parameters for the system-level test of the multi-module charging system are predicted based on the module prediction parameters obtained in step S102 and the network location parameters obtained in step S103.

[0042] In some embodiments, determining the system prediction parameters for performing a system-level test on the multi-module charging system according to the module prediction parameters and the network location parameters includes: Determining, according to the network location parameter, an adjacent charging module of the charging module in the multi-module charging system; The system prediction parameters are determined according to the following formula: in, represents the system prediction parameter, represents the module prediction parameter of the charging module at time t, represents the module prediction parameter of the adjacent charging module at time t, represents the thermal resistance between the charging module and the adjacent charging module, represents the thermal resistance of the charging module, Represents the thermal capacity of the charging module.

[0043] For example, the above formula can be used to calculate the system prediction parameters of the system composed of the charging module and the corresponding adjacent charging modules. By traversing each charging module and its corresponding adjacent charging modules through the above method, the system prediction parameters of the entire multi-module charging system can be obtained. Can be based on and It is calculated by the relationship between the charging module and the adjacent charging module. For example, if the charging module and the adjacent charging module are connected in series, then = .

[0044] In some embodiments, determining, according to the network location parameter, an adjacent charging module of the charging module in the multi-module charging system includes: Determine a candidate charging module that is closest to the charging module as the adjacent charging module; After determining the system prediction parameters of the charging module and the adjacent charging modules, the system consisting of the charging module and the adjacent charging modules is determined as a candidate charging module.

[0045] Exemplarily, the system prediction parameters of the system consisting of a charging module and the nearest candidate charging module can be calculated first, and then the system consisting of the two can be determined as a candidate charging module, until every charging module in every multi-module charging system is included in the system, thereby obtaining the system prediction parameters of the entire multi-module charging system.

[0046] Step S105: Analyze the system prediction parameters based on a preset deep learning network to obtain target system prediction parameters corresponding to the multi-module charging system.

[0047] For example, since the system prediction parameters are affected by many factors, the system prediction parameters obtained through steps S101 to S104 may have certain deviations; if the system prediction parameters are predicted directly through a deep neural network, there may also be large errors. Therefore, the system prediction parameters can be corrected through a deep neural network to obtain more accurate target system prediction parameters.

[0048] In some embodiments, the system prediction parameters are analyzed based on a preset deep learning network to obtain target system prediction parameters corresponding to the multi-module charging system, including: Inputting the module test parameter, the network position parameter and the system prediction parameter into the deep learning network to obtain an offset corresponding to the system prediction parameter; Determining a correction result of the system prediction parameter according to the system prediction parameter and the offset; The target system prediction parameters corresponding to the multi-module charging system are determined according to the correction result.

[0049] Exemplarily, the deep neural network can be pre-trained with module test parameter samples, network position parameter samples, system prediction parameter samples, and offset samples, so that the deep neural network can output the offset of the system prediction parameter based on the input module test parameters, network position parameters, and system prediction parameters.

[0050] The intelligent testing method for a multi-module charging system provided in the above embodiment performs a module-level test on at least one of the charging modules to obtain the module test parameters corresponding to each of the charging modules; determines the module prediction parameters of the charging module in the multi-module charging system according to the module test parameters; determines the network position parameters corresponding to each of the module test parameters according to the position of each of the charging modules in the multi-module charging system; determines the system prediction parameters for system-level testing of the multi-module charging system according to the module prediction parameters and the network position parameters; and analyzes the system prediction parameters based on a preset deep learning network to obtain the target system prediction parameters corresponding to the multi-module charging system. The test results of the system and the test can be predicted according to the test results of the modules and the tests, simplifying the test process of the intelligent testing of the multi-module charging system.

[0051] See also Figure 2 , Figure 2It is a schematic diagram of an intelligent testing device for a multi-module charging system provided in one embodiment of the present application. The multi-module charging system includes at least one charging module. The intelligent testing device for the multi-module charging system can be configured in a server or a terminal to execute the aforementioned intelligent testing method for the multi-module charging system.

[0052] like Figure 2 As shown, the multi-module charging system intelligent testing device includes: a module testing module 110 , a first prediction module 120 , a position determination module 130 , a second prediction module 140 , and a parameter correction module 150 .

[0053] A module testing module 110, configured to perform a module-level test on at least one of the charging modules to obtain module testing parameters corresponding to each of the charging modules; A first prediction module 120, configured to determine module prediction parameters of the charging module in the multi-module charging system according to the module test parameters; A location determination module 130, configured to determine a network location parameter corresponding to each module test parameter according to a location of each charging module in the multi-module charging system; A second prediction module 140, configured to determine a system prediction parameter for performing a system-level test on the multi-module charging system according to the module prediction parameter and the network location parameter; The parameter correction module 150 is used to analyze the system prediction parameters based on a preset deep learning network to obtain the target system prediction parameters corresponding to the multi-module charging system.

[0054] In some embodiments, the module testing module 110, in the process of performing module-level testing on at least one of the charging modules to obtain module testing parameters corresponding to each of the charging modules, is used to: Perform module-level testing on at least one of the charging modules to obtain thermal resistance and thermal capacity corresponding to each of the charging modules.

[0055] In some embodiments, the module testing module 110, in the process of performing module-level testing on at least one of the charging modules to obtain the thermal resistance and thermal capacity corresponding to each of the charging modules, is used to: Controlling the charging module to enter a running mode and testing a temperature change of the charging module; The thermal resistance is determined according to the ratio of the temperature change to a preset thermal power, and the thermal capacity is determined according to the ratio of the thermal power to the temperature change.

[0056] In some embodiments, the first prediction module 120, in the process of determining the module prediction parameter of the charging module in the multi-module charging system according to the module test parameter, is used to: Determine module prediction parameters of the charging module in the multi-module charging system according to the module test parameters and the detected ambient temperature.

[0057] In some embodiments, the first prediction module 120, in the process of determining the module prediction parameter of the charging module in the multi-module charging system according to the module test parameter and the detected ambient temperature, is used to: The module prediction parameters are determined according to the following formula: in, represents the module prediction parameters of the charging module at time t, represents thermal resistance, represents heat capacity, represents the ambient temperature, and Q represents the thermal power of the charging module.

[0058] In some embodiments, the second prediction module 140, in the process of determining the system prediction parameters for performing a system-level test on the multi-module charging system according to the module prediction parameters and the network location parameters, is used to: Determining, according to the network location parameter, an adjacent charging module of the charging module in the multi-module charging system; The system prediction parameters are determined according to the following formula: in, represents the system prediction parameters of the charging module, represents the module prediction parameter of the charging module at time t, represents the module prediction parameter of the adjacent charging module at time t, represents the thermal resistance between the charging module and the adjacent charging module, represents the thermal resistance of the charging module, Represents the thermal capacity of the charging module.

[0059] In some embodiments, the second prediction module 140, in the process of determining the adjacent charging modules of the charging module in the multi-module charging system according to the network location parameter, is used to: Determine a candidate charging module that is closest to the charging module as the adjacent charging module; After determining the system prediction parameters of the charging module and the adjacent charging modules, the system consisting of the charging module and the adjacent charging modules is determined as a candidate charging module.

[0060] In some embodiments, the parameter correction module 150 is used to analyze the system prediction parameters based on a preset deep learning network to obtain the target system prediction parameters corresponding to the multi-module charging system, and is used to: Inputting the module test parameter, the network position parameter and the system prediction parameter into the deep learning network to obtain an offset corresponding to the system prediction parameter; Determining a correction result of the system prediction parameter according to the system prediction parameter and the offset; The target system prediction parameters corresponding to the multi-module charging system are determined according to the correction result.

[0061] It should be noted that those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described devices and modules and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0062] The method and apparatus of the present application can be used in many general or special computing system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronic devices, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, etc. The present application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present application can also be practiced in distributed computing environments, in which tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0063] Exemplarily, the above method and apparatus may be implemented in the form of a computer program. The computer program may be implemented in Figure 3 Runs on the computer device shown.

[0064] See also Figure 3 , Figure 3 A schematic block diagram of the structure of a computer device provided in an embodiment of the present application. The computer device may be a server or a terminal.

[0065] like Figure 3As shown, the computer device includes a processor, a memory, and a network interface connected via a system bus, wherein the memory may include a storage medium and an internal memory.

[0066] The storage medium can store an operating system and a computer program. The computer program includes program instructions, and when the program instructions are executed, the processor can execute any one of the multi-module charging system intelligent testing methods.

[0067] The processor is used to provide computing and control capabilities and support the operation of the entire computer equipment.

[0068] The internal memory provides an environment for the operation of the computer program in the storage medium. When the computer program is executed by the processor, the processor can execute any intelligent testing method for the multi-module charging system.

[0069] The network interface is used for network communication, such as sending assigned tasks, etc. Those skilled in the art will understand that Figure 3 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0070] It should be understood that the processor may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among them, the general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0071] In one embodiment, the processor is used to run a computer program stored in the memory to implement the following steps: Performing module-level testing on at least one of the charging modules to obtain module testing parameters corresponding to each of the charging modules; Determining module prediction parameters of the charging module in the multi-module charging system according to the module test parameters; Determine the network location parameter corresponding to each module test parameter according to the position of each charging module in the multi-module charging system; Determining system prediction parameters for performing system-level testing on the multi-module charging system according to the module prediction parameters and the network location parameters; The system prediction parameters are analyzed based on a preset deep learning network to obtain target system prediction parameters corresponding to the multi-module charging system.

[0072] In some embodiments, the processor is used to implement the process of performing module-level testing on at least one of the charging modules to obtain module test parameters corresponding to each of the charging modules, and is used to implement: Perform module-level testing on at least one of the charging modules to obtain thermal resistance and thermal capacity corresponding to each of the charging modules.

[0073] In some embodiments, the processor is used to implement the process of performing module-level testing on at least one of the charging modules to obtain the thermal resistance and thermal capacity corresponding to each of the charging modules, and to implement: Controlling the charging module to enter a running mode and testing a temperature change of the charging module; The thermal resistance is determined according to the ratio of the temperature change to a preset thermal power, and the thermal capacity is determined according to the ratio of the thermal power to the temperature change.

[0074] In some embodiments, the processor is used to implement a process of determining a module prediction parameter of the charging module in the multi-module charging system, and is used to implement: Determine module prediction parameters of the charging module in the multi-module charging system according to the module test parameters and the detected ambient temperature.

[0075] In some embodiments, the processor is used to implement the process of determining the module prediction parameters of the charging module in the multi-module charging system according to the module test parameters and the detected ambient temperature, and is used to implement: The module prediction parameters are determined according to the following formula: in, represents the module prediction parameters of the charging module at time t, represents thermal resistance, represents heat capacity, represents the ambient temperature, and Q represents the thermal power of the charging module.

[0076] In some embodiments, the processor is used to implement the process of determining the system prediction parameters for performing system-level testing on the multi-module charging system according to the module prediction parameters and the network location parameters, and is used to implement: Determining, according to the network location parameter, an adjacent charging module of the charging module in the multi-module charging system; The system prediction parameters are determined according to the following formula: in, represents the system prediction parameters of the charging module, represents the module prediction parameter of the charging module at time t, represents the module prediction parameter of the adjacent charging module at time t, represents the thermal resistance between the charging module and the adjacent charging module, represents the thermal resistance of the charging module, Represents the thermal capacity of the charging module.

[0077] In some embodiments, the processor is used to implement the process of determining, according to the network location parameter, the adjacent charging modules of the charging module in the multi-module charging system, to implement: Determine a candidate charging module that is closest to the charging module as the adjacent charging module; After determining the system prediction parameters of the charging module and the adjacent charging modules, the system consisting of the charging module and the adjacent charging modules is determined as a candidate charging module.

[0078] In some embodiments, the processor is used to implement the process of analyzing the system prediction parameters based on the preset deep learning network to obtain the target system prediction parameters corresponding to the multi-module charging system, and to implement: Inputting the module test parameter, the network position parameter and the system prediction parameter into the deep learning network to obtain an offset corresponding to the system prediction parameter; Determining a correction result of the system prediction parameter according to the system prediction parameter and the offset; The target system prediction parameters corresponding to the multi-module charging system are determined according to the correction result.

[0079] It should be noted that, technical personnel in the relevant field can clearly understand that, for the convenience and simplicity of description, the specific working process of the above-described intelligent testing method for a multi-module charging system can refer to the corresponding process in the aforementioned embodiment of the intelligent testing method for a multi-module charging system, and will not be repeated here.

[0080] It should be understood that the terms used in this application specification are only for the purpose of describing specific embodiments and are not intended to limit the application. As used in this application specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0081] It should also be understood that the term "and / or" used in the specification of this application and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, including these combinations. It should be noted that, in this article, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or system including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or system. In the absence of further restrictions, an element defined by the sentence "including a..." does not exclude the presence of other identical elements in the process, method, article or system including the element.

[0082] The serial numbers of the embodiments of the present application are for description only and do not represent the advantages and disadvantages of the embodiments. The above description is only a specific implementation mode of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed in the present application, and these modifications or replacements should be included in the protection scope of the present application. Therefore, the protection scope of the present application shall be based on the protection scope of the claims.

Claims

1. A multi-module charging system intelligent testing method, characterized in that: The multi-module charging system includes at least one charging module, and the method includes: Performing module-level testing on at least one of the charging modules to obtain module testing parameters corresponding to each of the charging modules; Determining module prediction parameters of the charging module in the multi-module charging system according to the module test parameters; Determine the network location parameter corresponding to each module test parameter according to the position of each charging module in the multi-module charging system; Determining system prediction parameters for performing system-level testing on the multi-module charging system according to the module prediction parameters and the network location parameters; The system prediction parameters are analyzed based on a preset deep learning network to obtain target system prediction parameters corresponding to the multi-module charging system.

2. The intelligent testing method for a multi-module charging system according to claim 1, characterized in that: The performing module-level testing on at least one of the charging modules to obtain module test parameters corresponding to each of the charging modules includes: Perform module-level testing on at least one of the charging modules to obtain thermal resistance and thermal capacity corresponding to each of the charging modules.

3. The intelligent testing method for a multi-module charging system according to claim 2, characterized in that: The performing module-level testing on at least one of the charging modules to obtain the thermal resistance and thermal capacity corresponding to each of the charging modules includes: Controlling the charging module to enter a running mode and testing a temperature change of the charging module; The thermal resistance is determined according to the ratio of the temperature change to a preset thermal power, and the thermal capacity is determined according to the ratio of the thermal power to the temperature change.

4. The intelligent testing method for a multi-module charging system according to claim 1, characterized in that: The step of determining the module prediction parameters of the charging module in the multi-module charging system according to the module test parameters includes: Determine module prediction parameters of the charging module in the multi-module charging system according to the module test parameters and the detected ambient temperature.

5. The intelligent testing method for a multi-module charging system according to claim 4, characterized in that: The step of determining the module prediction parameters of the charging module in the multi-module charging system according to the module test parameters and the detected ambient temperature includes: The module prediction parameters are determined according to the following formula: in, represents the module prediction parameters of the charging module at time t, represents thermal resistance, represents heat capacity, represents the ambient temperature, and Q represents the thermal power of the charging module.

6. The intelligent testing method for a multi-module charging system according to claim 1, characterized in that: The determining of the system prediction parameters for performing a system-level test on the multi-module charging system according to the module prediction parameters and the network location parameters includes: Determining, according to the network location parameter, an adjacent charging module of the charging module in the multi-module charging system; The system prediction parameters are determined according to the following formula: in, represents the system prediction parameter, represents the module prediction parameter of the charging module at time t, represents the module prediction parameter of the adjacent charging module at time t, represents the thermal resistance between the charging module and the adjacent charging module, represents the thermal resistance of the charging module, Represents the thermal capacity of the charging module.

7. The intelligent testing method for a multi-module charging system according to claim 6, characterized in that: The step of determining, according to the network location parameter, an adjacent charging module of the charging module in the multi-module charging system comprises: Determine a candidate charging module that is closest to the charging module as the adjacent charging module; After determining the system prediction parameters of the charging module and the adjacent charging modules, the system consisting of the charging module and the adjacent charging modules is determined as a candidate charging module.

8. The intelligent testing method for a multi-module charging system according to any one of claims 1 to 7, characterized in that: The system prediction parameters are analyzed based on a preset deep learning network to obtain target system prediction parameters corresponding to the multi-module charging system, including: Inputting the module test parameter, the network position parameter and the system prediction parameter into the deep learning network to obtain an offset corresponding to the system prediction parameter; Determining a correction result of the system prediction parameter according to the system prediction parameter and the offset; The target system prediction parameters corresponding to the multi-module charging system are determined according to the correction result.

9. An intelligent testing device for a multi-module charging system, characterized in that: The multi-module charging system includes at least one charging module, and the multi-module charging system intelligent testing device includes: A module testing module, used to perform a module-level test on at least one of the charging modules to obtain module testing parameters corresponding to each of the charging modules; A first prediction module, used to determine module prediction parameters of the charging module in the multi-module charging system according to the module test parameters; A position determination module, used to determine the network position parameters corresponding to the test parameters of each module according to the position of each charging module in the multi-module charging system; a second prediction module, configured to determine system prediction parameters for performing a system-level test on the multi-module charging system according to the module prediction parameters and the network location parameters; A parameter correction module is used to analyze the system prediction parameters based on a preset deep learning network to obtain target system prediction parameters corresponding to the multi-module charging system.

10. A computer device, characterized in that: The computer device includes a processor, a memory, and a computer program stored in the memory and executable by the processor, wherein when the computer program is executed by the processor, the steps of the intelligent testing method for a multi-module charging system as described in any one of claims 1 to 8 are implemented.

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