A multi-scenario implementation method for a photoelectric conversion device

Abstract

The present invention provides a multi-scenario implementation method for a photoelectric conversion device, belonging to the field of digital data processing technology. The method comprises: extracting features from all operating modes of the photoelectric conversion device to determine a feature vector for each operating mode; collecting environmental data of the current scene of the photoelectric conversion device and determining an estimated environmental vector for the current scene of the photoelectric conversion device based on the environmental data; determining the current operating mode of the current scene of the photoelectric conversion device based on all feature vectors and the estimated environmental vector; and automatically switching the photoelectric conversion device to the current operating mode to suit the current scene. This method can reduce manual intervention, automatically adapt to multiple scenarios, flexibly select power supply methods, improve operational efficiency and adaptability, enhance the device's ease of use, energy efficiency management, and battery life, and improve the ease of use and energy efficiency of the photoelectric conversion device.

Description

Technical Field

[0001] The present invention relates to the technical field of digital data processing, and in particular to a multi-scenario implementation method for a photoelectric conversion device. Background Art

[0002] Photoelectric conversion devices are widely used in solar photovoltaic power generation, photoelectric sensors, photoelectric detectors, and optical communications. However, these devices typically operate only in fixed scenarios or under specific conditions and lack adaptability to diverse environments. Changing environmental conditions require manual adjustment or reconfiguration, making intelligent, automated operation difficult. They are also insensitive to environmental conditions and unable to optimize their operating modes based on real-time conditions, resulting in reduced energy efficiency and wasted resources. Furthermore, traditional devices are unable to accurately identify the characteristics of different scenarios.

[0003] Therefore, the present invention provides a multi-scenario implementation method for a photoelectric conversion device. Summary of the Invention

[0004] The present invention provides a multi-scenario implementation method for a photoelectric conversion device. By determining the characteristic vectors of all working modes of the photoelectric conversion device and the expected environmental vector of the current scene, the current working mode of the current scene of the photoelectric conversion device is determined, and the current working mode is automatically switched to. This method can reduce manual intervention, automatically adapt to multiple scenes, flexibly select power supply methods, improve operating efficiency and adaptability, improve the ease of use, energy efficiency management and endurance of the device, and improve the ease of use and energy utilization efficiency of the photoelectric conversion device.

[0005] The present invention provides a multi-scenario implementation method for a photoelectric conversion device, comprising:

[0006] 101: Extract features of all working modes of the photoelectric conversion device and determine a feature vector of each working mode;

[0007] 102: Collecting environmental data of a scene in which the photoelectric conversion device is currently located, and determining an estimated environmental vector of the scene in which the photoelectric conversion device is currently located based on the environmental data;

[0008] 103: Determine a current operating mode of the photoelectric conversion device in the current scene based on all feature vectors and the predicted environment vector;

[0009] 104: The photoelectric conversion device automatically switches to the current working mode to adapt to the current scenario.

[0010] According to a multi-scenario implementation method of a photoelectric conversion device provided by the present invention, the photoelectric conversion device includes a built-in battery, a power indicator, and a charging interface.

[0011] According to a multi-scenario implementation method of a photoelectric conversion device provided by the present invention, feature extraction is performed on all operating modes of the photoelectric conversion device to determine a feature vector for each operating mode, including:

[0012] Acquiring mode operation data of each working mode of the photoelectric conversion device within a specified time period, and preprocessing the operation data of each mode respectively, wherein the preprocessing includes data cleaning, data smoothing, and data standardization;

[0013] Determine the time window and window function based on the specified time period and all mode operation data;

[0014] Segmenting the mode operation data based on the time window to determine the window operation data of each working mode, wherein the window operation data includes a plurality of sub-window operation data;

[0015] Determine a segmentation sequence of the corresponding sub-window running data based on a segmentation order of each sub-window running data in the window running data;

[0016] Applying a window function to the window operation data of each working mode respectively, performing Fourier transform on the window operation data after applying the window function, and determining frequency domain data of each working mode, wherein the frequency domain data includes a plurality of sub-frequency domain data;

[0017] Based on each sub-frequency domain data in each frequency domain data, draw a frequency-amplitude graph and a frequency-power graph of each sub-frequency domain data;

[0018] Selecting the maximum amplitude in the frequency-amplitude diagram as the extreme amplitude of the corresponding sub-frequency domain data, and determining the main amplitude range of the corresponding sub-frequency domain data based on the frequency-amplitude diagram;

[0019] Determine the main frequency range of the corresponding sub-frequency domain data based on the frequency-power diagram;

[0020] Determine the distribution uniformity value of the corresponding sub-frequency domain data based on the frequency-amplitude diagram and the frequency-power diagram;

[0021] Based on the extreme amplitude value, main amplitude range, main frequency range and distribution uniformity value of the same sub-frequency domain data, the sub-eigenvector of the corresponding sub-frequency domain data is determined;

[0022] All sub-frequency domain data in the frequency domain data of each working mode are arranged based on the corresponding segmentation sequence, and the eigenvector of the corresponding working mode is determined based on the sub-eigenvectors of all the arranged sub-frequency domain data of each working mode.

[0023] According to a multi-scenario implementation method of a photoelectric conversion device provided by the present invention, a eigenvector of a corresponding working mode is determined based on the sub-eigenvectors of all sub-frequency domain data after arrangement of each working mode, including:

[0024] Calculate the eigenvector of the corresponding working mode based on all sub-frequency domain data and all sub-eigenvectors of each working mode;

[0025]

[0026] in, They represent the jth sub-frequency domain data and the kth sub-frequency domain data of the ath working mode respectively. represents the data consistency value of the j-th sub-frequency domain data and the k-th sub-frequency domain data of the a-th working mode, N1 represents the number of data in the sub-frequency domain data, Represents the data value in the jth sub-frequency domain data of the ath working mode, Represents the data value in the kth sub-frequency domain data of the ath working mode, represents the probability of consistency between the j-th sub-frequency domain data and the k-th sub-frequency domain data of the a-th working mode, They represent the non-consistent probability of the j-th sub-frequency domain data of the a-th working mode based on the k-th sub-frequency domain data, and the non-consistent probability of the k-th sub-frequency domain data based on the j-th sub-frequency domain data, respectively. N2 represents the number of sub-frequency domain data of the working mode. represents the pth eigenvalue of the ath working mode, The pth eigenvalue of the sub-eigenvector of the kth sub-frequency domain data of the ath working mode, The pth eigenvalue of the sub-eigenvector of the jth sub-frequency domain data of the ath working mode, F a The eigenvector representing the ath working mode, represents the first eigenvalue of the ath working mode, represents the N3th eigenvalue of the ath working mode, where N3 represents the number of eigenvalues ​​of the eigenvector.

[0027] According to a multi-scenario implementation method for a photoelectric conversion device provided by the present invention, environmental data of a current scene of the photoelectric conversion device is collected, and an estimated environmental vector of the current scene of the photoelectric conversion device is determined based on the environmental data, including:

[0028] Identify multiple key environmental variables based on all operating modes of the photoelectric conversion device, and determine the acquisition frequency based on the environmental change rate of all key environmental variables;

[0029] Collect environmental data of each key environmental variable based on the collection frequency, and pre-process the collected environmental data;

[0030] Feature extraction is performed on the pre-processed environmental data of each key environmental variable to determine the characteristic value of each key environmental variable, and an estimated environmental vector is determined based on the characteristic values ​​of all key environmental variables.

[0031] According to the present invention, a multi-scenario implementation method for a photoelectric conversion device is provided, which determines a current operating mode of a current scenario of the photoelectric conversion device based on all feature vectors and an estimated environment vector, including:

[0032] Compare the number of eigenvalues ​​in the feature vector of the working mode and the number of key environmental variables. If the number of key environmental variables is greater than the number of eigenvalues ​​in the feature vector, perform dimensionality reduction on the predicted environmental vector to determine the environmental vector of the current scene. If the number of key environmental variables is less than the number of eigenvalues ​​in the feature vector, perform interpolation and dimensionality increase on the predicted environmental vector to determine the environmental vector of the current scene.

[0033] Based on the environment vector and the feature vector of each working mode, the matching value between each working mode and the environment vector of the current scene is calculated;

[0034]

[0035] Among them, M a represents the matching value between the feature vector of the ath working mode and the environment vector of the current scene, τ represents the first adjustment parameter, E represents the environment vector of the current scene, e p represents the pth eigenvalue in the environment vector of the current scene, α1 and α2 represent the first weight and the second weight respectively, ||F a || and ||E|| respectively represent the modulus of the feature vector of the ath working mode and the modulus of the environment vector of the current scene, δ represents the second adjustment parameter, μ represents the third adjustment parameter, and β represents the exponential parameter. The absolute difference between the pth eigenvalue of the eigenvector of the ath working mode and the pth eigenvalue of the environment vector of the current scene;

[0036] The operating mode of the characteristic vector corresponding to the largest matching value is selected from all matching values ​​as the current operating mode of the photoelectric conversion device in the current scene.

[0037] According to a multi-scenario implementation method of a photoelectric conversion device provided by the present invention, the photoelectric conversion device automatically switches to a current operating mode to adapt to the current scenario, including:

[0038] After the photoelectric conversion device switches to the current working mode, the working parameters of the current working mode of the photoelectric conversion device are adjusted based on the matching value between the characteristic vector corresponding to the current working mode and the environment vector.

[0039] According to a multi-scenario implementation method for a photoelectric conversion device provided by the present invention, the photoelectric conversion device selects a built-in battery or a charging port for power supply based on the current scenario;

[0040] The built-in battery can be charged by connecting to the charging port;

[0041] The built-in battery includes a photovoltaic cell assembly that can be charged by converting light energy absorbed by the photovoltaic panel of the photoelectric conversion device into electrical energy;

[0042] When the built-in battery voltage is lower than the first set threshold, the built-in battery is charged, and when the built-in battery voltage is higher than the second set threshold, the charging is stopped;

[0043] When the power consumption rate of the photoelectric conversion device is greater than the preset power consumption rate, the built-in battery is charged;

[0044] The battery indicator displays the battery level of the photoelectric device and the voltage of the built-in battery.

[0045] Compared with the prior art, the present invention has the following advantages:

[0046] By determining the characteristic vectors of all working modes of the photoelectric conversion device, the expected environmental vector of the current scene, determining the current working mode of the photoelectric conversion device in the current scene, and automatically switching to the current working mode, manual intervention can be reduced, multiple scenes can be automatically adapted, power supply methods can be flexibly selected, operational efficiency and adaptability can be improved, and the ease of use, energy efficiency management and endurance of the equipment can be improved, thereby improving the ease of use and energy utilization efficiency of the photoelectric conversion equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the technical solutions in the present invention or the prior art, a brief introduction is given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are 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.

[0048] Figure 1 It is a flowchart of a multi-scenario implementation method of a photoelectric conversion device provided by an embodiment of the present invention.

[0049] Figure 2 It is a schematic diagram of a photoelectric conversion device according to a multi-scenario implementation method of a photoelectric conversion device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0050] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the embodiments described are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0051] Example 1:

[0052] The embodiment of the present invention provides a multi-scenario implementation method of a photoelectric conversion device, such as Figure 1 and Figure 2 Shown, including:

[0053] 101: Extract features of all working modes of the photoelectric conversion device and determine a feature vector of each working mode;

[0054] 102: Collecting environmental data of a scene in which the photoelectric conversion device is currently located, and determining an estimated environmental vector of the scene in which the photoelectric conversion device is currently located based on the environmental data;

[0055] 103: Determine a current operating mode of the photoelectric conversion device in the current scene based on all feature vectors and the predicted environment vector;

[0056] 104: The photoelectric conversion device automatically switches to the current working mode to adapt to the current scenario.

[0057] In this embodiment, each operating mode of the photoelectric conversion device corresponds to a feature vector, and the feature vector includes values ​​of all features of the operating mode corresponding to the photoelectric conversion device.

[0058] In this embodiment, the environmental data of the scene in which the photoelectric conversion device is currently located is collected using sensors installed on the photoelectric conversion device.

[0059] In this embodiment, the environmental data refers to external condition data of the scene in which the photoelectric conversion device is currently located, such as factors affecting the working efficiency of the device, such as light, temperature, humidity, etc.

[0060] In this embodiment, the expected environment vector represents a mathematical representation of the predicted environment state based on the currently collected environment data to match the current working mode of the device. The expected environment vector is a vector that has not yet been aligned with the feature vector of the working mode in terms of the number of features, that is, a vector that has not been processed with dimensionality reduction or dimensionality increase.

[0061] In this embodiment, the environment vector is determined based on the predicted environment vector, and the working mode that best suits the current scene is determined in combination with all working mode feature vectors.

[0062] In this embodiment, the current working mode is the working mode corresponding to the maximum matching value of the environment vector of the current scene.

[0063] In this embodiment, Figure 2 As shown, the optical fiber interface is used to connect optical fibers and receive optical signals.

[0064] In this embodiment, Figure 2 As shown, the photoelectric conversion device converts the received optical signal into an electrical signal through an isolated level converter, and then connects the converted signal to CAN and RS485 output.

[0065] In this embodiment, the photoelectric conversion device realizes the simultaneous conversion of optical signals into CAN and RS485. When connecting, only one of the two ports can be selected for connection, realizing bidirectional conversion, that is, converting optical fiber signals into wired transmission or converting wired transmission into optical fiber transmission.

[0066] In this embodiment, Figure 2 As shown, the physical switch is used to turn on the current device to enter the working state. When it is turned off, the battery is disconnected from the power supply of the entire system and no power is consumed. When it is turned on, the system automatically enters the working state.

[0067] The beneficial effects of the above technical solution are as follows: by determining the characteristic vectors of all working modes of the photoelectric conversion device, the expected environmental vector of the current scene, determining the current working mode of the current scene of the photoelectric conversion device, and automatically switching to the current working mode, it can reduce manual intervention, automatically adapt to multiple scenes, flexibly select power supply methods, improve operating efficiency and adaptability, improve the ease of use, energy efficiency management and endurance of the equipment, and improve the ease of use and energy utilization efficiency of the photoelectric conversion equipment.

[0068] Example 2:

[0069] An embodiment of the present invention provides a multi-scenario implementation method for a photoelectric conversion device, wherein the photoelectric conversion device includes a built-in battery, a power indicator, and a charging interface.

[0070] In this embodiment, the device can be powered by a built-in battery to facilitate temporary wiring during construction without the need to find a power source.

[0071] In this embodiment, Figure 2 As shown, the device has a built-in battery for storing electrical energy, which can convert light energy into electrical energy and store it so that the device can work normally even when there is no light or the environmental conditions are poor.

[0072] In this embodiment, Figure 2As shown, the device provides a charging interface through which the user can charge the built-in battery externally to ensure that the device still has enough power to support its operation when the light energy is insufficient.

[0073] In this embodiment, Figure 2 As shown, the battery indicator usually uses a pointer, a digital tube or an LED light to display the remaining power.

[0074] The beneficial effects of the above technical solution are: by providing the photoelectric conversion device with a built-in battery, a power indicator and a charging interface, the power supply method can be flexibly selected, the power level can be monitored in real time, and the battery charging management can be automatically performed, thereby improving the device's ease of use, energy efficiency management and battery life.

[0075] Example 3:

[0076] An embodiment of the present invention provides a multi-scenario implementation method for a photoelectric conversion device, which extracts features from all operating modes of the photoelectric conversion device and determines a feature vector for each operating mode, including:

[0077] Acquiring mode operation data of each working mode of the photoelectric conversion device within a specified time period, and preprocessing the operation data of each mode respectively, wherein the preprocessing includes data cleaning, data smoothing, and data standardization;

[0078] Determine the time window and window function based on the specified time period and all mode operation data;

[0079] Segmenting the mode operation data based on the time window to determine the window operation data of each working mode, wherein the window operation data includes a plurality of sub-window operation data;

[0080] Determine a segmentation sequence of the corresponding sub-window running data based on a segmentation order of each sub-window running data in the window running data;

[0081] Applying a window function to the window operation data of each working mode respectively, performing Fourier transform on the window operation data after applying the window function, and determining frequency domain data of each working mode, wherein the frequency domain data includes a plurality of sub-frequency domain data;

[0082] Based on each sub-frequency domain data in each frequency domain data, draw a frequency-amplitude graph and a frequency-power graph of each sub-frequency domain data;

[0083] Selecting the maximum amplitude in the frequency-amplitude diagram as the extreme amplitude of the corresponding sub-frequency domain data, and determining the main amplitude range of the corresponding sub-frequency domain data based on the frequency-amplitude diagram;

[0084] Determine the main frequency range of the corresponding sub-frequency domain data based on the frequency-power diagram;

[0085] Determine the distribution uniformity value of the corresponding sub-frequency domain data based on the frequency-amplitude diagram and the frequency-power diagram;

[0086] Based on the extreme amplitude value, main amplitude range, main frequency range and distribution uniformity value of the same sub-frequency domain data, the sub-eigenvector of the corresponding sub-frequency domain data is determined;

[0087] All sub-frequency domain data in the frequency domain data of each working mode are arranged based on the corresponding segmentation sequence, and the eigenvector of the corresponding working mode is determined based on the sub-eigenvectors of all the arranged sub-frequency domain data of each working mode.

[0088] In this embodiment, data cleaning means removing invalid data such as noise and outliers from the model operation data, data smoothing means reducing short-term fluctuations in the model operation data to make it more stable, and data standardization means adjusting the model operation data to a unified dimension for subsequent analysis.

[0089] In this embodiment, the mode operation data includes input data, output data, device status and other auxiliary data of the corresponding working mode;

[0090] In this embodiment, the time window represents a time period for dividing the mode operation data when analyzing the time series relationship of the mode operation data, so as to facilitate local analysis of sub-window operation data in different time periods.

[0091] In this embodiment, the window function refers to a function applied to sub-window running data of a time window, which is used to reduce the distortion of data at the window boundary. Common window functions include Hamming window, Hanning window, etc.

[0092] In this embodiment, Fourier transform is applied to convert the windowed running data into frequency domain data.

[0093] In this embodiment, the horizontal axis of the frequency-amplitude graph represents the frequency, and the vertical axis represents the amplitude of the corresponding frequency, so that the distribution of the signal at each frequency can be observed intuitively.

[0094] In this embodiment, the horizontal axis of the frequency-power graph is frequency, and the vertical axis is power, which intuitively reflects the energy distribution of the signal at different frequencies.

[0095] In this embodiment, it is observed whether each feature in the sub-feature vector changes gradually over time or exhibits periodic fluctuations, so as to identify potential long-term changes or mode switching.

[0096] In this embodiment, the extreme amplitude value refers to the maximum amplitude in the frequency-amplitude diagram, representing the strongest frequency component in the signal.

[0097] In this embodiment, the main amplitude range refers to a region in the frequency-amplitude diagram where the amplitudes are significantly concentrated, reflecting the amplitude range of the main frequency components in the signal.

[0098] In this embodiment, the main frequency range refers to a frequency interval in which energy is significantly concentrated in the frequency-power graph, reflecting the frequency range in which the main energy in the signal is concentrated.

[0099] In this embodiment, the distribution uniformity value represents the degree of uniformity of the distribution of frequency domain data in terms of frequency and amplitude, and is used to measure whether the distribution of data at different frequencies is consistent.

[0100] The beneficial effects of the above technical solution are as follows: by extracting features from all working modes of the photoelectric conversion device and determining the feature vector of each working mode, a data basis can be provided for determining the current working mode of the current scene in which the photoelectric conversion device is currently located, so as to dynamically match the current working mode according to the environmental data.

[0101] Example 4:

[0102] An embodiment of the present invention provides a multi-scenario implementation method for a photoelectric conversion device, which determines a eigenvector of the corresponding operating mode based on the sub-eigenvectors of all sub-frequency domain data arranged after each operating mode, including:

[0103] Calculate the eigenvector of the corresponding working mode based on all sub-frequency domain data and all sub-eigenvectors of each working mode;

[0104]

[0105] in, They represent the jth sub-frequency domain data and the kth sub-frequency domain data of the ath working mode respectively. represents the data consistency value of the j-th sub-frequency domain data and the k-th sub-frequency domain data of the a-th working mode, N1 represents the number of data in the sub-frequency domain data, Represents the data value in the jth sub-frequency domain data of the ath working mode, Represents the data value in the kth sub-frequency domain data of the ath working mode, represents the probability of consistency between the j-th sub-frequency domain data and the k-th sub-frequency domain data of the a-th working mode, They represent the non-consistent probability of the j-th sub-frequency domain data of the a-th working mode based on the k-th sub-frequency domain data, and the non-consistent probability of the k-th sub-frequency domain data based on the j-th sub-frequency domain data, respectively. N2 represents the number of sub-frequency domain data of the working mode. represents the pth eigenvalue of the ath working mode, The pth eigenvalue of the sub-eigenvector of the kth sub-frequency domain data of the ath working mode, The pth eigenvalue of the sub-eigenvector of the jth sub-frequency domain data of the ath working mode, F a The eigenvector representing the ath working mode, represents the first eigenvalue of the ath working mode, represents the N3th eigenvalue of the ath working mode, where N3 represents the number of eigenvalues ​​of the eigenvector.

[0106] In this embodiment, each working mode corresponds to a feature vector.

[0107] In this embodiment, the probability of agreement Indicates that there is a At the same time, there is a probability.

[0108] In this embodiment, express is a data value in the j-th sub-frequency domain data of the a-th working mode.

[0109] In this embodiment, express is a data value in the kth sub-frequency domain data of the ath working mode.

[0110] In this embodiment, the non-uniform probability Indicates that the jth sub-frequency domain data of the ath working mode exists However, the kth sub-frequency domain data does not exist probability.

[0111] In this embodiment, the non-uniform probability Indicates that the jth sub-frequency domain data of the ath working mode does not exist The kth sub-frequency domain data contains probability.

[0112] In this embodiment, Represents the comprehensive consistency value of the j-th sub-frequency domain data in the eigenvector of the a-th operating mode.

[0113] The beneficial effects of the above technical solution are as follows: according to the sub-feature vectors of all sub-frequency domain data after arrangement of each working mode, the feature vector of the corresponding working mode is determined, which can refine pattern recognition, accurately capture the characteristics of the working mode, improve the adaptability of the equipment to different scenarios, and enhance the accuracy and efficiency of operation.

[0114] Example 5:

[0115] An embodiment of the present invention provides a multi-scenario implementation method for a photoelectric conversion device, which collects environmental data of a current scene of the photoelectric conversion device and determines an estimated environmental vector of the current scene of the photoelectric conversion device based on the environmental data, including:

[0116] Identify multiple key environmental variables based on all operating modes of the photoelectric conversion device, and determine the acquisition frequency based on the environmental change rate of all key environmental variables;

[0117] Collect environmental data of each key environmental variable based on the collection frequency, and pre-process the collected environmental data;

[0118] Feature extraction is performed on the pre-processed environmental data of each key environmental variable to determine the characteristic value of each key environmental variable, and an estimated environmental vector is determined based on the characteristic values ​​of all key environmental variables.

[0119] In this embodiment, key environmental variables represent external environmental factors that affect the performance of the photoelectric conversion device during operation, such as light intensity, temperature, humidity, wind speed, etc. Identifying these variables can help better understand the interaction between the device and the environment.

[0120] In this embodiment, the environmental change rate represents the speed at which environmental variables change over time. A higher change rate means that environmental factors change faster, and more frequent data collection is required to capture these changes.

[0121] In this embodiment, the collection frequency is used to determine how often environmental data is collected. The higher the collection frequency, the more frequent the data collection, which is suitable for monitoring rapidly changing environmental variables.

[0122] In this embodiment, a series of processing steps for the collected environmental data may include data cleaning (removing noise and outliers), data smoothing (reducing short-term fluctuations) and data standardization (unifying dimensions), etc., to ensure the accuracy and consistency of the data.

[0123] The beneficial effects of the above technical solution are: collecting environmental data of the scene in which the photoelectric conversion device is currently located, and determining the expected environmental vector of the scene in which the photoelectric conversion device is currently located based on the environmental data, which can provide a data basis for determining the current working mode of the scene in which the photoelectric conversion device is currently located, thereby improving the adaptability and efficiency of the photoelectric conversion device in a changing environment, reducing manual adjustments, and enhancing automation capabilities.

[0124] Example 6:

[0125] An embodiment of the present invention provides a multi-scenario implementation method for a photoelectric conversion device, which determines the current operating mode of the current scenario of the photoelectric conversion device based on all feature vectors and an estimated environment vector, including:

[0126] Compare the number of eigenvalues ​​in the feature vector of the working mode and the number of key environmental variables. If the number of key environmental variables is greater than the number of eigenvalues ​​in the feature vector, perform dimensionality reduction on the predicted environmental vector to determine the environmental vector of the current scene. If the number of key environmental variables is less than the number of eigenvalues ​​in the feature vector, perform interpolation and dimensionality increase on the predicted environmental vector to determine the environmental vector of the current scene.

[0127] Based on the environment vector and the feature vector of each working mode, the matching value between each working mode and the environment vector of the current scene is calculated;

[0128]

[0129] Among them, M a represents the matching value between the feature vector of the ath working mode and the environment vector of the current scene, τ represents the first adjustment parameter, E represents the environment vector of the current scene, e p represents the pth eigenvalue in the environment vector of the current scene, α1 and α2 represent the first weight and the second weight respectively, ||F a || and ||E|| respectively represent the modulus of the feature vector of the ath working mode and the modulus of the environment vector of the current scene, δ represents the second adjustment parameter, μ represents the third adjustment parameter, and β represents the exponential parameter. The absolute difference between the pth eigenvalue of the eigenvector representing the ath working mode and the pth eigenvalue of the environment vector of the current scene;

[0130] The operating mode of the characteristic vector corresponding to the largest matching value is selected from all matching values ​​as the current operating mode of the photoelectric conversion device in the current scene.

[0131] In this embodiment, the number of eigenvalues ​​contained in the working mode feature vector is first compared with the number of key environmental variables. If the number of key environmental variables is greater than the number of eigenvalues ​​in the feature vector, it means that the dimension of the environmental data is high and the expected environmental vector needs to be reduced in dimension. If the number of key environmental variables is less than the number of eigenvalues, the environmental vector needs to be increased in dimension through interpolation.

[0132] In this embodiment, when the dimension of the environmental data is too high, the dimension reduction method may be principal component analysis (PCA), linear discriminant analysis (LDA), etc.

[0133] In this embodiment, when the dimension of the environmental data is low, the dimension is increased by interpolation to make the environmental data match the feature vector.

[0134] In this embodiment, represents the cosine similarity between the feature vector of the a-th working mode and the environment vector, and the first weight α1 represents the weight based on the cosine similarity.

[0135] In this embodiment, It represents the eigenvalue matching between the eigenvector of the ath working mode and the environment vector, and the second weight α1 represents the weight based on the eigenvalue matching.

[0136] In this embodiment, in the current scenario, the device selects the optimal current working mode, which is determined based on the matching value between the environment vector and each feature vector.

[0137] The beneficial effects of the above technical solution are: based on all characteristic vectors and expected environmental vectors, the current working mode of the photoelectric conversion device in the current scene is determined, which can refine the pattern matching, improve the operating efficiency and adaptability, and improve the ease of use and energy utilization efficiency of the photoelectric conversion device.

[0138] Example 7:

[0139] An embodiment of the present invention provides a multi-scenario implementation method for a photoelectric conversion device, wherein the photoelectric conversion device automatically switches to a current operating mode to adapt to the current scenario, including:

[0140] After the photoelectric conversion device switches to the current working mode, the working parameters of the current working mode of the photoelectric conversion device are adjusted based on the matching value between the characteristic vector corresponding to the current working mode and the environment vector.

[0141] In this embodiment, after switching to the current working mode, the photoelectric conversion device will dynamically adjust the working parameters of the device based on the matching value between the characteristic vector of the current working mode and the current environment vector. The matching value reflects the adaptability of the device to operate in the current environment. The larger the matching value, the smaller the parameter adjustment of the device; when the matching value is small, the device may require a larger adjustment to better adapt to the environment.

[0142] The beneficial effects of the above technical solution are: the photoelectric conversion equipment automatically switches to the current working mode to adapt to the current scenario, which can reduce manual intervention, automatically adapt to multiple scenarios, flexibly select power supply methods, improve operating efficiency and adaptability, improve the equipment's ease of use, energy efficiency management and endurance, and improve the photoelectric conversion equipment's ease of use and energy utilization efficiency.

[0143] Example 8:

[0144] An embodiment of the present invention provides a multi-scenario implementation method for a photoelectric conversion device, wherein the photoelectric conversion device selects a built-in battery or a charging port for power supply based on the current scenario;

[0145] The built-in battery can be charged by connecting to the charging port;

[0146] The built-in battery includes a photovoltaic cell assembly that can be charged by converting light energy absorbed by a photovoltaic panel of a photoelectric conversion device into electrical energy;

[0147] When the built-in battery voltage is lower than the first set threshold, the built-in battery is charged, and when the built-in battery voltage is higher than the second set threshold, the charging is stopped;

[0148] When the power consumption rate of the photoelectric conversion device is greater than the preset power consumption rate, the built-in battery is charged;

[0149] The battery indicator displays the battery level of the photoelectric device and the voltage of the built-in battery.

[0150] In this embodiment, the device will automatically choose to use the built-in battery for power supply or to use the external charging port for power supply according to the current environment or scene. For example, if the current scene environmental conditions are suitable for built-in battery power supply, the device may give priority to using the battery; when the battery power is low, it may switch to the charging port for power supply.

[0151] In this embodiment, the first set threshold represents the lower limit of the built-in battery voltage. When the voltage is lower than this threshold, the device starts charging.

[0152] In this embodiment, the second set threshold represents the upper limit of the built-in battery voltage. When the voltage exceeds this threshold, the device stops charging to prevent overcharging.

[0153] In this embodiment, the preset power consumption rate represents the upper limit of the power consumption rate of the photoelectric conversion device. When the power consumption rate is higher than this threshold, the device consumes electricity faster and starts charging.

[0154] In this embodiment, the device is provided with a battery indicator that displays the battery level. The user can use it to view the current battery level and voltage of the device in real time, understand the remaining available battery power and voltage, and conveniently determine whether the device needs to be charged or replaced.

[0155] The beneficial effects of the above technical solution are: automatic switching of power supply modes and intelligent control of battery charging through scene perception, which achieves energy efficiency optimization and improved power safety. The power indicator displays power information in real time for easy monitoring, which helps to extend the life of the equipment and improve its ease of use.

[0156] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they may be located in one location or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of the present embodiment. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.

[0157] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, or of course, by hardware. Based on this understanding, the essence of the above technical solution or the part that contributes to the existing technology can be embodied in the form of a software product. The computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or certain parts of the embodiments.

[0158] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A multi-scenario implementation method for a photoelectric conversion device, characterized in that: include: 101: Extract features of all working modes of the photoelectric conversion device and determine a feature vector of each working mode; 102: Collecting environmental data of a scene in which the photoelectric conversion device is currently located, and determining an estimated environmental vector of the scene in which the photoelectric conversion device is currently located based on the environmental data; 103: Determine a current operating mode of the photoelectric conversion device in the current scene based on all feature vectors and the predicted environment vector; 104: The photoelectric conversion device automatically switches to a current operating mode to adapt to the current scenario. Feature extraction is performed on all operating modes of the photoelectric conversion device to determine a feature vector for each operating mode, including: Acquiring mode operation data of each working mode of the photoelectric conversion device within a specified time period, and preprocessing the operation data of each mode respectively, wherein the preprocessing includes data cleaning, data smoothing, and data standardization; Determine the time window and window function based on the specified time period and all mode operation data; Segmenting the mode operation data based on the time window to determine the window operation data of each working mode, wherein the window operation data includes a plurality of sub-window operation data; Determine a segmentation sequence of the corresponding sub-window running data based on a segmentation order of each sub-window running data in the window running data; Applying a window function to the window operation data of each working mode respectively, performing Fourier transform on the window operation data after applying the window function, and determining frequency domain data of each working mode, wherein the frequency domain data includes a plurality of sub-frequency domain data; Based on each sub-frequency domain data in each frequency domain data, draw a frequency-amplitude graph and a frequency-power graph of each sub-frequency domain data; Selecting the maximum amplitude in the frequency-amplitude diagram as the extreme amplitude of the corresponding sub-frequency domain data, and determining the main amplitude range of the corresponding sub-frequency domain data based on the frequency-amplitude diagram; Determine the main frequency range of the corresponding sub-frequency domain data based on the frequency-power diagram; Determine the distribution uniformity value of the corresponding sub-frequency domain data based on the frequency-amplitude diagram and the frequency-power diagram; Based on the extreme amplitude value, main amplitude range, main frequency range and distribution uniformity value of the same sub-frequency domain data, the sub-eigenvector of the corresponding sub-frequency domain data is determined; All sub-frequency domain data in the frequency domain data of each working mode are arranged based on the corresponding segmentation sequence, and the eigenvector of the corresponding working mode is determined based on the sub-eigenvectors of all the arranged sub-frequency domain data of each working mode.

2. The multi-scenario implementation method of a photoelectric conversion device according to claim 1, characterized in that: The photoelectric conversion device includes a built-in battery, a power indicator and a charging interface.

3. The multi-scenario implementation method of a photoelectric conversion device according to claim 1, characterized in that: Based on the sub-feature vectors of all sub-frequency domain data after arrangement for each working mode, the feature vector of the corresponding working mode is determined, including: Calculate the eigenvector of the corresponding working mode based on all sub-frequency domain data and all sub-eigenvectors of each working mode; in, They represent the jth sub-frequency domain data and the kth sub-frequency domain data of the ath working mode respectively. represents the data consistency value of the j-th sub-frequency domain data and the k-th sub-frequency domain data of the a-th working mode, N1 represents the number of data in the sub-frequency domain data, Represents the data value in the jth sub-frequency domain data of the ath working mode, Represents the data value in the kth sub-frequency domain data of the ath working mode, represents the probability of consistency between the jth sub-frequency domain data and the kth sub-frequency domain data of the ath working mode, They represent the non-consistent probability of the j-th sub-frequency domain data of the a-th working mode based on the k-th sub-frequency domain data, and the non-consistent probability of the k-th sub-frequency domain data based on the j-th sub-frequency domain data, respectively. N2 represents the number of sub-frequency domain data of the working mode. represents the pth eigenvalue of the ath working mode, The pth eigenvalue of the sub-eigenvector of the kth sub-frequency domain data of the ath working mode, The pth eigenvalue of the sub-eigenvector of the jth sub-frequency domain data of the ath working mode, F a The eigenvector representing the ath working mode, represents the first eigenvalue of the ath working mode, represents the N3th eigenvalue of the ath working mode, where N3 represents the number of eigenvalues ​​of the eigenvector.

4. The multi-scenario implementation method of a photoelectric conversion device according to claim 3, characterized in that: Collecting environmental data of the scene in which the photoelectric conversion device is currently located, and determining an estimated environmental vector of the scene in which the photoelectric conversion device is currently located based on the environmental data, including: Identify multiple key environmental variables based on all operating modes of the photoelectric conversion device, and determine the acquisition frequency based on the environmental change rate of all key environmental variables; Collect environmental data of each key environmental variable based on the collection frequency, and pre-process the collected environmental data; Feature extraction is performed on the pre-processed environmental data of each key environmental variable to determine the characteristic value of each key environmental variable, and an estimated environmental vector is determined based on the characteristic values ​​of all key environmental variables.

5. The multi-scenario implementation method of a photoelectric conversion device according to claim 4, characterized in that: Based on all feature vectors and the expected environment vector, the current operating mode of the photoelectric conversion device in the current scene is determined, including: Compare the number of eigenvalues ​​in the feature vector of the working mode and the number of key environmental variables. If the number of key environmental variables is greater than the number of eigenvalues ​​in the feature vector, perform dimensionality reduction on the predicted environmental vector to determine the environmental vector of the current scene. If the number of key environmental variables is less than the number of eigenvalues ​​in the feature vector, perform interpolation and dimensionality increase on the predicted environmental vector to determine the environmental vector of the current scene. Based on the environment vector and the feature vector of each working mode, the matching value between each working mode and the environment vector of the current scene is calculated; Among them, M a represents the matching value between the feature vector of the ath working mode and the environment vector of the current scene, τ represents the first adjustment parameter, E represents the environment vector of the current scene, e p represents the pth eigenvalue in the environment vector of the current scene, α1 and α2 represent the first weight and the second weight respectively, ‖F a ‖ and ‖E‖ respectively represent the modulus of the feature vector of the ath working mode and the modulus of the environment vector of the current scene, δ represents the second adjustment parameter, μ represents the third adjustment parameter, and β represents the exponential parameter. represents the absolute difference between the pth eigenvalue of the eigenvector of the ath working mode and the pth eigenvalue of the environment vector of the current scene; e represents the exponential base, F a represents the eigenvector of the ath working mode, N3 represents the number of eigenvalues ​​of the eigenvector, represents the pth eigenvalue of the ath working mode; The operating mode of the characteristic vector corresponding to the largest matching value is selected from all matching values ​​as the current operating mode of the photoelectric conversion device in the current scene.

6. The multi-scenario implementation method of a photoelectric conversion device according to claim 1, characterized in that: The photoelectric conversion device automatically switches to the current operating mode to suit the current scenario, including: After the photoelectric conversion device switches to the current working mode, the working parameters of the current working mode of the photoelectric conversion device are adjusted based on the matching value between the characteristic vector corresponding to the current working mode and the environment vector.

7. The multi-scenario implementation method of a photoelectric conversion device according to claim 2, characterized in that: The photoelectric conversion device selects the built-in battery or charging port for power supply based on the current scenario; The built-in battery can be charged by connecting to the charging port; The built-in battery includes a photovoltaic cell assembly that can be charged by converting light energy absorbed by a photovoltaic panel of a photoelectric conversion device into electrical energy; When the built-in battery voltage is lower than the first set threshold, the built-in battery is charged, and when the built-in battery voltage is higher than the second set threshold, the charging is stopped; When the power consumption rate of the photoelectric conversion device is greater than the preset power consumption rate, the built-in battery is charged; The battery indicator displays the battery level of the photoelectric device and the voltage of the built-in battery.

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