A photovoltaic module full life cycle identity management method

Abstract

The application discloses a kind of photovoltaic module full life cycle identity management methods, comprising the following steps: S1: continuously record the real-time running position of target photovoltaic module, the time and reason of each disassembly, installation, maintenance event, form full life cycle document data;S2: calculate current power attenuation rate, cumulative maintenance frequency and three data indexes of abnormal stress cumulative event number;S3: calculate the remaining economic life index of photovoltaic module;S4: the remaining economic life index output by S3 is compared with the preset economic exit threshold value, and whether the decommissioning pre-notification is output according to the comparison result is judged;S5: output the estimated residual value interval with data proof.The application is classified by difference based on the ratio of attenuation acceleration index and normalized attenuation rate, so that photovoltaic module with stable remaining power generation capacity is retained and photovoltaic module without utilization value is recommended to be recycled, resource waste is avoided, and the full life cycle benefit of power station assets is improved.

Description

Technical Field

[0001] This invention relates to the field of photovoltaic module management technology, specifically to a method for managing the identity of photovoltaic modules throughout their entire lifecycle. Background Technology

[0002] As the scale of distributed photovoltaic power stations continues to expand, a single plant area often deploys tens of thousands of photovoltaic modules. Over an operating cycle of more than 20 years, these modules will undergo multiple circulation stages, including warehousing, installation, maintenance, dismantling, and replacement. Traditional management methods rely heavily on manual paper records or electronic spreadsheets, which are prone to problems such as unclear asset quantities, unknown locations, and incorrect or missing installations, leading to asset loss and chaotic operation and maintenance.

[0003] To address the aforementioned issues, existing technologies have developed solutions that utilize RFID technology to assign unique electronic identities to photovoltaic modules and deploy readers at key locations to track module positions. For example, embedding RFID tags in the frame of the photovoltaic module and placing readers at factory entrances and warehouses can automatically record module entry and exit, as well as the approximate installation area. These solutions achieve initial asset digitization and effectively realize the goal of photovoltaic module management.

[0004] However, most current photovoltaic module management methods adopt a "one-size-fits-all" replacement strategy, resulting in the premature scrapping of many modules that still have power generation value, leading to resource waste. At the same time, the lack of residual value assessment methods based on actual operating history results in the undervaluation of photovoltaic modules, harming the overall lifecycle revenue of both the modules and the power plant. Summary of the Invention

[0005] To address this issue, the present invention provides a method for full lifecycle identity management of photovoltaic modules, thereby solving the problem that the lack of residual value assessment methods based on actual operating history in the prior art leads to the underestimation of the value of photovoltaic modules and the waste of resources.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] A method for full lifecycle identity management of photovoltaic modules includes the following steps:

[0008] S1: Set RFID tags when photovoltaic modules leave the factory, store unique identification codes and factory rated power, and continuously record the real-time operating location of the target photovoltaic module, the time and reason of each disassembly, installation and maintenance event, forming full life cycle document data;

[0009] S2: Continuously collect full lifecycle document data of the target photovoltaic module using the identification code as an index and calculate three data indicators: current power attenuation rate, cumulative number of repairs, and cumulative number of abnormal stress events;

[0010] S3: Normalize the three data indicators output by S2, and dynamically calculate the remaining economic life index of the photovoltaic module based on the three processed data indicators.

[0011] S4: Compare the remaining economic life index output by S3 with the preset economic exit threshold, and determine whether to output a retirement pre-notification based on the comparison result;

[0012] S5: For the target photovoltaic module that triggered the decommissioning pre-notification in S4, retrieve the full life cycle document data of the target module up to the triggering time, classify it for differentiated treatment, call the corresponding residual value assessment model, and output the estimated residual value range with accompanying data proof.

[0013] Furthermore, the specific steps of S2 are as follows:

[0014] S2.1: Based on the DC output power and backsheet temperature of the photovoltaic module collected at fixed intervals, and the real-time solar irradiance provided by the weather station, the measured power under any field conditions is uniformly converted into standard test conditions, and the current power attenuation rate is calculated.

[0015] S2.2: Retrieve event records marked as maintenance in the full lifecycle document data using the identification code as an index, automatically count according to preset statistical rules, and determine the cumulative number of maintenance times;

[0016] S2.3: Using vibration sensors installed on the module frame and acoustic shock wave sensors in the power station, the physical stress data exerted on the module by the external environment is captured in real time, and automatically recorded and accumulated to obtain the cumulative number of abnormal stress events.

[0017] Furthermore, the remaining economic life index The value ranges from 0 to 1. The closer it is to 1, the better the overall condition of the photovoltaic module and the higher the economic surplus; the closer it is to 0, the sooner the economic lifespan of the photovoltaic module will be exhausted. The calculation formula is as follows:

[0018]

[0019] in, , and The ratio of the preset weighting coefficients to the normalized decay rate. This is the normalized decay rate ratio. This is the normalized ratio of maintenance frequency. This represents the normalized ratio of abnormal stress events.

[0020] Furthermore, if the remaining economic life index is determined in step S4... Preset economic exit threshold This indicates that continuing to retain the target photovoltaic modules would result in a loss of overall power plant revenue, and the process of generating a pre-decommissioning notice should be initiated immediately.

[0021] If the remaining economic life index Preset economic exit threshold This indicates that the overall health status of the target photovoltaic module is normal and it is in normal operating condition.

[0022] Furthermore, S5 includes the following steps:

[0023] S5.1: Using the historical sequence value of the current power decay rate D continuously collected and stored in the full life cycle document data in S2.1, calculate the decay acceleration index, which represents the performance degradation trend of the target photovoltaic module;

[0024] S5.2: Classify the degradation acceleration index of the target photovoltaic module and the ratio of the normalized degradation rate of the target photovoltaic module at the current moment through the preset residual value assessment model, and output the estimated residual value range.

[0025] Furthermore, the specific content of S5.1 is as follows:

[0026] 1) Extract multiple data points of the current power degradation rate of the target photovoltaic module from the full life cycle document data within a preset recent statistical window, calculated according to S2.1;

[0027] 2) Perform a first-order linear fit on all data points within the recent statistical window to obtain the recent decay slope. ;

[0028] 3) Extract all historical current power degradation rate data points of the target photovoltaic module from the stable service period to the start date of the recent statistical window from the full life cycle document data, perform first-order linear fitting, and obtain the long-term historical average degradation slope. ;

[0029] 4) Based on recent decay slope and long-term historical average decay slope Calculate the decay acceleration index .

[0030] Furthermore, the classification rules for the residual value assessment model in S5 are as follows:

[0031] If the normalized degradation rate ratio of the target photovoltaic module is A preset first attenuation threshold, and its attenuation acceleration index. The preset acceleration judgment threshold determines that although the target photovoltaic module has a certain degree of power degradation, it can still continue to serve, and the target photovoltaic module is marked as being able to be reused in a tiered manner.

[0032] If the degradation acceleration index of the target photovoltaic module Accelerated determination threshold, or normalized decay rate ratio If the preset second attenuation threshold is reached, it is determined that the target photovoltaic module has entered a stage of severe performance degradation, has no value for secondary use, and is marked as a recommended material for recycling.

[0033] For the few cases that do not meet the above two conditions, the target photovoltaic module will be marked as requiring manual evaluation, prompting manual judgment and decision-making.

[0034] Furthermore, for the target photovoltaic module that can be used in a tiered manner, the equivalent output power under the standard test conditions calculated in the last step of S2.1 is extracted and used as the reference power. The estimated residual value range with reference power and degradation acceleration index is output.

[0035] For target photovoltaic modules that are recommended for material recycling, the residual value assessment model calls the internally updated market recycling unit price database, and combines it with the model and specifications recorded when the target photovoltaic module leaves the factory. It then retrieves the typical material composition content table preset for the target photovoltaic module, multiplies the content of each material by the current market recycling unit price in the market recycling unit price database, and sums them up to output the estimated residual value range based on the material recycling value.

[0036] The present invention has the following advantages: By introducing a degradation acceleration index and classifying photovoltaic modules based on the ratio of the degradation acceleration index to the normalized degradation rate, the present invention allows for the continued retention of photovoltaic modules with stable remaining power generation capacity and recommends the recycling of photovoltaic modules with no utilization value. This fundamentally avoids the waste of resources caused by a one-size-fits-all replacement and improves the full life cycle benefits of power plant assets.

[0037] Meanwhile, by using the equivalent output power under standard test conditions as the benchmark power and combining it with the degradation acceleration index to assess the residual value of photovoltaic modules that can be reused in stages and those that require material recycling, a residual value assessment method based on the real full life cycle operation data of photovoltaic modules was constructed, thereby improving the level of refinement and economic benefits of mid-to-late stage asset management of photovoltaic power plants.

[0038] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. Attached Figure Description

[0039] To more intuitively illustrate the prior art and this application, exemplary drawings are provided below. It should be understood that the specific shapes and structures shown in the drawings should not generally be regarded as limiting conditions for implementing this application; for example, based on the technical concept disclosed in this application and the exemplary drawings, those skilled in the art are able to easily make conventional adjustments or further optimizations to the addition / reduction / classification, specific shapes, positional relationships, connection methods, size ratios, etc. of certain units (components).

[0040] Figure 1 This is a flowchart illustrating the implementation of a photovoltaic module full lifecycle identity management method according to the present invention. Detailed Implementation

[0041] The following specific embodiments illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. It should be understood that these embodiments are merely for further explanation of the present invention and should not be construed as limiting the scope of protection of the present invention. Technical engineers in the field can make some non-essential improvements and adjustments to the present invention based on the above-described content. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0042] Please see Figure 1 A method for full lifecycle identity management of photovoltaic modules includes the following steps:

[0043] S1: Identity Initialization and Full Lifecycle Documentation Data Establishment: RFID tags are set on photovoltaic modules at the factory to store unique identification codes and factory rated power. During power plant operation, the back-end management system uses the identification code as an index to continuously record the real-time operating location of the photovoltaic module, the time and reason for each disassembly, installation, and maintenance event, forming full lifecycle documentation data. This facilitates the establishment of a unique identification and event record document for each photovoltaic module throughout its entire lifecycle, fundamentally solving the data foundation problem of power plants being unable to make refined decommissioning decisions in the mid-to-late stages due to the lack of historical information on photovoltaic modules.

[0044] Fixed RFID readers and writers are installed at the entrance of the power plant's material warehouse, in each array support area, and at maintenance workstations, as well as handheld reading terminals carried by maintenance personnel. When any photovoltaic module enters the effective reading range of any reader or writer, the reader or writer automatically captures the module's identification code and uploads it in real time to the back-end management system via wired or wireless network, along with the reader or writer's own serial number, location information, and current timestamp.

[0045] After receiving event data, the backend management system creates or appends an event type record for that module in the history database, using the identification code as the primary key. The event type is automatically determined by the system based on the reader's location; for example, an inbound event is triggered when a reader at the material warehouse entrance; an installation event is triggered when the module is first read in the array bracket area; a disassembly event is triggered when the same module reappears in the warehouse after disappearing from the bracket area; if a disassembly event is accompanied by a maintenance work order number, it is automatically marked as a maintenance event. The time, location coordinates, and associated work order information of each event are completely recorded, forming full lifecycle document data.

[0046] S2: Using the identification code as an index, continuously collect full lifecycle documentation data for the target photovoltaic module and calculate three data indicators: current power degradation rate, cumulative number of repairs, and cumulative number of abnormal stress events. This facilitates the continuous binding and updating of these three key indicators—degradation performance, repair frequency, and abnormal stress—with the identification code, forming the data foundation for assessing the true remaining value of each module.

[0047] The specific steps of S2 are as follows:

[0048] S2.1: Based on the DC output power and backsheet temperature of the photovoltaic module collected at fixed intervals, and the real-time solar irradiance provided by the weather station, the measured power under any field conditions is uniformly converted into the equivalent output power under standard test conditions (STC: irradiance 1000 W / m², cell temperature 25 ℃), and compared with the rated power recorded at the factory to calculate the current power degradation rate. The calculation formula is as follows:

[0049]

[0050] in, This is the factory rated power. Equivalent output power under standard test conditions The calculation formula is as follows:

[0051]

[0052] in, To measure the DC output power of the photovoltaic module; Real-time solar irradiance; This is the preset power temperature coefficient; This refers to the backplate temperature.

[0053] In S2.1, the DC output power and backplane temperature are acquired through DC current sensors, DC voltage sensors, and temperature sensors installed in the junction box of each photovoltaic module. The DC current and DC voltage sensors synchronously measure the DC current and DC voltage output of the photovoltaic module at fixed intervals (e.g., every 5 or 15 minutes), and the measured DC output power of the module is calculated in real time by multiplying the two values. The temperature sensor measures the surface temperature of the photovoltaic module's backplane at fixed intervals. Finally, the acquired DC output power and temperature data, along with the device's unique identifier, are uploaded to the backend management system via the device's built-in power line carrier communication, completing the association with the corresponding photovoltaic module's identification code.

[0054] Temperature sensors measure the surface temperature of the photovoltaic module backsheet at fixed intervals, and the data is ultimately collected by the backend management system, serving as the input parameter for cell temperature in the standard test condition correction calculation.

[0055] S2.2: Retrieve event records marked as maintenance in the full lifecycle document data using the identification code as an index, and automatically count according to preset statistical rules (such as only counting formal maintenance such as junction box failure, PID repair, glass replacement, etc., excluding routine cleaning and visual inspection) to determine the cumulative number of maintenance times.

[0056] S2.3: Using vibration sensors installed on the module frame and acoustic shock wave sensors in the power station, the physical stress data exerted on the module by the external environment is captured in real time. This physical stress is then compared with preset fatigue and shock resistance safety thresholds for different module models. When the physical stress data collected by the sensors exceeds the safety threshold, an abnormal stress event that may cause latent damage to the photovoltaic module is determined, and the event is automatically recorded and accumulated to obtain the cumulative number of abnormal stress events.

[0057] S3: Normalize the three data indicators output from S2 by dividing them by the preset failure threshold, maximum allowable number of repairs, and maximum allowable number of stress events for the photovoltaic module, respectively, to obtain three normalized attenuation rate ratios between 0 and 1. Normalized maintenance frequency ratio and the normalized ratio of abnormal stress events Then dynamically calculate the remaining economic life index of the photovoltaic module. Remaining economic life index The value ranges from 0 to 1. The closer it is to 1, the better the overall condition of the photovoltaic module and the higher the economic surplus; the closer it is to 0, the sooner the economic lifespan of the photovoltaic module will be exhausted. The calculation formula is as follows:

[0058]

[0059] in, , and The preset weighting coefficients, + + =1.

[0060] Normalized decay ratio The closer to 1, the more severe the degradation; normalized maintenance ratio A value closer to 1 indicates a higher maintenance frequency; Normalized abnormal stress event ratio The closer it is to 1, the more abnormal stress it experiences.

[0061] S4: The remaining economic life index output by S3 With the preset economic exit threshold Compare: If This indicates that continuing to retain the photovoltaic module would negatively impact the overall revenue of the power station, and the process for generating a pre-decommissioning notice will be initiated immediately. Details are as follows:

[0062] First, a snapshot of the target photovoltaic module's instantaneous state at the trigger moment is obtained, including the current power degradation rate, cumulative number of repairs, and cumulative number of abnormal stress events. Simultaneously, a pre-retirement notification is automatically generated. The pre-retirement notification includes the following fields: unique identification code of the photovoltaic module, array and support location coordinates, trigger timestamp, and current remaining economic life index. Numerical values, trigger thresholds, and instantaneous state snapshots.

[0063] After the decommissioning pre-notification is generated, it is distributed in parallel through multiple channels via a pre-configured message push interface. On the large screen interface in the power plant's central monitoring room, the corresponding element of the target photovoltaic module in the power plant's geographic information system map is automatically highlighted and rendered as a flashing red, and a floating information window pops up to display a snapshot of the instantaneous status. Simultaneously, the notification is pushed to the handheld terminals of all authorized maintenance personnel via the internal message bus. Upon receiving the push, the terminal triggers an audio-visual and vibration alert. Maintenance personnel can click on the notification to directly jump to the full lifecycle document data page of the target photovoltaic module to view complete data.

[0064] like This indicates that the overall health of the target photovoltaic module is normal, and the expected benefits of continuing to operate it in the power plant remain intact. The sum of its maintenance costs and mismatch losses has not yet reached the critical point of its economic life. At this time, the background management system does not trigger a decommissioning pre-notification, and the target photovoltaic module remains in normal operation. Data collection and remaining economic life index updates for the module continue according to the fixed cycle from S2 to S3 until the calculation results of a future cycle are displayed. Then, the S4 retirement pre-notification trigger process will begin.

[0065] Preset economic exit threshold Analysis using the built-in economic model reveals a critical point where the revenue from continued operation of the photovoltaic module is exactly equal to the sum of its maintenance costs and mismatch losses. The economic model works as follows:

[0066] 1) Calculate the expected return if the target photovoltaic module continues to operate for another year. This benefit is derived from the equivalent output power of the target photovoltaic module. The historical average annual effective radiation hours at the installation location And actual grid connection electricity price The calculation formula is as follows, jointly determined:

[0067]

[0068] 2) By combining the pre-set planned maintenance cost allocation with the random maintenance risk reserve based on the failure probability through the historical maintenance frequency record of the target photovoltaic module, the annual maintenance expenditure is obtained;

[0069] 3) Determine the mismatch loss: When the target photovoltaic module's maximum power point current is lower than that of the normal modules in the same string due to degradation, the difference between the ideal total power without mismatch and the actual total power is calculated through string electrical simulation as the total mismatch power loss. Then, the total loss is allocated to the target photovoltaic module according to the proportion of the target photovoltaic module's current deviation value to the sum of the current deviation values ​​of all modules in the string, to obtain the mismatch power loss it should bear. Finally, multiply this by the average annual effective irradiance hours and the feed-in tariff at that location to obtain the annual mismatch loss cost.

[0070] 4) Define the expected returns for the target photovoltaic module to continue operating for another year. It equals the sum of annual maintenance expenditure and annual mismatch loss cost; then extract the remaining economic life index corresponding to the critical state point. The numerical value, which is calibrated as the economic exit threshold for this photovoltaic module.

[0071] .

[0072] S5: For the target photovoltaic modules that triggered the decommissioning pre-notification in S4, retrieve the full lifecycle document data of the module up to the triggering time, classify them according to the specific characteristics of their performance degradation, and call the corresponding residual value assessment model to output the estimated residual value range with accompanying data. This step further analyzes its inherent degradation mode to avoid blindly scrapping modules with stable remaining power generation value, thereby solving the problem of resource waste and asset undervaluation caused by the "one-size-fits-all" replacement in existing technologies.

[0073] S5 includes the following steps:

[0074] S5.1: Using the historical sequence value of the current power decay rate D continuously collected and stored in the full life cycle document data in S2.1, calculate the decay acceleration index, which represents the performance degradation trend of the target photovoltaic module;

[0075] First, extract multiple data points of the current power degradation rate D of the target photovoltaic module from the full life cycle document data within a preset recent statistical window (e.g., the past 6 months), calculated according to S2.1; then perform a first-order linear fit on all data points within this recent statistical window to obtain the recent degradation slope. .

[0076] Then, extract all historical current power degradation rate (D) data points of the target photovoltaic module from its stable service period (e.g., from the second year of service to avoid interference from early light-induced degradation (LID)) to the start date of the recent statistical window from the full life cycle document data, and perform first-order linear fitting to obtain the long-term historical average degradation slope. And calculate the decay acceleration index. The calculation formula is as follows:

[0077]

[0078] S5.2: The degradation acceleration index A of the target photovoltaic module and the ratio of the normalized degradation rate of the target photovoltaic module at the current moment are classified using a pre-defined residual value assessment model. The classification rules of the residual value assessment model are as follows:

[0079] If the normalized degradation rate ratio of the target photovoltaic module is A preset first attenuation threshold, and its attenuation acceleration index. The preset acceleration threshold determines that although the target photovoltaic module has experienced some power degradation, its degradation rate trend remains relatively flat, indicating that its performance within its remaining lifespan is predictable and it is still suitable for continued operation in low-power scenarios. In this case, the target photovoltaic module is marked as capable of being reused in a tiered manner.

[0080] For target photovoltaic modules that can be reused in stages, extract the equivalent output power under the standard test conditions calculated in the last step of S2.1, and use it as the reference power. Output the estimated residual value range with the reference power and the degradation acceleration index.

[0081] If the degradation acceleration index of the target photovoltaic module Accelerate the determination threshold, or its normalized decay rate ratio If the preset second attenuation threshold (the second attenuation threshold is greater than the first attenuation threshold) is set, the target photovoltaic module is determined to have entered a stage of severe performance degradation, has no stable remaining power generation capacity, and has no value for secondary utilization, and is marked as a recommended material for recycling.

[0082] For target photovoltaic modules that are recommended for material recycling, the residual value assessment model calls the internally updated market recycling unit price database, and combines it with the model and specifications recorded when the target photovoltaic module leaves the factory. It then retrieves the typical material composition content table preset for the target photovoltaic module, multiplies the content of each material by the current market recycling unit price in the market recycling unit price database, and sums them up to output the estimated residual value range based on the material recycling value.

[0083] For the few cases that do not meet the above two conditions, they will be marked as requiring manual evaluation. The full life cycle documentation data of the target photovoltaic module, the remaining economic life index R, the degradation acceleration index A, and all relevant data will be packaged and prompted for manual judgment and decision-making.

[0084] Once the above-mentioned disposal classification suggestions are actually implemented (whether it is for tiered utilization, material recycling and dismantling, or manual evaluation), the maintenance personnel scan the RFID tag of the dismantled photovoltaic module with a handheld RFID reader and writer terminal, and confirm the execution of the decommissioning disposal operation on the terminal interface.

[0085] This invention introduces a degradation acceleration index and differentiates photovoltaic modules based on the ratio of the degradation acceleration index to the normalized degradation rate. This allows for the continued retention of photovoltaic modules with stable remaining power generation capacity and the recycling of photovoltaic modules with no utilization value. This fundamentally avoids the waste of resources caused by a one-size-fits-all replacement and improves the full life cycle benefits of power plant assets.

[0086] Meanwhile, by using the equivalent output power under standard test conditions as the benchmark power and combining it with the degradation acceleration index to assess the residual value of photovoltaic modules that can be reused in stages and those that require material recycling, a residual value assessment method based on the real full life cycle operation data of photovoltaic modules was constructed, thereby improving the level of refinement and economic benefits of mid-to-late stage asset management of photovoltaic power plants.

[0087] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for full lifecycle identity management of photovoltaic modules, characterized in that, Includes the following steps: S1: Set RFID tags when photovoltaic modules leave the factory, store unique identification codes and factory rated power, and continuously record the real-time operating location of the target photovoltaic module, the time and reason of each disassembly, installation and maintenance event, forming full life cycle document data; S2: Continuously collect full lifecycle document data of the target photovoltaic module using the identification code as an index and calculate three data indicators: current power attenuation rate, cumulative number of repairs, and cumulative number of abnormal stress events; S3: Normalize the three data indicators output by S2, and dynamically calculate the remaining economic life index of the photovoltaic module based on the three processed data indicators. S4: Compare the remaining economic life index output by S3 with the preset economic exit threshold, and determine whether to output a retirement pre-notification based on the comparison result; S5: For the target photovoltaic module that triggered the decommissioning pre-notification in S4, retrieve the full life cycle document data of the target module up to the triggering time, classify it for differentiated treatment, call the corresponding residual value assessment model, and output the estimated residual value range with accompanying data proof.

2. The photovoltaic module full lifecycle identity management method according to claim 1, characterized in that, The specific steps of S2 are as follows: S2.1: Based on the DC output power and backsheet temperature of the photovoltaic module collected at fixed intervals, and the real-time solar irradiance provided by the weather station, the measured power under any field conditions is uniformly converted into standard test conditions, and the current power attenuation rate is calculated. S2.2: Retrieve event records marked as maintenance in the full lifecycle document data using the identification code as an index, automatically count according to preset statistical rules, and determine the cumulative number of maintenance times; S2.3: Using vibration sensors installed on the module frame and acoustic shock wave sensors in the power station, the physical stress data exerted on the module by the external environment is captured in real time, and automatically recorded and accumulated to obtain the cumulative number of abnormal stress events.

3. The photovoltaic module full lifecycle identity management method according to claim 1, characterized in that, The remaining economic life index The value ranges from 0 to 1. The closer it is to 1, the better the overall condition of the photovoltaic module and the higher the economic surplus; the closer it is to 0, the sooner the economic lifespan of the photovoltaic module will be exhausted. The calculation formula is as follows: in, , and The ratio of the preset weighting coefficients to the normalized decay rate. This is the normalized decay rate ratio. This represents the normalized ratio of maintenance frequency. This represents the normalized ratio of abnormal stress events.

4. The photovoltaic module full lifecycle identity management method according to claim 1, characterized in that, If the remaining economic life index is determined in S4 Preset economic exit threshold This indicates that continuing to retain the target photovoltaic modules would result in a loss of overall power plant revenue, and the process of generating a pre-decommissioning notice should be initiated immediately. If the remaining economic life index Preset economic exit threshold This indicates that the overall health status of the target photovoltaic module is normal and it is in normal operating condition.

5. The photovoltaic module full lifecycle identity management method according to claim 1, characterized in that, S5 includes the following steps: S5.1: Using the historical sequence value of the current power decay rate D continuously collected and stored in the full life cycle document data in S2.1, calculate the decay acceleration index, which represents the performance degradation trend of the target photovoltaic module; S5.2: Classify the degradation acceleration index of the target photovoltaic module and the ratio of the normalized degradation rate of the target photovoltaic module at the current moment through the preset residual value assessment model, and output the estimated residual value range.

6. The photovoltaic module full lifecycle identity management method according to claim 5, characterized in that, The specific content of S5.1 is as follows: 1) Extract multiple data points of the current power degradation rate of the target photovoltaic module from the full life cycle document data within a preset recent statistical window, calculated according to S2.1; 2) Perform a first-order linear fit on all data points within the recent statistical window to obtain the recent decay slope. ; 3) Extract all historical current power degradation rate data points of the target photovoltaic module from the stable service period to the start date of the recent statistical window from the full life cycle document data, perform first-order linear fitting, and obtain the long-term historical average degradation slope. ; 4) Based on recent decay slope and long-term historical average decay slope Calculate the decay acceleration index .

7. The photovoltaic module full lifecycle identity management method according to claim 1, characterized in that, The classification rules for the residual value assessment model in S5 are as follows: If the normalized degradation rate ratio of the target photovoltaic module is A preset first attenuation threshold, and its attenuation acceleration index. The preset acceleration judgment threshold determines that although the target photovoltaic module has a certain degree of power degradation, it can still continue to serve, and the target photovoltaic module is marked as being able to be reused in a tiered manner. If the degradation acceleration index of the target photovoltaic module Accelerated determination threshold, or normalized decay rate ratio If the preset second attenuation threshold is reached, the target photovoltaic module is determined to have entered a stage of severe performance degradation, has no value for reuse, and is marked as a recommended material for recycling.

8. The photovoltaic module full lifecycle identity management method according to claim 7, characterized in that, For the target photovoltaic module that can be used in a tiered manner, extract the equivalent output power under the standard test conditions calculated in the last time in S2.1, and use it as the reference power to output the estimated residual value range with the reference power and the degradation acceleration index. For target photovoltaic modules that are recommended for material recycling, the residual value assessment model calls the internally updated market recycling unit price database, and combines it with the model and specifications recorded when the target photovoltaic module leaves the factory. It then retrieves the typical material composition content table preset for the target photovoltaic module, multiplies the content of each material by the current market recycling unit price in the market recycling unit price database, and sums them up to output the estimated residual value range based on the material recycling value.

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