Method for improving performance of light absorption layer of perovskite solar cell

Abstract

The invention discloses a method for improving the performance of a light absorption layer of a perovskite solar cell, and relates to the technical field of improving the performance of the light absorption layer of the perovskite solar cell. The hole migration rate is matched with the light intensity transient rhythm, and the recombination loss caused by carrier accumulation is thoroughly eliminated; the reinforcement learning agent generates a voltage instruction in real time through the light intensity change rate, actively reconstructs an energy band under millisecond light intensity fluctuation such as cloud layer shielding, and breaks through the physical limit of response lag of traditional static interface engineering; according to the photon flow correction algorithm, micro-cavity etching coordinates are generated based on defect distribution, so that incident photons accurately avoid deep defect clusters, meanwhile, the hole array with the gradually-changed taper angles guides photon path optimization, and the light capturing capacity of the thick-film perovskite is remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of improving the performance of a light-absorbing layer of a perovskite solar cell, and in particular to a method for improving the performance of a light-absorbing layer of a perovskite solar cell. Background Art

[0002] Currently, high-performance perovskite cells generally use organic-inorganic composite light-absorbing layers (such as FA / MA / Cs mixed cation systems). The interface energy level matching between organic-inorganic composite light-absorbing layers and hole transport layers (such as Spiro-OMeTAD and PTAA) has become a key bottleneck. Current interface engineering strategies (mostly using energy-level-adaptive molecular intercalation to fine-tune the band position through molecular dipoles) or gradient doping techniques (such as gradually doping Li-TFSI in the transport layer) are used to smooth the band curvature.

[0003] In strong light transient scenarios, such as millisecond-level light intensity fluctuations caused by cloud cover, the interface between the perovskite absorption layer and the transmission layer is exposed to the hole injection hysteresis effect. The energy barrier formed by the energy level mutation (>0.5eV steep drop) causes the accumulation of photogenerated holes at the interface. At this time, traditional molecular intercalation cannot respond to dynamic carrier concentration changes due to the fixed energy level offset, and the gradient doping layer is limited by the solid solubility of the material, making it difficult to form a sufficiently wide transition zone, resulting in a surge in the interface recombination rate. The efficiency fluctuations of the device in actual outdoor operation are significantly amplified.

[0004] To alleviate this problem, some solutions introduce dynamic regulation mechanisms of functional molecules to enable the interface energy band to adaptively bend with light intensity. However, such solutions rely on the molecular diffusion rate, and the response delay is still in the second level, which is much slower than the millisecond-level change in light intensity. Therefore, there is an urgent need for a solution to improve the performance of the perovskite solar cell light-absorbing layer to solve this problem. Summary of the Invention

[0005] In view of the above existing problems, the present invention is proposed.

[0006] The present invention provides a method for improving the performance of the light-absorbing layer of perovskite solar cells to solve the problem that current interface engineering cannot solve the hole injection hysteresis and energy level mutation caused by strong light transients, resulting in outdoor efficiency fluctuations.

[0007] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0008] An embodiment of the present invention provides a method for improving the performance of a light-absorbing layer of a perovskite solar cell, which comprises:

[0009] Step S1, implanting a piezoelectric ceramic array at the interface between the perovskite light absorption layer and the hole transport layer;

[0010] Step S2, monitoring the rate of change of incident light intensity in real time through a light intensity sensor;

[0011] Step S3, driving the reinforcement learning agent to generate a voltage instruction set based on the monitoring data;

[0012] Step S4: controlling the piezoelectric ceramic array to perform deformation to reconstruct the interface energy band gradient.

[0013] As a preferred embodiment of the method for improving the light-absorbing layer performance of a perovskite solar cell according to the present invention, the piezoelectric ceramic array comprises:

[0014] Staggered arrangement of lead zirconate titanate PZT units;

[0015] The PZT unit is covered with a poly(3,4-ethylenedioxythiophene)-polystyrenesulfonic acid (PEDOT:PSS) ion-conducting layer.

[0016] As a preferred embodiment of the method for improving the performance of the light-absorbing layer of a perovskite solar cell according to the present invention, the reinforcement learning agent in step S3 comprises:

[0017] The state space is defined as a six-dimensional vector of the rate of change of light intensity and the gradient of carrier concentration;

[0018] The output of the motion space is a combination of piezoelectric unit driving voltages.

[0019] As a preferred embodiment of the method for improving the performance of the light-absorbing layer of a perovskite solar cell according to the present invention, in step S3, in order to make the reinforcement learning agent converge to a high-yield action in the three-dimensional coupling space of the optoelectronic structure, the real-time efficiency is first calculated, and then multiple penalty terms are constructed, and finally an instant reward that is adaptive to the light intensity is given. The steps include:

[0020] When entering the decision time t, first use the monitoring quantity to obtain the instantaneous photoelectric conversion efficiency:

[0021]

[0022] Among them, η t represents the photoelectric conversion efficiency at time t, t represents the current time step, J sc,t Indicates the short-circuit current density at time t, the subscript sc indicates short circuit, V oc,t Indicates the open circuit voltage at time t, the subscript oc indicates open circuit, FF t represents the filling factor at time t, P in,t represents the incident light power density at time t;

[0023] Define relative efficiency gain:

[0024]

[0025] Where Δη t Represents the relative efficiency gain, ηbase represents the reference efficiency;

[0026] Under the condition that energy consumption and energy band matching are both paid attention to, construct instant rewards:

[0027]

[0028] Among them, R t represents the reward at time t, γ1(t), γ2(t), and γ3(t) represent the adaptive adjustment functions of the three weights over time t, and V t represents the driving voltage vector of the piezoelectric unit with a length of n, Represents the square of the vector's two-norm, which is used to measure the driving energy consumption. represents the gradient of the interface conduction band along the thickness direction x at time t, E target represents the target step-shaped conduction band curve, ||·||1 represents a norm, which is used to quantify the conduction band mismatch;

[0029] The weight changes with the light intensity to reflect the adaptive control intention:

[0030]

[0031] Among them, γ i (t) represents the weight of the i-th item, γ i,0 represents the static benchmark weight, κ i represents the adjustment coefficient, tanh(·) represents the hyperbolic tangent function, Indicates the rate of change of incident light intensity at time t, I ref represents the normalized reference light intensity, and the subscript i corresponds to the three types of weights;

[0032] If efficiency drops and the drive energy consumption exceeds the threshold, a failure penalty is imposed:

[0033] When Δη t <0 and ||V t ||2>V lim , R t ←R t -λ fail ,

[0034] Among them, λ fail represents the failure penalty constant, V lim Indicates the voltage safety threshold.

[0035] As a preferred embodiment of the method for improving the light-absorbing layer performance of a perovskite solar cell according to the present invention, the PEDOT:PSS ion-conducting layer contains:

[0036] Tungsten oxide electrochromic nanoparticles;

[0037] The nanoparticles have a bimodal particle size distribution;

[0038] The staggered arrangement of PZT units is as follows:

[0039] The units are in a hexagonal close-packed topology;

[0040] The distance between adjacent cells is less than half the laser wavelength.

[0041] As a preferred solution of the method for improving the performance of the light-absorbing layer of a perovskite solar cell according to the present invention, the energy band gradient reconstruction in step S4 includes:

[0042] Adjust the physical thickness of the interface by expanding and contracting the piezoelectric ceramics;

[0043] Changing the local work function through redox reactions of electrochromic particles;

[0044] The two work together to form a stepped band structure.

[0045] As a preferred solution of the method for improving the performance of the light-absorbing layer of a perovskite solar cell according to the present invention, the driving voltage combination is implemented as follows:

[0046] Partition control of piezoelectric ceramic array;

[0047] Different voltages are applied to adjacent partitions to form a potential difference step;

[0048] Partition control strategies include:

[0049] Dividing the piezoelectric array into annular concentric regions;

[0050] The domain boundaries are aligned with the perovskite grain boundaries.

[0051] As a preferred embodiment of the method for improving the light-absorbing layer performance of a perovskite solar cell according to the present invention, the method further comprises:

[0052] Based on the defect distribution map of the perovskite light absorbing layer;

[0053] The microcavity etching coordinates are generated by the photon flux density correction algorithm.

[0054] As a preferred solution of the method for improving the performance of the light-absorbing layer of a perovskite solar cell according to the present invention, in the process of generating the microcavity etching coordinates by the photon flux density correction algorithm, the photon flux density field is first reconstructed and then mapped into an etching coordinate set, including:

[0055] Calculate the baseline photon flux density at a given two-dimensional plane coordinate (u, v):

[0056]

[0057] Where Φ0(u,v) represents the baseline photon flux density, λ min represents the lower limit of the spectrum, λ max Indicates the upper limit of the spectrum, S λ (u,v) represents the surface distribution spectral irradiance at wavelength λ, Δλ represents the discrete wavelength step, h represents Planck's constant, c represents the speed of light in vacuum, λ represents the wavelength variable of the summation index, u,v represent the horizontal and vertical coordinates of the plane respectively;

[0058] Take the global average to get the target photon flow Among them, Φ target represents the target photon flux density, A s represents the cross-sectional area of ​​the light-absorbing layer;

[0059] Photon flow deviation: ΔΦ(u,v)=Φ0(u,v)-Φ target , where ΔΦ(u,v) represents the deviation photon flux density;

[0060] Treating the deviation as a source term, solve for the Poisson potential function:

[0061]

[0062] in, represents the two-dimensional Laplace operator, ψ(u,v) represents the potential function, and α represents the photon diffusion correction coefficient;

[0063] The microcavity center density is obtained based on the potential function gradient:

[0064]

[0065] Among them, ρ c (u,v) represents the center surface density of the microcavity, β represents the spectral defect gain coefficient, represents the potential function gradient vector;

[0066] Generate an etching coordinate set using threshold sampling:

[0067]

[0068] in, represents the microcavity etching coordinate set, u k ,v k represents the coordinates of the kth etching center, ρ th represents the density threshold;

[0069] Where,

[0070]

[0071] Where α0 represents the base diffusion coefficient, χ represents the defect coupling weight, and σ drepresents the local defect surface area density, σ ref represents the reference defect density,

[0072]

[0073] Where β0 represents the reference gain coefficient, E g represents the bandgap width of the absorption layer, k B represents the Boltzmann constant, T s Indicates the etching surface temperature.

[0074] As a preferred solution of the method for improving the light-absorbing layer performance of a perovskite solar cell according to the present invention, the microcavity etching adopts:

[0075] Femtosecond laser pulses are applied to the upstream region of the defect cluster;

[0076] Forming a hole array with tapered angle.

[0077] The beneficial effects of the present invention are as follows: in the present invention, the step deformation of the piezoelectric ceramic array and the redox of the electrochromic particles synergistically transform the interface energy level mutation into a multi-level gradient structure, so that the hole migration rate matches the transient rhythm of the light intensity, and the recombination loss caused by carrier accumulation is completely eliminated; the reinforcement learning agent generates voltage instructions in real time through the light intensity change rate, and actively reconstructs the energy band under millisecond-level light intensity fluctuations such as cloud cover, breaking through the physical limit of the response lag of traditional static interface engineering; the photon flow correction algorithm generates microcavity etching coordinates based on the defect distribution, so that the incident photons accurately avoid deep defect clusters, and at the same time the tapered angle gradient hole array guides the photon path optimization, significantly improving the light capture ability of thick-film perovskite.

[0078] In addition, the energy consumption penalty term and voltage safety threshold constraint in the reward function ensure that the electrodeformation process operates within the physical tolerance range of the device, avoiding secondary damage caused by dynamic regulation. BRIEF DESCRIPTION OF THE DRAWINGS

[0079] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0080] Figure 1 This is a schematic flow chart of the method for improving the performance of the light-absorbing layer of a perovskite solar cell in Example 1. DETAILED DESCRIPTION

[0081] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0082] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0083] Secondly, the term "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in various places throughout this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive of other embodiments.

[0084] Example 1, with reference to Figure 1 This embodiment provides a method for improving the performance of a perovskite solar cell light absorption layer, comprising the following steps:

[0085] Step S1, implanting a piezoelectric ceramic array at the interface between the perovskite light absorption layer and the hole transport layer;

[0086] Step S2, monitoring the rate of change of incident light intensity in real time through a light intensity sensor;

[0087] Step S3, driving the reinforcement learning agent to generate a voltage instruction set based on the monitoring data;

[0088] The reinforcement learning agent in step S3 includes:

[0089] The state space is defined as a six-dimensional vector of the rate of change of light intensity and the gradient of carrier concentration;

[0090] The output of the motion space is a combination of the driving voltages of the piezoelectric units;

[0091] In step S3, in order to make the reinforcement learning agent converge to a high-yield action in the three-dimensional coupling space of the optoelectronic structure, the real-time efficiency is first calculated, and then multiple penalty terms are constructed, and finally an instant reward that adapts to the light intensity is given. The steps include:

[0092] When entering the decision time t, first use the monitoring quantity to obtain the instantaneous photoelectric conversion efficiency:

[0093]

[0094] Among them, η t represents the photoelectric conversion efficiency at time t, t represents the current time step, J sc,t Indicates the short-circuit current density at time t, the subscript sc indicates short circuit, V oc,t Indicates the open circuit voltage at time t, the subscript oc indicates open circuit, FF t represents the filling factor at time t, P in,trepresents the incident light power density at time t;

[0095] Define relative efficiency gain:

[0096]

[0097] Where Δη t Represents the relative efficiency gain, η base represents the reference efficiency;

[0098] Under the condition that energy consumption and energy band matching are both paid attention to, construct instant rewards:

[0099]

[0100] Among them, R t represents the reward at time t, γ1(t), γ2(t), and γ3(t) represent the adaptive adjustment functions of the three weights over time t, and V t represents the driving voltage vector of the piezoelectric unit with a length of n, Represents the square of the vector's two-norm, which is used to measure the driving energy consumption. represents the gradient of the interface conduction band along the thickness direction x at time t, E target represents the target step-shaped conduction band curve, ||·||1 represents a norm, which is used to quantify the conduction band mismatch;

[0101] The weight changes with the light intensity to reflect the adaptive control intention:

[0102]

[0103] Among them, γ i (t) represents the weight of the i-th item, γ i,0 represents the static benchmark weight, κ i represents the adjustment coefficient, which is [0.5-1.5], tanh(·) represents the hyperbolic tangent function, Indicates the rate of change of incident light intensity at time t, I ref represents the normalized reference light intensity, and the subscript i corresponds to the three types of weights;

[0104] If efficiency drops and the drive energy consumption exceeds the threshold, a failure penalty is imposed:

[0105] When Δη t <0 and ||V t ||2>V lim , R t ←R t -λ fail ,

[0106] Among them, λ fail represents the failure penalty constant, V lim Indicates the voltage safety threshold;

[0107] Specifically, the reward function uses relative efficiency gain as the core driving force, the two-norm penalty precisely limits the energy consumption of the piezoelectric drive, the one-norm penalty synchronously corrects the band ladder, and the weight is smoothly adjusted with the light intensity through the hyperbolic tangent function. This reduces the deformation frequency in low-light mode and amplifies the efficiency sensitivity under strong light impact, making full use of the dynamic margin. The failure penalty explicitly incorporates the voltage safety threshold into the policy space, narrowing the invalid search domain and improving the stability of early training. The overall closed loop takes into account the triple coupling of energy structure and electricity.

[0108] The drive voltage combination is implemented as follows:

[0109] Partition control of piezoelectric ceramic array;

[0110] Different voltages are applied to adjacent partitions to form a potential difference step;

[0111] Partition control strategies include:

[0112] Dividing the piezoelectric array into annular concentric regions;

[0113] The domain boundaries are aligned with the perovskite grain boundaries;

[0114] Step S4, controlling the piezoelectric ceramic array to perform deformation to reconstruct the interface energy band gradient;

[0115] The piezoelectric ceramic array contains:

[0116] Staggered arrangement of lead zirconate titanate PZT units;

[0117] A poly(3,4-ethylenedioxythiophene)-polystyrenesulfonic acid (PEDOT:PSS) ion-conducting layer covering the surface of the PZT unit;

[0118] Dispersed in the PEDOT:PSS ion-conducting layer are:

[0119] Tungsten oxide electrochromic nanoparticles;

[0120] The nanoparticle size showed a bimodal distribution;

[0121] The staggered arrangement of PZT units is as follows:

[0122] The units are in a hexagonal close-packed topology;

[0123] The distance between adjacent units is less than half the laser wavelength;

[0124] Bimodal distribution nanoparticles contain:

[0125] The first peak particle size is spherical particles with a range of 20-40 nm;

[0126] The second peak particle size is 80-100 nm for polyhedral particles;

[0127] The band gradient reconstruction in step S4 includes:

[0128] Adjust the physical thickness of the interface by expanding and contracting the piezoelectric ceramics;

[0129] Changing the local work function through redox reactions of electrochromic particles;

[0130] The two work together to form a stepped band structure;

[0131] Methods for improving the performance of the light-absorbing layer of perovskite solar cells also include:

[0132] Based on the defect distribution map of the perovskite light absorbing layer;

[0133] The microcavity etching coordinates are generated by photon flux density correction algorithm;

[0134] In the process of generating microcavity etching coordinates through the photon flux density correction algorithm, the photon flux density field is first reconstructed and then mapped into an etching coordinate set, including:

[0135] Calculate the baseline photon flux density at a given two-dimensional plane coordinate (u, v):

[0136]

[0137] Where Φ0(u,v) represents the baseline photon flux density, λ min represents the lower limit of the spectrum, λ max Indicates the upper limit of the spectrum, S λ (u,v) represents the surface distribution spectral irradiance at wavelength λ, Δλ represents the discrete wavelength step, h represents Planck's constant, c represents the speed of light in vacuum, λ represents the wavelength variable of the summation index, u,v represent the horizontal and vertical coordinates of the plane respectively;

[0138] Take the global average to get the target photon flow Among them, Φ target represents the target photon flux density, A s represents the cross-sectional area of ​​the light-absorbing layer;

[0139] Photon flow deviation: ΔΦ(u,v)=Φ0(u,v)-Φ target , where ΔΦ(u,v) represents the deviation photon flux density;

[0140] Treating the deviation as a source term, solve for the Poisson potential function:

[0141]

[0142] in, represents the two-dimensional Laplace operator, ψ(u,v) represents the potential function, and α represents the photon diffusion correction coefficient;

[0143] The microcavity center density is obtained based on the potential function gradient:

[0144]

[0145] Among them, ρ c (u,v) represents the center surface density of the microcavity, β represents the spectral defect gain coefficient, represents the potential function gradient vector;

[0146] Generate an etching coordinate set using threshold sampling:

[0147]

[0148] in, represents the microcavity etching coordinate set, u k ,v k represents the coordinates of the kth etching center, ρ th represents the density threshold;

[0149] Where,

[0150]

[0151] Where α0 represents the base diffusion coefficient, χ represents the defect coupling weight, which is an empirical constant (0.1-0.3), and σ d represents the local defect surface area density, σ ref Indicates the reference defect density, which is the industry standard value.

[0152]

[0153] Where β0 represents the reference gain coefficient, E g represents the bandgap width of the absorption layer, k B represents the Boltzmann constant, T s Indicates the etching surface temperature;

[0154] Specifically, the actual photon flow is first projected onto a two-dimensional surface, and the target flux is set based on the global mean. The transmission difference caused by defects is smoothly expanded in the form of a potential function. The gradient amplitude directly reflects the strength of the photon bottleneck, thereby guiding the microcavity etching density. The diffusion coefficient α expands proportionally with the defect density. The potential field in the high-defect area changes slowly to prevent over-dense etching. The gain coefficient β is coupled with the temperature index through the band gap width to effectively suppress the over-etching phenomenon of high-temperature etching. Threshold sampling is used to screen the etching center online. The coordinate set is updated in real time with the incident spectrum or defect map without the need for recalibration. The overall process enables the microcavity structure to be precisely arranged upstream of the photon flow bottleneck, improving local carrier injection and providing more uniform incident conditions for subsequent piezoelectric deformation strategies.

[0155] Microcavity etching uses:

[0156] Femtosecond laser pulses are applied to the upstream region of the defect cluster;

[0157] forming a hole array with a tapered angle;

[0158] The implementation of cone angle gradient is as follows:

[0159] The cone angle increases from 45° to 75° along the photon incident direction;

[0160] The hole depth is positively correlated with the defect cluster size.

[0161] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for improving the performance of the light-absorbing layer of a perovskite solar cell, characterized in that: include, Step S1, implanting a piezoelectric ceramic array at the interface between the perovskite light absorption layer and the hole transport layer; Step S2, monitoring the rate of change of incident light intensity in real time through a light intensity sensor; Step S3, driving the reinforcement learning agent to generate a voltage instruction set based on the monitoring data; Step S4: controlling the piezoelectric ceramic array to perform deformation to reconstruct the interface energy band gradient.

2. The method for improving the light absorption performance of a perovskite solar cell according to claim 1, wherein: The piezoelectric ceramic array comprises: Staggered arrangement of lead zirconate titanate PZT units; The PZT unit is covered with a poly(3,4-ethylenedioxythiophene)-polystyrenesulfonic acid (PEDOT:PSS) ion-conducting layer.

3. The method for improving the light-absorbing layer performance of a perovskite solar cell according to claim 1, wherein: The reinforcement learning agent in step S3 includes: The state space is defined as a six-dimensional vector of the rate of change of light intensity and the gradient of carrier concentration; The output of the motion space is a combination of piezoelectric unit driving voltages.

4. The method for improving the light absorption layer performance of a perovskite solar cell according to claim 3, wherein: In step S3, in order to make the reinforcement learning agent converge to a high-yield action in the three-dimensional coupling space of the optoelectronic structure, the real-time efficiency is first calculated, and then multiple penalty terms are constructed, and finally an instant reward that adapts to the light intensity is given. The steps include: When entering the decision time t, first use the monitoring quantity to obtain the instantaneous photoelectric conversion efficiency: Among them, η t represents the photoelectric conversion efficiency at time t, t represents the current time step, J sc,t Indicates the short-circuit current density at time t, the subscript sc indicates short circuit, V oc,t Indicates the open circuit voltage at time t, the subscript oc indicates open circuit, FF t represents the filling factor at time t, P in,t represents the incident light power density at time t; Define relative efficiency gain: Where Δη t Represents the relative efficiency gain, η base represents the reference efficiency; Under the condition that energy consumption and energy band matching are both paid attention to, construct instant rewards: Among them, R t represents the reward at time t, γ1(t), γ2(t), and γ3(t) represent the adaptive adjustment functions of the three weights over time t, and V t represents the driving voltage vector of the piezoelectric unit with a length of n, Represents the square of the vector's two-norm, which is used to measure the driving energy consumption. represents the gradient of the interface conduction band along the thickness direction x at time t, E target represents the target step-shaped conduction band curve, ||·||1 represents a norm, which is used to quantify the conduction band mismatch; The weight changes with the light intensity to reflect the adaptive control intention: Among them, γ i (t) represents the weight of the i-th item, γ i,0 represents the static benchmark weight, κ i represents the adjustment coefficient, tanh(·) represents the hyperbolic tangent function, Indicates the rate of change of incident light intensity at time t, I ref represents the normalized reference light intensity, and the subscript i corresponds to the three types of weights; If efficiency drops and the drive energy consumption exceeds the threshold, a failure penalty is imposed: When Δη t < 0 and ||V t ||2 > V lim , R t ←R t -λ fail , Among them, λ fail represents the failure penalty constant, V lim Indicates the voltage safety threshold.

5. The method for improving the light-absorbing layer performance of a perovskite solar cell according to claim 3, wherein: Dispersed in the PEDOT:PSS ion-conducting layer are: Tungsten oxide electrochromic nanoparticles; The nanoparticles have a bimodal particle size distribution; The staggered arrangement of PZT units is as follows: The units are in a hexagonal close-packed topology; The distance between adjacent cells is less than half the laser wavelength.

6. The method for improving the light absorption layer performance of a perovskite solar cell according to claim 1, wherein: The band gradient reconstruction in step S4 includes: Adjust the physical thickness of the interface by expanding and contracting the piezoelectric ceramics; Changing the local work function through redox reactions of electrochromic particles; The two work together to form a stepped band structure.

7. The method for improving the light-absorbing layer performance of a perovskite solar cell according to claim 3, wherein: The driving voltage combination is executed as follows: Partition control of piezoelectric ceramic array; Different voltages are applied to adjacent partitions to form a potential difference step; Partition control strategies include: Dividing the piezoelectric array into annular concentric regions; The domain boundaries are aligned with the perovskite grain boundaries.

8. The method for improving the light-absorbing layer performance of a perovskite solar cell according to claim 1, wherein: Also includes: Based on the defect distribution map of the perovskite light absorbing layer; The microcavity etching coordinates are generated by photon flux density correction algorithm.

9. The method for improving the light-absorbing layer performance of a perovskite solar cell according to claim 8, wherein: In the process of generating microcavity etching coordinates by using the photon flux density correction algorithm, the photon flux density field is first reconstructed and then mapped into an etching coordinate set, including: Calculate the baseline photon flux density at a given two-dimensional plane coordinate (u, v): Where Φ0(u,v) represents the baseline photon flux density, λ min represents the lower limit of the spectrum, λ max Indicates the upper limit of the spectrum, S λ (u,v) represents the surface distribution spectral irradiance at wavelength λ, Δλ represents the discrete wavelength step, h represents Planck's constant, c represents the speed of light in vacuum, λ represents the wavelength variable of the summation index, u,v represent the horizontal and vertical coordinates of the plane respectively; Take the global average to get the target photon flow Among them, Φ target represents the target photon flux density, A s represents the cross-sectional area of ​​the light-absorbing layer; Photon flow deviation: ΔΦ(u,v)=Φ0(u,v)-Φ target , where ΔΦ(u,v) represents the deviation photon flux density; Treating the deviation as a source term, solve for the Poisson potential function: in, represents the two-dimensional Laplace operator, ψ(u,v) represents the potential function, and α represents the photon diffusion correction coefficient; The microcavity center density is obtained based on the potential function gradient: Among them, ρ c (u,v) represents the center surface density of the microcavity, β represents the spectral defect gain coefficient, represents the potential function gradient vector; Generate an etching coordinate set using threshold sampling: in, represents the microcavity etching coordinate set, u k ,v k represents the coordinates of the kth etching center, ρ th represents the density threshold; Where, Where α0 represents the base diffusion coefficient, χ represents the defect coupling weight, and σ d represents the local defect surface area density, σ ref represents the reference defect density, Where β0 represents the reference gain coefficient, E g represents the bandgap width of the absorption layer, k B represents the Boltzmann constant, T s Indicates the etching surface temperature.

10. The method for improving the light-absorbing layer performance of a perovskite solar cell according to claim 8, wherein: Microcavity etching uses: Femtosecond laser pulses are applied to the upstream region of the defect cluster; A hole array with a tapered angle is formed.

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