Perovskite cell detection method and computer readable storage medium

Abstract

The invention discloses a detection method of a perovskite battery. The method comprises the steps of obtaining test task configuration; establishing a communication connection with an execution device; source table test parameters are generated through intelligent parameter adaptation; the source table test parameters are issued to the source table, and a test channel set is configured for the programmable logic controller; the sunlight simulator is controlled to enter target illumination intensity and wait for stability, and the temperature controller is controlled to enter target temperature and wait for stability; a synchronous trigger signal is generated by the programmable logic controller, and a plurality of channels are synchronously activated; controlling the source table to test according to the test mode, obtaining sampling data of each channel, and caching the sampling data; performing time sequence calibration on the sampling data of each channel according to the timestamp of the synchronous trigger signal so as to obtain time-aligned multi-channel sampling data; and generating and outputting a basic performance test result and a deep performance test result through composite performance analysis based on the multi-channel sampling data.

Description

Technical Field

[0001] This application relates to the field of battery testing technology, and in particular to a method for testing perovskite batteries and a computer-readable storage medium. Background Technology

[0002] Perovskite solar cells have become a research hotspot and a key focus of industrialization in the photovoltaic field in recent years due to their advantages such as high potential for improving photoelectric conversion efficiency, relatively low manufacturing cost, and flexibility and bendability.

[0003] With the rapid iteration of device efficiency, process routes and application scenarios, the R&D and mass production stages have placed higher demands on the performance testing of perovskite solar cells. This requires not only higher accuracy and repeatability of test results, but also higher efficiency and automation of the testing process, especially in the increasingly prominent testing needs for multi-condition, multi-parameter combinations and long-term stability assessment.

[0004] Currently, the testing of perovskite solar cells typically relies on traditional photovoltaic cell testing systems and their supporting software platforms. The hardware mainly consists of a source meter, a solar simulator, and a temperature control system.

[0005] However, these traditional solutions often struggle to meet the demands of both high-throughput and high-consistency testing. Since traditional testing equipment is primarily single-channel, expanding to multiple channels can lead to acquisition delays and timing inconsistencies. This makes it difficult to align data from each channel under a unified time reference when testing multiple devices in parallel, thus affecting the comparability and accuracy of parallel test results.

[0006] Furthermore, the testing process requires configuring numerous parameters, such as the source meter range, scan range and step size, integration period, and temperature threshold. Traditional solutions rely solely on manual experience for setting and adjustment, which can easily lead to parameter mismatches when switching between multiple modes or different device types, resulting in complex operation, low efficiency, and a high risk of errors.

[0007] Furthermore, the analytical capabilities of existing testing software are generally biased towards the basic level, typically only providing IV curve plotting and calculation of basic indicators such as open-circuit voltage Voc and short-circuit current Isc. It is difficult to accurately characterize the more representative deep electrical characteristics of perovskite devices. For example, the ability to calculate and evaluate parameters such as ideality factor, reverse saturation current density, and corrected maximum power point related indicators is insufficient, which affects the in-depth diagnosis of device mechanisms, defects, and degradation behavior.

[0008] In terms of data management and traceability, existing solutions often store test data in a scattered manner as local files, lacking a unified data structure and management interface. This results in insufficient data traceability, historical comparison, and batch statistical analysis capabilities, making it difficult to meet the systematic management needs of multiple batches and conditions of data during the R&D process. It is also not conducive to subsequent integration and reuse with data analysis platforms such as MATLAB and Python.

[0009] Furthermore, perovskite solar cells exhibit significant dynamic response and time-varying characteristics. Dynamic tests such as IV Delay, Bias Delay, and Light Soaking are crucial for evaluating the dynamic behavior and stability of these devices. However, traditional methods offer limited support for these dynamic testing procedures, making it difficult to meet the comprehensive evaluation requirements for the dynamic characteristics and long-term reliability of perovskite devices.

[0010] There are currently some general-purpose software platforms for perovskite solar cell testing. However, these platforms often use polling-based acquisition methods for multi-channel testing, resulting in timing deviations between channels reaching the order of hundreds of milliseconds, which can easily lead to inconsistent timing of parallel test results. Moreover, their parameter configurations typically lack intelligent adaptation mechanisms; key parameters such as range, scan step size, and integration period require manual trial and error based on device characteristics, resulting in a high overall operational threshold and low efficiency.

[0011] In addition, since the analysis model and algorithm are not optimized for the characteristics of perovskite devices, the accuracy and stability of deep parameters are difficult to guarantee, and the error is large, resulting in many shortcomings and defects in practical applications. Summary of the Invention

[0012] This application provides a method for detecting perovskite solar cells and a computer-readable storage medium, aiming to address at least one deficiency in existing outdoor testing methods.

[0013] In a first aspect, embodiments of this application provide a method for detecting perovskite solar cells. The method includes: acquiring a test task configuration, the test task configuration including cell type, test mode, test channel set, target light intensity, device area, and target temperature; establishing a communication connection with an execution device, the execution device including: a source meter, a programmable logic controller (PLC), a solar simulator, and a temperature controller; generating source meter test parameters based on the cell type and the test mode through intelligent parameter adaptation; the source meter test parameters including: voltage range, current range, number of scan points, scan step size, and integration period; sending the source meter test parameters to the source meter, and configuring a test channel set for the PLC; controlling the solar simulator to enter the target light intensity and wait for stabilization, and controlling... The temperature controller reaches the target temperature and waits for it to stabilize; the programmable logic controller generates a synchronization trigger signal to synchronously activate multiple channels in the test channel set and begin collecting electrical data; the source meter is controlled to perform tests according to the test mode to obtain sampling data from each channel and cache the sampling data, which includes voltage data and corresponding current output; after all multiple channels have completed acquisition, the sampling data of each channel is time-calibrated according to the timestamp of the synchronization trigger signal to obtain time-aligned multi-channel sampling data; based on the multi-channel sampling data, basic performance test results and deep performance test results are generated through composite performance analysis; the basic performance test results and deep performance test results are output.

[0014] In some embodiments, the method further includes: performing status detection on the execution device, and suspending the test and outputting abnormal information when at least one execution device is not in a ready state.

[0015] In some embodiments, the method further includes storing the test task, the corresponding multi-channel sampling data, the basic performance test results, and the deep performance test results in a preset standardized data structure.

[0016] In some embodiments, the test channel set includes test channels 1 to 8, and the channel number of each test channel is fixedly mapped to the bit address of the programmable logic controller.

[0017] In some embodiments, the intelligent parameter adaptation is based on a parameter knowledge base, which includes the mapping relationship between battery type, test mode, and default parameters.

[0018] In some embodiments, the intelligent parameter adaptation specifically includes: estimating voltage and current parameters based on initial scan data, and dynamically adjusting the voltage range and the current range based on the estimated voltage and current parameters; wherein the estimated voltage parameter is the voltage corresponding to the point with the smallest absolute current value; the estimated current parameter is the current corresponding to the point with the smallest absolute voltage value; the voltage range is generated by multiplying the estimated voltage parameter by a preset margin coefficient, and the current range is generated by multiplying the estimated current parameter by a preset margin coefficient, and is limited to the range supported by the device.

[0019] In some embodiments, the intelligent parameter adaptation specifically includes: verifying the automatically generated scan step size, and when the scan step size is less than the preset minimum step size, correcting the scan step size to the minimum step size.

[0020] In some embodiments, the composite performance analysis specifically includes: selecting data points within a preset voltage range in a preset proximity range of the short-circuit current and performing linear fitting to obtain the series resistance; performing linear fitting on the current data and voltage data in a high-voltage region to obtain the ideal factor; wherein, the high-voltage region is a region where the voltage value is greater than 0.8 times the estimated voltage parameter.

[0021] In some embodiments, the test modes include current-voltage scan, maximum power point tracking, constant point output, and single-point scan or function test.

[0022] Secondly, embodiments of this application provide a computer-readable storage medium. This computer-readable storage medium stores a computer program, which, when executed by a processor, implements the function of the perovskite solar cell detection method described above.

[0023] At least one beneficial effect of the perovskite solar cell testing method of this application embodiment is that the testing method, through a closed-loop process of "task configuration - equipment collaboration - synchronous acquisition - timing calibration - composite analysis - result output", enables the testing process to complete the parallel testing of multiple devices under unified operating conditions and a unified time reference.

[0024] On the one hand, a synchronous trigger signal is used to simultaneously activate multiple channels in the test channel set, and after acquisition, the sampling data of each channel is time-calibrated according to the timestamp of the synchronous trigger signal, thereby obtaining time-aligned multi-channel sampling data. This synchronous trigger and timing calibration mechanism significantly suppresses the timing deviation introduced between channels by traditional polling acquisition, avoids parameter offset and misjudgment caused by asynchronous acquisition when testing multiple devices in parallel, and enables multi-channel parallel testing to have higher timing consistency and data reliability, while significantly improving test throughput.

[0025] Moreover, intelligent parameter adaptation is performed based on battery type and test mode, automatically generating source table test parameters and sending them to the source table. This reduces reliance on operator experience, lowers configuration complexity and error probability when switching between multiple modes, significantly shortens test preparation time, thereby improving overall usability and reducing the operating threshold for beginners.

[0026] On the other hand, after obtaining time-aligned multi-channel sampling data, composite performance analysis is performed, generating both basic performance test results and in-depth performance test results. This enables a more comprehensive quantitative evaluation of the device's deep electrical characteristics, thereby improving the analysis's coverage and interpretability, reducing analytical errors, enhancing accuracy and reliability, and better meeting the needs of quality inspection for high-precision and multi-dimensional evaluation. Attached Figure Description

[0027] Figure 1 This is a flowchart of a method for detecting perovskite solar cells according to an embodiment of this application; Figure 2 This is a schematic diagram of an electronic device according to an embodiment of this application. Detailed Implementation

[0028] To facilitate understanding of this application, a more detailed description is provided below with reference to the accompanying drawings and specific embodiments. It should be noted that when an element is described as being "fixed to" another element, it can be directly on the other element, or one or more intermediate elements may exist between them. When an element is described as being "connected" to another element, it can be directly connected to the other element, or one or more intermediate elements may exist between them. The terms "upper," "lower," "inner," "outer," "bottom," etc., used in this specification indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, and are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of this application. Furthermore, the terms "first," "second," "third," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0029] Unless otherwise defined, all technical and scientific terms used in this specification have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the scope of this application. The term "and / or" as used in this specification includes any and all combinations of one or more of the associated listed items.

[0030] Furthermore, the technical features involved in the different embodiments of this application described below can be combined with each other as long as they do not conflict with each other.

[0031] Figure 1 A detection method for perovskite solar cells provided in the embodiments of this application. For example... Figure 1 As shown, the detection method includes: S101. Obtain test task configuration.

[0032] The test task configuration includes battery type, test mode, test channel set, target light intensity, device area and target temperature.

[0033] Specifically, the test modes include current-voltage scan, maximum power point tracking, constant point output, and single-point scan or function test.

[0034] S102. Establish a communication connection with the execution device.

[0035] The execution device includes: a source meter, a programmable logic controller, a solar simulator, and a temperature controller.

[0036] S103. Based on battery type and test mode, source table test parameters are generated through intelligent parameter adaptation.

[0037] The test parameters of this source meter include: voltage range, current range, number of scan points, scan step size, and integration period.

[0038] In some embodiments, this intelligent parameter adaptation is based on a parameter knowledge base. This parameter knowledge base includes a mapping relationship between battery type, test mode, and default parameters.

[0039] Specifically, the intelligent parameter adaptation includes: estimating voltage and current parameters based on initial scan data, and dynamically adjusting the voltage range and the current range based on the estimated voltage and current parameters.

[0040] Furthermore, the intelligent parameter adaptation also verifies the automatically generated scan step size. When the scan step size is less than the preset minimum step size, the scan step size is corrected to the minimum step size.

[0041] The estimated voltage parameter is the voltage corresponding to the point with the smallest absolute current value, and the estimated current parameter is the current corresponding to the point with the smallest absolute voltage value. The voltage range is generated by multiplying the estimated voltage parameter by a preset margin coefficient, and the current range is generated by multiplying the estimated current parameter by a preset margin coefficient, and is limited to the range supported by the device.

[0042] S104. Send the source table test parameters to the source table and configure the test channel set for the programmable logic controller.

[0043] Specifically, the test channel set includes test channels 1 through 8, and the channel number of each test channel is fixedly mapped to the bit address of the programmable logic controller.

[0044] S105. Control the solar simulator to enter the target light intensity and wait for it to stabilize, and control the temperature controller to enter the target temperature and wait for it to stabilize.

[0045] S106. The programmable logic controller generates a synchronous trigger signal to synchronously activate multiple channels in the test channel set and begin collecting electrical data.

[0046] S107. Control the source table to perform tests in test mode, obtain sampling data of each channel and cache the sampling data.

[0047] The sampled data includes voltage data and the corresponding current output.

[0048] S108. After multiple channels have completed acquisition, the sampling data of each channel is time-calibrated according to the timestamp of the synchronization trigger signal to obtain time-aligned multi-channel sampling data.

[0049] S109. Based on multi-channel sampling data, generate basic performance test results and deep performance test results through composite performance analysis.

[0050] The basic performance test results include: open-circuit voltage Voc, short-circuit current Isc, maximum power Pmax, maximum power point voltage Vmp, maximum power point current Imp, fill factor FF, and conversion efficiency η. The advanced performance test results include: ideality factor n, reverse saturation current density J0, series resistance Rs, parallel resistance Rsh, and MMF-corrected power.

[0051] Specifically, the composite performance analysis includes: selecting data points within a preset voltage range in the vicinity of the short-circuit current to obtain the series resistance through linear fitting; and performing linear fitting on the current data and voltage data in the high-voltage region to obtain the ideal factor; wherein the high-voltage region is where the voltage value is greater than 0.8 times the estimated voltage parameter.

[0052] S110 outputs basic performance test results and deep performance test results.

[0053] The results of basic performance tests and in-depth performance tests can be presented and output as visual information in any suitable manner.

[0054] In some embodiments, after step S102, the detection method further includes: performing status detection on the execution device, and when at least one execution device is not in a ready state, terminating the test and outputting abnormal information.

[0055] In some embodiments, after step S110, the detection method further includes storing the test task, the corresponding multi-channel sampling data, the basic performance test results and the deep performance test results in a preset standardized data structure.

[0056] To fully describe the inventive concept of this application, the following describes in detail the flow of the core method steps of the detection method with specific examples.

[0057] 1) Multi-channel synchronous acquisition: Initially, a fixed mapping relationship is established between channel numbers and PLC addresses, and each channel is activated, reset, and controlled for state switching via PLC I / O signals. For example, a one-to-one mapping is established between test channels 1 to 8 and address bits within the M8052.1–M8053.0 range of the programmable logic controller. Through this fixed mapping relationship, channel control is driven by deterministic hardware bit signals, avoiding inconsistent channel start-up and stop times caused by software polling or non-deterministic scheduling.

[0058] Secondly, a unified synchronous trigger signal is generated by a programmable logic controller (PLC) and applied simultaneously to all activated channels, enabling all test channels to start data acquisition on the same trigger edge. This hardware-based triggering method keeps the trigger latency within a small range (e.g., ≤1ms), fundamentally reducing the relative time deviation when starting multi-channel acquisition and providing a foundation for the time consistency of subsequent multi-channel data.

[0059] Secondly, after each test channel completes data acquisition and obtains sampled data, it does not immediately upload the data channel by channel. Instead, the acquired data is temporarily stored in a local cache. Once all channels have completed acquisition, the data is uploaded to the host computer all at once via a batch transmission protocol for data analysis. By reducing frequent inter-channel communication and transmission queuing, timing jitter and additional latency caused by network / bus transmission can be reduced, thereby improving the overall consistency and efficiency of multi-channel data upload.

[0060] Finally, the host computer performs timing calibration on the data of each channel based on the timestamp information of the synchronization trigger signal generated by the programmable logic controller, so that the sampled data of each channel are aligned under a unified time reference. Through timing calibration, residual differences in the acquisition start and transmission process can be further eliminated, ensuring the consistency of timestamps of data from different channels. The calibration accuracy can be controlled at a high level (e.g., ≤5ms), thereby meeting the timing consistency requirements of parallel testing of multiple devices.

[0061] In addition, to ensure the stability of data access during multi-channel parallel acquisition and uploading, a thread-safe data caching structure is adopted for data management. For example, data for each channel is stored in the form of a cache dictionary, and a locking mechanism (such as lock mutual exclusion) is used to ensure atomicity and consistency during multi-threaded access, avoiding problems such as concurrent writing, overwriting, or reading incomplete data, thereby improving the reliability and repeatability of the synchronous acquisition link.

[0062] 2) Intelligent parameter adaptation: This intelligent parameter adaptation combines knowledge-based and data-driven approaches to automatically generate parameter configurations that match the current device and test task, while meeting the constraints of device capability boundaries and test duration. This reduces operational complexity and improves the consistency and reliability of parameter settings.

[0063] First, a mapping knowledge base is built to record "battery type - test mode - parameter configuration" to accumulate recommended parameter combinations for different device types and different test tasks.

[0064] The mapping knowledge base covers mainstream battery types such as perovskite and crystalline silicon, and provides corresponding optimal or recommended parameter configuration items (e.g., voltage / current range selection rules, number of scan points or scan step size, integration period NPLC, delay strategy, etc.) for common test modes such as IV scanning, MPPT tracking, and constant point output, thus providing an initial basis for subsequent automated adaptation.

[0065] Secondly, characteristic quantities such as Voc and Isc, or their changing trends, are obtained from the initial sampling curve, and the battery type or its approximate category is determined accordingly, achieving automatic battery type identification. This reduces manual input while improving the specificity and fault tolerance of parameter selection. Alternatively, the user can manually select the battery type.

[0066] Secondly, the requirements of the selected test mode are analyzed to determine the set of key parameters and their constraints. For example, different dynamic / delay tests (such as DD, HD, HS) or power point tracking tests (such as MPPT) have different requirements for scan step size, number of sampling points, range coverage, integration period NPLC, and necessary delay / stabilization waiting conditions. In this case, it is necessary to perform structured analysis of the test mode to clarify the parameters that need to be calculated, inherited, and adjusted according to rules during the parameter generation stage, ensuring the integrity and consistency of the configuration.

[0067] Finally, the default parameters in the mapping knowledge base are used as initial values, and the parameters are dynamically calculated and adjusted based on the Voc and Isc estimates obtained from the initial scan data. For example, the voltage range and scan upper limit are determined based on the Voc estimate, and the current range and protection threshold are determined based on the Isc estimate. On this basis, the scan step size, number of scan points, and NPLC parameters are adjusted in a coordinated manner to balance measurement accuracy, acquisition time, and equipment range utilization, thereby achieving adaptive configuration for different devices and different modes.

[0068] In addition, to ensure the usability of automatically generated parameters and to avoid introducing abnormal testing risks, the parameters undergo reasonableness verification and correction. Specific verification conditions include: whether the configured voltage / current range exceeds the hardware limit or resolution range of the source meter; whether the scan step size is too small, leading to excessively long acquisition time or data redundancy; and whether the integration period of the NPLC matches the target test cycle or noise level. Accordingly, when a parameter is detected as not meeting the above verification conditions, it will be corrected according to rules to bring it back to the executable range. If necessary, the reason for the correction will be indicated or recorded to ensure the stable execution of the test task.

[0069] Preferably, for user-confirmed valid custom parameter combinations under specific battery types and test modes, these can be written to or updated to the corresponding mapping knowledge base, enabling subsequent similar devices and similar test tasks to directly reuse the configuration, thereby further shortening test preparation time and improving configuration consistency among different operators.

[0070] 3) Composite performance analysis: This composite performance analysis uses discrete voltage-current sampling data acquired through multiple channels as input. While ensuring rapid calculation of basic electrical performance indicators, it further fits and derives relevant parameters of the device equivalent circuit and diode model, thereby achieving a comprehensive performance evaluation and quantitative characterization of perovskite solar cells at the mechanistic level.

[0071] In terms of basic parameter calculation, the open-circuit voltage Voc, short-circuit current Isc, maximum output power Pmax, maximum power point voltage Vmp, maximum power point current Imp, fill factor FF, and photoelectric conversion efficiency η are directly extracted or calculated from the sampled data.

[0072] The open-circuit voltage Voc can be determined by the voltage corresponding to the sampling point with the smallest absolute current value, for example, satisfying the condition that... The short-circuit current Isc can be determined by the current corresponding to the sampling point with the smallest absolute voltage value, for example, satisfying the condition... point.

[0073] For power-related metrics, instantaneous power is calculated from each sampled data point. The maximum value among these is taken as the maximum output power Pmax, and the voltage and current corresponding to this maximum power are determined as Vmp and Imp, respectively. (Fill factor) .

[0074] The conversion efficiency η is calculated based on the incident light power and the effective area of ​​the device. For example, according to... The unit of light intensity is mW / cm², and the unit of area is cm². The "1000" in the above formula is used for power unit conversion to ensure consistency with the light intensity expression used.

[0075] In terms of analyzing deeper parameters, within the framework of equivalent circuit and diode model, we further obtained indices such as series resistance Rs, parallel resistance Rsh, ideality factor n, reverse saturation current density J0, and MMF-corrected power considering the effects of resistance and temperature.

[0076] For the series resistance Rs, local data points are selected near the short-circuit current for linear fitting. For example, data points with voltages less than 10% of the short-circuit current Isc are selected near the short-circuit current Isc, and the local slope of the voltage-current relationship is calculated. Its absolute value is taken as Rs.

[0077] For a parallel resistor Rsh, local data points are selected near the open-circuit voltage for linear fitting. For example, data points with an absolute current value less than 10 times the current at the open-circuit voltage Voc are selected. The local slope is calculated, and the reciprocal of the absolute value of the slope is used as Rsh to reflect the leakage and bypass characteristics of the device in the near-open circuit region.

[0078] The solution for the ideality factor n is based on the diode equation. In the high-voltage region, data points are selected for linearization fitting. For example, data points satisfying... The sampling points, for The slope is obtained by linear fitting with V. Thus, the ideal factor can be obtained. , where q is the electron charge, k is the Boltzmann constant, and T is the absolute temperature.

[0079] After obtaining n, the reverse saturation current I0 is derived by combining the open-circuit voltage Voc and the short-circuit current Isc, and the reverse saturation current density is further calculated based on the device area S. Among them, I0 can be according to The calculation enables quantitative characterization of key parameters related to device recombination and interface loss.

[0080] In addition, to more closely approximate the output capability of perovskite solar cells under actual operating conditions affected by series resistance and temperature drift, the calculation of MMF-corrected power can be introduced.

[0081] This corrected power is based on the maximum power point, taking into account both the voltage drop caused by the series resistance and the effect of temperature on the current. For example, Calculate, where, The temperature coefficient of current. This represents the difference between the actual temperature and the reference temperature.

[0082] By combining the output of the basic and deep parameters mentioned above, performance index evaluation and mechanism parameter diagnosis can be achieved simultaneously on the same set of sampled data, thereby improving the accuracy of analysis and the ability to interpret results.

[0083] 4) Cross-device collaborative control: In some embodiments, by using a layered decoupling approach, the underlying communication and device differences are separated from the upper-level test process control, enabling the unified management of multiple types of devices and achieving stable and orderly linkage control during the testing process, thereby improving system compatibility, scalability, and operational reliability.

[0084] At the communication layer, a communication protocol abstraction layer is set up and a unified communication interface ICommunication is defined to shield the differences between different physical links and protocol stacks, such as LAN, GPIB, and RS232. Upper-layer modules only invoke general communication capabilities such as connection establishment, command sending, data reception, and timeout settings through the ICommunication interface, without needing to concern themselves with the implementation details of specific communication protocols. Through this abstraction layer, replacing or adding a communication protocol only involves extending the interface implementation, without affecting the upper-layer testing process and business logic, thereby reducing the system's coupling to specific communication protocols.

[0085] At the device level, a device driver adaptation layer is set up, providing dedicated driver modules for different types of execution devices such as source meters, PLCs, temperature controllers, and solar simulators. Each device driver uses ICommunication to complete functions such as device connection, identity / capability identification, parameter configuration distribution, data reading and writing, and necessary instruction encapsulation and parsing, and provides a unified entry point for calling device capabilities. With the help of this adaptation layer, the differences in command sets between different manufacturers and models of devices are encapsulated within the corresponding drivers. The upper layer only needs to orchestrate test processes based on device capabilities, thereby achieving plug-and-play access and rapid replacement of devices.

[0086] At the process control level, a collaborative scheduling service is set up, and a state machine model is used to uniformly orchestrate and constrain device collaboration. Specifically, the collaborative scheduling service defines a set of working states for various devices and triggers device state transitions and linkage actions based on test process events. For example, it completes connection detection and readiness verification before entering the test, coordinates the start order and waiting conditions of illumination, temperature, and source meter measurements during test execution, and performs shutdown, reset, and resource release after test completion. Through the state machine-driven scheduling method, the multi-device collaboration process can have deterministic state boundaries and transition conditions, avoiding process disorder and test failures caused by inconsistent control sequences or missing state judgments. Even better, an exception handling mechanism has been further established. When a device communication anomaly, response timeout, or data read / write failure is detected, the collaborative scheduling service triggers a retry strategy, such as performing 3 retries by default. If the retries still fail, the system switches to a backup device (if one exists) according to the system configuration, or terminates the current test process and outputs clear exception prompts and fault information to the user, so as to reduce the test risk caused by single point of communication failure and improve the efficiency of fault handling.

[0087] Furthermore, a device status monitoring mechanism is implemented to monitor and centrally manage the connection and operational status of all devices in real time, and to display a summary view in the user interface. This allows operators to monitor the online status, readiness status, and alarm information of source meters, PLCs, temperature controllers, and solar simulators in real time. Through this cross-device collaborative control architecture, it is possible to adapt to combinations of multiple protocols and device types without modifying the upper-layer business code, improving the consistency and maintainability of device collaboration and reducing system expansion and upgrade costs.

[0088] 4) Specific code implementation of related functions:

[0089] In summary, the perovskite solar cell testing method provided in this application comprehensively employs multi-channel synchronous acquisition, intelligent parameter adaptation, composite performance analysis, cross-device collaborative control, and data visualization, thereby enabling parallel testing, automated configuration, and multi-dimensional performance evaluation of perovskite solar cells within the same platform.

[0090] First, a multi-channel synchronous acquisition mechanism is used to achieve parallel startup of multiple channels and data time alignment. The inter-channel delay can be controlled within a small range, avoiding the efficiency bottleneck and timing error caused by traditional polling acquisition, thereby supporting parallel testing of multiple devices.

[0091] Secondly, through the composite performance analysis algorithm, basic parameters such as Voc, Isc, Pmax, FF, and efficiency, as well as deep parameters such as ideal factor, J0, Rs, and Rsh, are realized, enabling more comprehensive performance characterization and diagnosis. The analysis error can be controlled at a low level, which can meet the needs of high-precision R&D and quality inspection.

[0092] Furthermore, intelligent parameter adaptation automatically generates and verifies key configurations such as range, step size, number of points, and integration period, enabling rapid parameter setting. Simultaneously, communication protocol abstraction and device driver adaptation achieve unified access and collaborative control of multiple protocols and devices, eliminating the need to modify upper-layer code when adding new devices, significantly reducing expansion costs.

[0093] Finally, through efficient data visualization and standardized data storage and export, it supports multi-curve comparison, dynamic refresh and interactive viewing, and is compatible with formats such as JSON and CSV, making it easy to interface with platforms such as MATLAB and Python, and reducing the cost of secondary data utilization.

[0094] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0095] Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this application. The computer software can be stored in a computer-readable storage medium, and when executed, the program can include the processes of the embodiments of the methods described above. The storage medium can be a magnetic disk, optical disk, read-only memory, or random access memory, etc.

[0096] Figure 2 The diagram illustrates the structure of an electronic device according to an embodiment of this application. This application does not limit the specific implementation of the electronic device. Figure 2 As shown, the electronic device may include: a processor 21, a communication interface 22, a memory 23, and a communication bus 24.

[0097] The processor 21, communication interface 22, and memory 23 communicate with each other via communication bus 34. Communication interface 22 is used to communicate with other network elements, such as clients or other servers. The processor 21 executes program 25, specifically performing the relevant steps in the perovskite solar cell detection method of the above embodiments.

[0098] Specifically, program 25 may include program code, which includes computer operation instructions. Specifically, it may be used to cause processor 21 to execute the perovskite solar cell detection method in any of the above method embodiments.

[0099] In this embodiment of the application, depending on the type of hardware used, the processor 21 may be a central processing unit, or it may be other general-purpose processors, digital signal processors, application-specific integrated circuits, off-the-shelf programmable gate arrays or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.

[0100] Memory 23 is used to store program 25. Memory 23 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other non-volatile solid-state storage device. It has a program storage area and a data storage area, which are used to store program 25 and corresponding data information, respectively. For example, non-volatile software programs, non-volatile computer executable programs and modules are stored in the program storage area, or data information of the cluster system is stored in the data storage area.

[0101] This application also provides a computer-readable storage medium. This computer-readable storage medium can be a non-volatile computer-readable storage medium. This computer-readable storage medium stores a computer program.

[0102] When the computer program is executed by a processor, it implements one or more steps of the height information correction method disclosed in the embodiments of this application. The complete computer program product is embodied on one or more computer-readable storage media (including but not limited to, disk storage, CD-ROM, optical storage, etc.) containing the computer program disclosed in the embodiments of this application.

[0103] The above description is merely an embodiment of this application and does not limit the patent scope of this application. Any equivalent structural or procedural transformations made using the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.

Claims

1. A method for detecting perovskite solar cells, characterized in that, include: Obtain the test task configuration, which includes battery type, test mode, test channel set, target light intensity, device area and target temperature; Establish a communication connection with the execution device, which includes: a source table, a programmable logic controller, a solar simulator, and a temperature controller; Based on the battery type and the test mode, source meter test parameters are generated through intelligent parameter adaptation; the source meter test parameters include: voltage range, current range, number of scan points, scan step size, and integration period; The test parameters of the source table are sent to the source table, and a set of test channels is configured for the programmable logic controller; The solar simulator is controlled to enter the target light intensity and wait for it to stabilize, and the temperature controller is controlled to enter the target temperature and wait for it to stabilize; The programmable logic controller generates a synchronous trigger signal to synchronously activate multiple channels in the test channel set and begin collecting electrical data. The source meter is controlled to perform the test according to the test mode to obtain the sampling data of each channel and cache the sampling data. The sampling data includes voltage data and corresponding current output. After all channels have been acquired, the sampling data of each channel is time-calibrated according to the timestamp of the synchronization trigger signal to obtain time-aligned multi-channel sampling data. Based on the multi-channel sampling data, basic performance test results and deep performance test results are generated through composite performance analysis. Output the basic performance test results and the deep performance test results.

2. The detection method for perovskite solar cells according to claim 1, characterized in that, The method further includes: The execution device is subjected to status detection. When at least one execution device is not in a ready state, the test is stopped and an abnormal information is output.

3. The detection method for perovskite solar cells according to claim 1, characterized in that, The method further includes: The test tasks, corresponding multi-channel sampling data, basic performance test results, and deep performance test results are stored in a preset standardized data structure.

4. The detection method for perovskite solar cells according to claim 1, characterized in that, The test channel set includes test channels 1 to 8, and the channel number of each test channel is fixedly mapped to the bit address of the programmable logic controller.

5. The detection method for perovskite solar cells according to claim 1, characterized in that, The intelligent parameter adaptation is based on a parameter knowledge base, which includes the mapping relationship between battery type, test mode, and default parameters.

6. The detection method for perovskite solar cells according to claim 5, characterized in that: The intelligent parameter adaptation specifically includes: estimating voltage and current parameters based on initial scan data, and dynamically adjusting the voltage range and the current range based on the estimated voltage and current parameters; Wherein, the estimated voltage parameter is the voltage corresponding to the point where the absolute value of the current is the smallest; the estimated current parameter is the current corresponding to the point where the absolute value of the voltage is the smallest. The voltage range is generated by multiplying the estimated voltage parameter by a preset margin coefficient, and the current range is generated by multiplying the estimated current parameter by a preset margin coefficient, and is limited to the range supported by the device.

7. The detection method for perovskite solar cells according to claim 5, characterized in that, The intelligent parameter adaptation specifically includes: The automatically generated scan step size is verified. If the scan step size is less than the preset minimum step size, the scan step size is corrected to the minimum step size.

8. The detection method for perovskite solar cells according to claim 1, characterized in that, The composite performance analysis specifically includes: Within a preset range of short-circuit current, data points within a preset voltage range are selected for linear fitting to obtain the series resistance; An ideal factor is obtained by linearly fitting current and voltage data in a high-voltage region; wherein, the high-voltage region is where the voltage value is greater than 0.8 times the estimated voltage parameter.

9. The detection method for perovskite solar cells according to claim 1, characterized in that, The test modes include current-voltage scan, maximum power point tracking, constant point output, and single-point scan or function test.

10. A computer-readable storage medium having a computer program stored thereon, wherein when the program is executed by a processor, it is characterized in that, The method for detecting perovskite solar cells as described in any one of claims 1-9 is provided.

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