Intelligent Testing Method, Device and Equipment for Multi-module Charging System

Abstract

The present application 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 includes at least one charging module. The method includes: performing module-level testing on at least one of the charging modules to obtain module test 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; determining network position parameters corresponding to each of the module test parameters according to the positions of the charging modules 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 parameters based on a preset deep learning network to obtain target system prediction parameters 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 requirements of different vehicles to achieve power sharing and flexible charging, so it has received more and more favor from users. However, due to the complexity of the multi-module charging system, when testing the multi-module charging system, it is necessary to not only perform module-level testing on each module, but also perform system-level testing on the overall multi-module charging system, 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 the present application is to provide an intelligent testing method, device and equipment for a multi-module charging system, aiming to simplify the testing process of the intelligent testing of the multi-module charging system.

[0004] In a first aspect, the present application provides an intelligent testing method for a multi-module charging system, and the intelligent testing method for the multi-module charging system includes the following steps:

[0005] Perform module-level testing on at least one of the charging modules to obtain module test parameters corresponding to each of the charging modules;

[0006] Determine module prediction parameters of the charging module in the multi-module charging system according to the module test parameters;

[0007] Determine network position parameters corresponding to each of the module test parameters according to the positions of the charging modules in the multi-module charging system;

[0008] Determine 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;

[0009] 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.

[0010] In some embodiments, 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:

[0011] Perform module-level testing on at least one of the charging modules to obtain the thermal resistance and heat capacity corresponding to each of the charging modules.

[0012] In some embodiments, the module-level testing of at least one of the charging modules to obtain the thermal resistance and heat capacity corresponding to each of the charging modules includes:

[0013] Controlling the charging module to enter the operating mode and testing the temperature change of the charging module;

[0014] Determining the thermal resistance based on the ratio of the temperature change to the preset thermal power, and determining the heat capacity based on the ratio of the thermal power to the temperature change.

[0015] In some embodiments, the determining of the module prediction parameters of the charging module in the multi-module charging system according to the module test parameters includes:

[0016] 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.

[0017] In some embodiments, the determining of 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:

[0018] Determining the module prediction parameters according to the following formula:

[0019]

[0020] Wherein, represents the module prediction parameter of the charging module at time t, represents the thermal resistance, represents the heat capacity, represents the ambient temperature, and Q represents the thermal power of the charging module.

[0021] In some embodiments, the determining of the system prediction parameters for system-level testing of the multi-module charging system according to the module prediction parameters and the network location parameters includes:

[0022] Determining the adjacent charging modules of the charging module in the multi-module charging system according to the network location parameters;

[0023] Determining the system prediction parameters according to the following formula:

[0024]

[0025] Wherein, 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 heat capacity of the charging module

[0026] In some embodiments, determining the adjacent charging module of the charging module in the multi-module charging system according to the network location parameter includes:

[0027] determining the candidate charging module closest to the charging module as the adjacent charging module

[0028] After determining the system prediction parameters of the charging module and the adjacent charging module, the system composed of the charging module and the adjacent charging module is determined as a candidate charging module

[0029] In some embodiments, analyzing 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 includes:

[0030] inputting the module test parameters, the network location parameters, and the system prediction parameters into the deep learning network to obtain the offset corresponding to the system prediction parameters

[0031] determining the correction result of the system prediction parameters according to the system prediction parameters and the offset

[0032] determining the target system prediction parameters corresponding to the multi-module charging system according to the correction result

[0033] In a second aspect, the present application further provides an intelligent testing device for a multi-module charging system. The multi-module charging system includes at least one charging module. The intelligent testing device for the multi-module charging system includes:

[0034] a module testing module, configured to perform module-level testing on at least one of the charging modules to obtain module test parameters corresponding to each of the charging modules

[0035] a first prediction module, configured to determine the module prediction parameters of the charging module in the multi-module charging system according to the module test parameters

[0036] a position determining module, configured to determine the network location parameters corresponding to the module test parameters according to the positions of the charging modules in the multi-module charging system

[0037] a second prediction module, configured to determine 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

[0038] A parameter correction module for analyzing 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.

[0039] In a third aspect, the present application also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the intelligent testing method for the multi-module charging system as described above is implemented.

[0040] The present application provides an intelligent testing method, device and equipment for a multi-module charging system. The present application performs module-level testing on at least one of the charging modules to obtain the module testing parameters corresponding to each charging module; determines the module prediction parameters of the charging module in the multi-module charging system according to the module testing parameters; determines the network position parameters corresponding to each module testing parameter according to the positions 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; 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. Since the system-level testing is predicted based on the test results of the module-level testing, the testing process of the intelligent testing of the multi-module charging system is simplified. Description of the Drawings

[0041] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for the description of the embodiments will be briefly introduced below. Obviously, the drawings in the following description are some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0042] Figure 1 It is a schematic flowchart of an intelligent testing method for a multi-module charging system provided by an embodiment of the present application;

[0043] Figure 2 It is a schematic block diagram of an intelligent testing device for a multi-module charging system provided by an embodiment of the present application;

[0044] Figure 3 It is a schematic block diagram of the structure of a computer device related to an embodiment of the present application. Detailed Embodiments

[0045] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

[0046] The flowchart shown in the accompanying drawings is only an example, and does not necessarily include all contents and operations / steps, nor does it necessarily need to be executed in the described order. For example, some operations / steps can be decomposed, combined, or partially merged, so the actual execution order may change according to the actual situation.

[0047] The embodiments of the present application provide an intelligent testing method, device, and equipment for a multi-module charging system.

[0048] Next, some embodiments of the present application will be described in detail in conjunction with the accompanying drawings. Without conflict, the following embodiments and the features in the embodiments can be combined with each other.

[0049] Please refer to Figure 1 , Figure 1 which is a schematic flowchart of an intelligent testing method for a multi-module charging system provided for the embodiments of the present application. This intelligent testing method for a multi-module charging system can be used in a terminal or a server to predict the test results of the system and the test based on the test results of the modules and the test, and simplify the test process of the intelligent testing of the multi-module charging system. Among them, the terminal can be an electronic device such as a mobile phone, a tablet computer, a notebook 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 basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, Content Delivery Network (CDN), and big data and artificial intelligence platforms.

[0050] As Figure 1 shown, this intelligent testing method for a multi-module charging system includes steps S101 to S105.

[0051] Step S101: Perform module-level testing on at least one of the charging modules to obtain module test parameters corresponding to each of the charging modules.

[0052] Exemplarily, to ensure the normal operation of a multi-module charging system, it is necessary to test each of 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 electrical parameters such as input and output voltage, current, and power, as well as safety performance parameters such as insulation resistance and leakage current. Since the operation of the charging module will cause the temperature to rise, in order to avoid 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 carried out when the charging module or the multi-module charging system is running for a long time, therefore, testing the charging module and the multi-module charging system separately takes a lot of time.

[0053] Exemplarily, to simplify the test process of the multi-module charging system, the intelligent test method for the multi-module charging system provided by the embodiments of the present application predicts the overall test result of the multi-module charging system based on the module test parameters obtained by testing each charging module separately.

[0054] In some embodiments, the performing module-level tests on at least one of the charging modules to obtain module test parameters corresponding to each of the charging modules includes:

[0055] Performing module-level tests on at least one of the charging modules to obtain the thermal resistance and heat capacity corresponding to each of the charging modules.

[0056] Exemplarily, the electrical parameters and safety performance parameters (such as leakage current, etc.) of the multi-module charging system can be calculated according to the circuit connection mode (such as series, parallel, etc.) of multiple charging modules, while the temperature change during the operation of the multi-module charging system is relatively complex. 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 through the intelligent test method for the multi-module charging system provided by the embodiments of the present application for prediction.

[0057] Among them, the thermal resistance and heat capacity are used to describe the heat transfer ability of each node in the multi-module charging system, that is, the heat transfer ability of each charging module.

[0058] In some embodiments, the performing module-level tests on at least one of the charging modules to obtain the thermal resistance and heat capacity corresponding to each of the charging modules includes:

[0059] Controlling the charging module to enter the operating mode and testing the temperature change amount of the charging module;

[0060] Determining the thermal resistance according to the ratio of the temperature change amount to the preset thermal power, and determining the heat capacity according to the ratio of the thermal power to the temperature change amount.

[0061] Exemplarily, thermal resistance is a physical quantity that measures the ability of a charging module to impede heat transfer, and heat capacity is a physical quantity that measures the temperature change when a charging module absorbs or releases heat. Among them, the greater the thermal resistance, the greater the temperature difference under the same thermal power; the greater the heat capacity, the smaller the rate of temperature change under the same thermal power. Therefore, the magnitudes of thermal resistance and heat capacity can be determined based on the temperature change amount and thermal power during the testing process.

[0062] Exemplarily, the thermal power can be obtained by testing one or more charging modules. The average thermal power of multiple charging modules can be pre-tested as the basis for subsequent calculation of thermal resistance and heat capacity.

[0063] Step S102: Determine the module prediction parameters of the charging module in the multi-module charging system according to the module test parameters.

[0064] Exemplarily, the module prediction parameters represent the prediction results of the parameter change conditions of each charging module during the system-level test.

[0065] In some embodiments, the determining the module prediction parameters of the charging module in the multi-module charging system according to the module test parameters includes:

[0066] Determine 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.

[0067] Exemplarily, the ambient temperature during testing affects the heat dissipation ability of the charging system. Therefore, the module prediction parameters are related to the ambient temperature. The ambient temperature can be the temperature actually detected during the testing process, or can be determined according to the regulations on the ambient temperature during testing in relevant standards.

[0068] In some embodiments, the 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:

[0069] Determine the module prediction parameters according to the following formula:

[0070]

[0071] Wherein, represents the module prediction parameter of the charging module at time t, represents the thermal resistance, represents the heat capacity, represents the ambient temperature, and Q represents the thermal power of the charging module.

[0072] 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 time t.

[0073] Step S103: Determine the network position parameters corresponding to the module test parameters according to the positions of the charging modules in the multi-module charging system.

[0074] Exemplarily, the network position parameter is used to describe the positions of the respective charging modules in the multi-module charging system. Since the heat dissipation performance of the system is related to the arrangement of the respective charging modules in the system, the system prediction parameter is also related to the network position parameter.

[0075] Step S104: Determine the system prediction parameter for performing system-level testing on the multi-module charging system according to the module prediction parameter and the network position parameter.

[0076] Exemplarily, predict the system prediction parameter for performing system-level testing on the multi-module charging system according to the module prediction parameter obtained in step S102 and the network position parameter obtained in step S103.

[0077] In some embodiments, the determining the system prediction parameter for performing system-level testing on the multi-module charging system according to the module prediction parameter and the network position parameter includes:

[0078] Determine the adjacent charging modules of the charging module in the multi-module charging system according to the network position parameter;

[0079] Determine the system prediction parameter according to the following formula:

[0080]

[0081] where, 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 heat capacity of the charging module.

[0082] Exemplarily, the system prediction parameter of the system composed of the charging module and the corresponding adjacent charging module can be calculated through the above formula. By traversing each charging module and its corresponding adjacent charging module through the above method, the system prediction parameter of the entire multi-module charging system can be obtained. Where, can be based on and calculated based on the relationship between the charging module and adjacent charging modules. For example, if the charging module and an adjacent charging module are connected in series, then = .

[0083] In some embodiments, determining the adjacent charging module of the charging module in the multi-module charging system according to the network location parameter includes:

[0084] determining the candidate charging module closest to the charging module as the adjacent charging module;

[0085] After determining the system prediction parameters of the charging module and the adjacent charging module, the system composed of the charging module and the adjacent charging module is determined as a candidate charging module.

[0086] Exemplarily, the system prediction parameters of the system composed of the charging module and the candidate charging module closest to it can be calculated first, and then the system composed of the two is determined as a candidate charging module until each charging module in each multi-module charging system is included in the system to obtain the system prediction parameters of the entire multi-module charging system.

[0087] Step S105: 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.

[0088] Exemplarily, since the system prediction parameters are affected by various factors, there may be a certain deviation in the system prediction parameters obtained through steps S101 - S104; there may also be a large error if directly predicting the system prediction parameters through a deep neural network. Therefore, the system prediction parameters can be corrected through a deep neural network to obtain a relatively accurate target system prediction parameter.

[0089] In some embodiments, analyzing 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 includes:

[0090] inputting the module test parameters, the network location parameters, and the system prediction parameters into the deep learning network to obtain the offset corresponding to the system prediction parameters;

[0091] determining the correction result of the system prediction parameters according to the system prediction parameters and the offset;

[0092] determining the target system prediction parameters corresponding to the multi-module charging system according to the correction result.

[0093] Exemplarily, the deep neural network can be pre-trained with module test parameter samples, network location 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 location parameters, and system prediction parameters.

[0094] The intelligent testing method for the multi-module charging system provided in the above embodiment performs module-level testing on at least one of the charging modules to obtain the module test parameters corresponding to each charging module; determines the module prediction parameters of the charging module in the multi-module charging system according to the module test parameters; determines the network location parameters corresponding to each module test parameter according to the positions of the charging modules in the multi-module charging system; determines 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 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. It can predict the test results of the system and testing based on the test results of the module and testing, simplifying the test process of the intelligent testing of the multi-module charging system.

[0095] Please refer to Figure 2 , Figure 2 FIG. is a schematic diagram of an intelligent testing device for a multi-module charging system provided in an embodiment of the present application. The multi-module charging system includes at least one charging module. This intelligent testing device for the multi-module charging system can be configured in a server or a terminal and is used to execute the foregoing intelligent testing method for the multi-module charging system.

[0096] As Figure 2 shown, this intelligent testing device for the multi-module charging system 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.

[0097] The module testing module 110 is used to perform module-level testing on at least one of the charging modules to obtain the module test parameters corresponding to each charging module;

[0098] The first prediction module 120 is used to determine the module prediction parameters of the charging module in the multi-module charging system according to the module test parameters;

[0099] The position determination module 130 is used to determine the network location parameters corresponding to each module test parameter according to the positions of the charging modules in the multi-module charging system;

[0100] The second prediction module 140 is configured to determine 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;

[0101] The parameter correction module 150 is configured 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.

[0102] In some embodiments, when the module testing module 110 is used to perform module-level testing on at least one of the charging modules to obtain module testing parameters corresponding to each of the charging modules, it is configured to:

[0103] Perform module-level testing on at least one of the charging modules to obtain the thermal resistance and heat capacity corresponding to each of the charging modules.

[0104] In some embodiments, when the module testing module 110 is used to perform module-level testing on at least one of the charging modules to obtain the thermal resistance and heat capacity corresponding to each of the charging modules, it is configured to:

[0105] Control the charging module to enter an operating mode and test the temperature change of the charging module;

[0106] Determine the thermal resistance according to the ratio of the temperature change to a preset thermal power, and determine the heat capacity according to the ratio of the thermal power to the temperature change.

[0107] In some embodiments, when the first prediction module 120 is used to determine the module prediction parameters of the charging module in the multi-module charging system according to the module testing parameters, it is configured to:

[0108] Determine the module prediction parameters of the charging module in the multi-module charging system according to the module testing parameters and the detected ambient temperature.

[0109] In some embodiments, when the first prediction module 120 is used to determine the module prediction parameters of the charging module in the multi-module charging system according to the module testing parameters and the detected ambient temperature, it is configured to:

[0110] Determine the module prediction parameters according to the following formula:

[0111]

[0112] Wherein, represents the module prediction parameter of the charging module at time t, represents the thermal resistance, represents the heat capacity, represents the ambient temperature, and Q represents the thermal power of the charging module.

[0113] In some embodiments, in the process of using the second prediction module 140 to determine the system prediction parameters for the system-level test of the multi-module charging system according to the module prediction parameters and the network location parameters, it is used to:

[0114] Determine the adjacent charging modules of the charging module in the multi-module charging system according to the network location parameters;

[0115] Determine the system prediction parameters according to the following formula:

[0116]

[0117] Where, represents the system prediction parameter 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 heat capacity of the charging module.

[0118] In some embodiments, in the process of using the second prediction module 140 to determine the adjacent charging modules of the charging module in the multi-module charging system according to the network location parameters, it is used to:

[0119] Determine the adjacent charging module as the candidate charging module closest to the charging module;

[0120] After determining the system prediction parameters of the charging module and the adjacent charging module, determine the system composed of the charging module and the adjacent charging module as the candidate charging module.

[0121] In some embodiments, in the process of using the parameter correction module 150 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, it is used to:

[0122] Input the module test parameters, the network location parameters, and the system prediction parameters into the deep learning network to obtain the offset corresponding to the system prediction parameters;

[0123] Determine the correction result of the system prediction parameters according to the system prediction parameters and the offset;

[0124] Determine the target system prediction parameters corresponding to the multi-module charging system according to the correction result.

[0125] It should be noted that those skilled in the art can clearly understand that for the convenience and conciseness of description, the specific working processes of the above-described device and each module and unit can refer to the corresponding processes in the foregoing method embodiments, and will not be described herein again.

[0126] The methods and devices of the present application can be used in many general-purpose or special-purpose computing system environments or configurations. For example: personal computers, server computers, handheld devices or portable devices, tablet devices, multi-processor systems, microprocessor-based systems, set-top boxes, programmable consumer electronic devices, network PCs, small computers, large computers, distributed computing environments including any of the above systems or devices, and so on. 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 a distributed computing environment where 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.

[0127] Exemplarily, the above methods and devices can be implemented in the form of a computer program, and the computer program can run on a computer device as Figure 3 shown.

[0128] Please refer to Figure 3 , Figure 3 , which is a schematic block diagram of the structure of a computer device provided by an embodiment of the present application. The computer device can be a server or a terminal.

[0129] As Figure 3 shown, the computer device includes a processor, a memory, and a network interface connected through a system bus. Among them, the memory can include a storage medium and an internal memory.

[0130] 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 intelligent testing method for a multi-module charging system.

[0131] The processor is used to provide computing and control capabilities to support the operation of the entire computer device.

[0132] 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 a multi-module charging system.

[0133] The network interface is used for network communication, such as sending the assigned tasks, etc. Those skilled in the art can understand that Figure 3 the structure shown in

[0134] 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 (DSPs), application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs) 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.

[0135] Among them, in one embodiment, the processor is used to run a computer program stored in the memory to implement the following steps:

[0136] Perform module-level testing on at least one of the charging modules to obtain module test parameters corresponding to each of the charging modules;

[0137] Determine module prediction parameters of the charging module in the multi-module charging system according to the module test parameters;

[0138] Determine network position parameters corresponding to each of the module test parameters according to the positions of the charging modules in the multi-module charging system;

[0139] Determine 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;

[0140] 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.

[0141] In some embodiments, when 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, it is used to implement:

[0142] Perform module-level testing on at least one of the charging modules to obtain the thermal resistance and heat capacity corresponding to each of the charging modules.

[0143] In some embodiments, when 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 heat capacity corresponding to each of the charging modules, it is used to implement:

[0144] Control the charging module to enter the operating mode and test the temperature change of the charging module;

[0145] Determine the thermal resistance according to the ratio of the temperature change to the preset thermal power, and determine the heat capacity according to the ratio of the thermal power to the temperature change.

[0146] In some embodiments, when the processor is used to implement the process of determining the module prediction parameters of the charging module in the multi-module charging system, it is used to implement:

[0147] Determine 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.

[0148] In some embodiments, when 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, it is used to implement:

[0149] Determine the module prediction parameters according to the following formula:

[0150]

[0151] where represents the module prediction parameter of the charging module at time t, represents the thermal resistance, represents the heat capacity, represents the ambient temperature, and Q represents the thermal power of the charging module.

[0152] In some embodiments, when the processor is used to implement the process of determining the system prediction parameters for system-level testing of the multi-module charging system according to the module prediction parameters and the network location parameters, it is used to implement:

[0153] Determine the adjacent charging modules of the charging module in the multi-module charging system according to the network location parameters;

[0154] Determine the system prediction parameters according to the following formula:

[0155]

[0156] Wherein, represents the system prediction parameter 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 heat capacity of the charging module.

[0157] In some embodiments, when the processor is used to implement the process of determining the adjacent charging module of the charging module in the multi-module charging system according to the network location parameter, it is used to implement:

[0158] Determine the candidate charging module closest to the charging module as the adjacent charging module;

[0159] After determining the system prediction parameters of the charging module and the adjacent charging module, determine the system composed of the charging module and the adjacent charging module as a candidate charging module.

[0160] In some embodiments, when the processor is used to implement the process of analyzing the system prediction parameter based on a preset deep learning network to obtain the target system prediction parameter corresponding to the multi-module charging system, it is used to implement:

[0161] Input the module test parameter, the network location parameter, and the system prediction parameter into the deep learning network to obtain the offset corresponding to the system prediction parameter;

[0162] Determine the correction result of the system prediction parameter according to the system prediction parameter and the offset;

[0163] Determine the target system prediction parameter corresponding to the multi-module charging system according to the correction result.

[0164] It should be noted that those skilled in the art can clearly understand that for the convenience and conciseness of description, the specific working process of the above-described intelligent testing method for the multi-module charging system can refer to the corresponding process in the embodiment of the intelligent testing method for the multi-module charging system described above, and will not be repeated here.

[0165] It should be understood that the terms used in the specification of the present application are only for the purpose of describing specific embodiments and are not intended to limit the present application. As used in the specification of the present application and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an", and "the" are intended to include the plural forms.

[0166] It should also be understood that the term "and / or" used in the specification and appended claims of this application refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations. It should be noted that in this text, the term "comprises", "comprising" or any other variation thereof is intended to cover non-exclusive inclusion, so that a process, method, article or system comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or elements inherent to such process, method, article or system. Without further limitation, an element defined by the statement "comprising an..." does not exclude the existence of additional identical elements in the process, method, article or system comprising that element.

[0167] The serial numbers of the embodiments of the present application above are only for description and do not represent the superiority or inferiority of the embodiments. The above is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art in the technical field disclosed by the present application can easily think of various equivalent modifications or substitutions, and these modifications or substitutions should all be covered within the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims

1. An intelligent testing method for a multi-module charging system, 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 test 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; Determining network position parameters corresponding to the module test parameters according to the positions of the charging modules 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; Analyzing the system prediction parameters based on a preset deep learning network to obtain target system prediction parameters corresponding to the multi-module charging system; Wherein, the determining the module prediction parameters of the charging module in the multi-module charging system according to the module test parameters includes: 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; Wherein, the 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: Determining the module prediction parameters according to the following formula: Among them, represents the module prediction parameter of the charging module at time t, represents the thermal resistance, represents the heat capacity, represents the ambient temperature, and Q represents the thermal power of the charging module; Wherein, the 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 position parameters includes: Determining adjacent charging modules of the charging module in the multi-module charging system according to the network position parameters; Determining the system prediction parameters according to the following formula: Among them, 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 heat capacity of the charging module.

2. The intelligent testing method for the 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: Performing module-level testing on at least one of the charging modules to obtain the thermal resistance and heat capacity corresponding to each of the charging modules.

3. The intelligent testing method for the 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 heat capacity corresponding to each of the charging modules includes: Controlling the charging module to enter an operating mode and testing the temperature change of the charging module; Determining the thermal resistance according to the ratio of the temperature change to a preset thermal power, and determining the heat capacity according to the ratio of the thermal power to the temperature change.

4. The intelligent test method for the multi-module charging system according to claim 1, characterized in that, The determining the adjacent charging modules of the charging module in the multi-module charging system according to the network position parameters includes: Determining the candidate charging module closest to the charging module as the adjacent charging module; After determining the system prediction parameters of the charging module and the adjacent charging module, determining the system composed of the charging module and the adjacent charging module as a candidate charging module.

5. The intelligent testing method for the multi-module charging system according to any one of claims 1-4, characterized in that The analyzing the system prediction parameters based on a preset deep learning network to obtain target system prediction parameters corresponding to the multi-module charging system includes: Inputting the module test parameters, the network position parameters and the system prediction parameters into the deep learning network to obtain an offset corresponding to the system prediction parameters; Determining a correction result of the system prediction parameters according to the system prediction parameters and the offset; Determine the target system prediction parameter corresponding to the multi-module charging system according to the correction result.

6. 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 intelligent testing device for the multi-module charging system includes: A module testing module, configured to perform module-level testing on at least one of the charging modules to obtain module testing parameters corresponding to each of the charging modules; A first prediction module, configured to determine the module prediction parameter of the charging module in the multi-module charging system according to the module testing parameter; A position determination module, configured to determine the network position parameter corresponding to each of the module testing parameters according to the positions of the charging modules in the multi-module charging system; A second prediction module, configured to determine the system prediction parameter for performing system-level testing on the multi-module charging system according to the module prediction parameter and the network position parameter; A parameter correction module, configured to analyze the system prediction parameter based on a preset deep learning network to obtain the target system prediction parameter corresponding to the multi-module charging system; Wherein, the determining the module prediction parameter of the charging module in the multi-module charging system according to the module testing parameter includes: Determining the module prediction parameter of the charging module in the multi-module charging system according to the module testing parameter and the detected ambient temperature; Wherein, the determining the module prediction parameter of the charging module in the multi-module charging system according to the module testing parameter and the detected ambient temperature includes: Determining the module prediction parameter according to the following formula: Among them, represents the module prediction parameter of the charging module at time t, represents the thermal resistance, represents the heat capacity, represents the ambient temperature, and Q represents the thermal power of the charging module; Wherein, the determining the system prediction parameter for performing system-level testing on the multi-module charging system according to the module prediction parameter and the network position parameter includes: Determining the adjacent charging modules of the charging module in the multi-module charging system according to the network position parameter; Determining the system prediction parameter according to the following formula: Among them, 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 heat capacity of the charging module.

7. A computer device, characterized in that, The computer device includes a processor, a memory, and a computer program stored on the memory and executable by the processor. When the computer program is executed by the processor, the steps of the intelligent testing method for the multi-module charging system according to any one of claims 1 to 5 are implemented.

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