Iot perception-based light storage and charging cooperative control method

Abstract

The application discloses a light storage and charging cooperative control method based on Internet of Things sensing, and relates to the technical field of cooperative control, and comprises the following steps: acquiring historical photovoltaic power generation data, constructing a power generation prediction function based on the historical photovoltaic power generation data; acquiring real-time predicted charging power based on real-time illumination intensity and the photovoltaic power generation prediction function; acquiring a first power threshold value and a second power threshold value of different predicted charging power; acquiring a first real-time power threshold value and a second real-time power threshold value based on the predicted charging power, a first threshold function and a second threshold function; evaluating charging based on real-time battery power, the first real-time power threshold value and the second real-time power threshold value, and optimizing charging control based on an evaluation result; the application is used to solve the problem that in the prior art, an abnormal threshold value is set as a fixed value, resulting in that abnormal detection of a fixed threshold value under different photovoltaic power generation and power consumption conditions is not completely accurate.

Description

Technical Field

[0001] This invention relates to the field of collaborative control technology, specifically to a collaborative control method for photovoltaic energy storage and charging based on Internet of Things (IoT) sensing. Background Technology

[0002] With the continuous and rapid growth of renewable energy installed capacity of photovoltaic power generation, the popularization of electric vehicles has also brought about a large-scale demand for the construction of charging infrastructure. Against this backdrop, an integrated system that combines photovoltaic power generation, energy storage system and electric vehicle charging facilities requires coordinated control of photovoltaic, energy storage and charging in order for the system to operate normally.

[0003] Existing photovoltaic-storage-charging control systems suffer from data anomaly detection issues. The existing anomaly thresholds are set to fixed values, which are not entirely accurate in detecting anomalies under different photovoltaic power generation and consumption conditions, resulting in low accuracy. For example, patent application CN114243802A discloses a method and system for coordinated control of photovoltaic-storage-charging systems in a distribution area. This scheme uses a fixed anomaly threshold, which is not entirely accurate in detecting anomalies under different photovoltaic power generation and consumption conditions. In other words, the fixed anomaly threshold setting in the prior art leads to inaccurate anomaly detection under different photovoltaic power generation and consumption conditions. Summary of the Invention

[0004] This invention aims to at least partially address one of the technical problems in the prior art by acquiring real-time light intensity and real-time battery power; acquiring historical photovoltaic power generation data and constructing a power generation prediction function based on the historical photovoltaic power generation data; acquiring real-time predicted charging power based on real-time light intensity and the photovoltaic power generation prediction function; acquiring a first power threshold and a second power threshold for different predicted charging powers; acquiring a first threshold function based on the first power threshold; acquiring a second threshold function based on the second power threshold; acquiring a first real-time power threshold and a second real-time power threshold based on the predicted charging power, the first threshold function, and the second threshold function; evaluating charging based on real-time battery power, the first real-time power threshold, and the second real-time power threshold; and optimizing charging control based on the evaluation results. This addresses the problem in the prior art where the abnormal threshold is set to a fixed value, resulting in inaccurate abnormal detection of the fixed threshold under different photovoltaic power generation and power consumption conditions.

[0005] To achieve the above objectives, this application provides a photovoltaic-storage-charging coordinated control method based on Internet of Things (IoT) sensing, comprising the following steps: Obtain real-time light intensity and real-time battery power; Obtain historical photovoltaic power generation data and construct a power generation prediction function based on the historical photovoltaic power generation data; Real-time predicted charging power is obtained based on real-time light intensity and photovoltaic power generation prediction function; Obtain a first power threshold and a second power threshold for different predicted charging powers; The first threshold function is obtained based on the first power threshold. The second threshold function is obtained based on the second power threshold. The first real-time power threshold and the second real-time power threshold are obtained based on the predicted charging power, the first threshold function, and the second threshold function. The charging process is evaluated based on real-time battery power, a first real-time power threshold, and a second real-time power threshold, and the charging control is optimized based on the evaluation results.

[0006] Furthermore, constructing a power generation prediction function based on historical photovoltaic power generation data includes the following sub-steps: Historical photovoltaic power generation data includes different light intensities and their corresponding power generation, which are marked as historical light intensity and historical power generation, respectively. Obtain the first number of historical light intensities and their corresponding historical power generation; A Cartesian coordinate system was established with historical solar intensity as the horizontal axis data and historical power generation as the vertical axis data, and it was marked as the power generation prediction coordinate system. The coordinate points with historical light intensity and corresponding historical power generation as the x and y axes, respectively, are marked as power generation prediction coordinate points; The function is obtained by fitting a function to all the power generation prediction coordinate points, and it is labeled as the power generation prediction function.

[0007] Furthermore, obtaining the real-time predicted charging power based on real-time illuminance and the photovoltaic power generation prediction function includes the following sub-steps: Substitute the real-time light intensity as the horizontal axis data into the power generation prediction function to obtain the vertical value, which is then marked as the real-time predicted power generation. Obtain real-time power consumption; The value obtained by subtracting the real-time power consumption from the predicted power generation is tagged as the predicted charging power.

[0008] Furthermore, obtaining the first power threshold and the second power threshold for different predicted charging powers includes the following sub-steps: Under normal charging conditions with the same predicted charging power, a second number of actual battery charging powers are obtained and marked as historical normal charging powers. A Cartesian coordinate system is established with historical normal charging power as the horizontal axis data and the number of historical normal charging powers as the vertical axis data, and it is marked as the power distribution coordinate system. The coordinates of the historical normal charging power and the number of historical normal charging powers are obtained as the x and y coordinates, respectively, and marked as power distribution coordinate points; Plot all power distribution coordinate points in the power distribution coordinate system; The function is obtained by fitting a function to all the power distribution coordinate points, and it is labeled as the power distribution function.

[0009] Furthermore, obtaining the first power threshold and the second power threshold for different predicted charging powers also includes the following sub-steps: Within the range of historical normal charging power, the area enclosed by the power distribution function and the horizontal axis of the power distribution coordinate system is obtained and marked as the overall distribution area; Obtain the area of ​​the entire distribution region and label it as the total area of ​​the distribution; Obtain the maximum value of the ordinate among all power distribution coordinate points and mark it as the power distribution height; Set a length and mark it as the first set length; Create a rectangle on the horizontal axis of the power distribution coordinate system with a height equal to the power distribution height and a width equal to the first set length, and be able to move left and right. Mark it as the distribution judgment rectangle. The average area of ​​the rectangle is obtained as: K2 = K1 × (F1 ÷ F2); where K2 is the average area of ​​the rectangle, K1 is the overall area of ​​the distribution, F1 is the first set length, and F2 is the length of the historical normal charging power range in the power distribution coordinate system; Set a ratio value, which is marked as the first ratio value; obtain the product of the average area of ​​the rectangle and the first ratio value, and mark it as the area anomaly threshold.

[0010] Furthermore, obtaining the first power threshold and the second power threshold for different predicted charging powers also includes the following sub-steps: Obtain the area of ​​the intersection between the distribution judgment rectangle and the overall distribution region, and mark it as the real-time rectangle area; The distribution judgment rectangle is shifted to the right from the leftmost side of the power distribution function. When the area of ​​the real-time rectangle is greater than or equal to the area anomaly threshold, the movement of the distribution judgment rectangle is stopped. The historical normal charging power corresponding to the minimum x-coordinate of the distribution judgment rectangle at this time is obtained and marked as the first power threshold. The distribution judgment rectangle is shifted to the left from the rightmost side of the power distribution function. When the area of ​​the real-time rectangle is greater than or equal to the area anomaly threshold, the movement of the distribution judgment rectangle is stopped. The historical normal charging power corresponding to the largest horizontal coordinate of the distribution judgment rectangle at this time is obtained and marked as the second power threshold.

[0011] Furthermore, obtaining the first threshold function based on the first power threshold includes the following sub-steps: Obtain the first power threshold and the second power threshold corresponding to different predicted charging powers; Mark the predicted charging power as the historical predicted charging power; Using historical predicted charging power as the horizontal axis value and the first power threshold as the vertical axis value, this is marked as the first threshold coordinate system; The coordinate points with the historical predicted charging power and the corresponding first power threshold as the x and y coordinates are marked as the first threshold coordinate points; The function is obtained by fitting a function to all the first threshold coordinate points, and it is marked as the first threshold function.

[0012] Furthermore, obtaining the second threshold function based on the second power threshold includes the following sub-steps: The coordinate system is marked with the historical predicted charging power as the horizontal axis value and the second power threshold as the vertical axis value. The coordinate points with the historical predicted charging power and the corresponding second power threshold as the x and y coordinates are marked as the second threshold coordinate points; The function is obtained by fitting a function to all the second threshold coordinate points, and it is labeled as the second threshold function.

[0013] Furthermore, obtaining the first real-time power threshold and the second real-time power threshold based on the predicted charging power, the first threshold function, and the second threshold function includes the following sub-steps: Substitute the real-time predicted charging power as the horizontal axis into the first threshold function to obtain the vertical axis, which is marked as the first real-time power threshold. Substituting the real-time predicted charging power as the horizontal axis into the second threshold function yields the vertical axis, which is then labeled as the second real-time power threshold.

[0014] Furthermore, the charging process is evaluated based on real-time battery power, a first real-time power threshold, and a second real-time power threshold. Optimizing the charging control based on the evaluation results includes the following sub-steps: If the real-time battery power is not within the range of the first real-time power threshold to the second real-time power threshold, it indicates that the charging is abnormal. If the real-time battery power is within the range of the first real-time power threshold to the second real-time power threshold, it indicates that the charging is normal.

[0015] The beneficial effects of this invention are as follows: This invention acquires real-time light intensity and real-time battery power; acquires historical photovoltaic power generation data and constructs a power generation prediction function based on the historical photovoltaic power generation data; acquires real-time predicted charging power based on real-time light intensity and the photovoltaic power generation prediction function; acquires a first power threshold and a second power threshold for different predicted charging powers; acquires a first threshold function based on the first power threshold; acquires a second threshold function based on the second power threshold; acquires a first real-time power threshold and a second real-time power threshold based on the predicted charging power, the first threshold function, and the second threshold function; evaluates charging based on real-time battery power, the first real-time power threshold, and the second real-time power threshold, and optimizes charging control based on the evaluation results. The advantage lies in setting different anomaly detection thresholds under different photovoltaic power generation and power consumption conditions, thereby improving the accuracy of anomaly detection. This invention obtains a first real-time power threshold and a second real-time power threshold based on predicted charging power, a first threshold function, and a second threshold function. The advantage is that the first real-time power threshold and the second real-time power threshold are obtained in real time. The first real-time power threshold and the second real-time power threshold are used to determine whether there is an anomaly, thereby improving the accuracy of anomaly detection. Attached Figure Description

[0016] Figure 1 This is a flowchart of the steps of the method of the present invention; Figure 2 This is a schematic diagram of the power generation prediction function of the present invention; Figure 3 This is a schematic diagram of the first power threshold and the second power threshold of the present invention; Figure 4 This is a schematic diagram of the first threshold function of the present invention; Figure 5 This is a schematic diagram of the second threshold function of the present invention. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] Example 1, please refer to Figure 1 As shown, this application provides a photoelectric storage-charging coordinated control method based on IoT sensing, including the following steps: Step S1: Obtain real-time light intensity and real-time battery power; real-time battery power is the actual charging power of the battery. In practical applications, the real-time light intensity and real-time battery power are 600W / m². 2 With 132W.

[0019] Step S2: Obtain historical photovoltaic power generation data and construct a power generation prediction function based on the historical photovoltaic power generation data; Step S2 includes the following sub-steps: Step S201: Historical photovoltaic power generation data includes different light intensities and corresponding power generation, which are marked as historical light intensities and historical power generation, respectively. Step S202: Obtain a first number of historical light intensity values ​​and corresponding historical power generation values; in order to obtain the power generation prediction function, the first number should not be too small and should be evenly distributed, for example, the first number is 100. Step S203: Establish a Cartesian coordinate system with historical solar intensity as the horizontal axis data and historical power generation as the vertical axis data, and mark it as the power generation prediction coordinate system; Step S204: Mark the coordinate points with historical light intensity and corresponding historical power generation as the horizontal and vertical axes, respectively, as the power generation prediction coordinate points; Step S205: Perform function fitting on all power generation prediction coordinate points to obtain the function, and mark it as the power generation prediction function; For practical applications, please refer to Figure 2 As shown, the power generation prediction function is obtained.

[0020] Step S3: Obtain the real-time predicted charging power based on the real-time light intensity and the photovoltaic power generation prediction function; Step S3 includes the following sub-steps: Step S301: Substitute the real-time light intensity as the horizontal axis data into the power generation prediction function to obtain the vertical value, and mark it as the real-time predicted power generation. Step S302: Obtain real-time power consumption; that is, the real-time power consumption, and store the remaining power consumption. Step S303: Subtract the real-time power consumption from the predicted power generation and mark it as the predicted charging power; the predicted charging power is the theoretical charging power. In practical applications, 600W / m 2 If the horizontal axis data is substituted into the power generation prediction function and the vertical axis value is 150W, then the real-time predicted power generation is 150W. For example, if the real-time power consumption is 0, then the predicted charging power is 150W.

[0021] Step S4: Obtain a first power threshold and a second power threshold for different predicted charging powers; Step S4 includes the following sub-steps: Step S401: Under the same predicted charging power and normal charging conditions, obtain a second number of actual battery charging powers and mark them as historical normal charging powers; for example, when the predicted charging power is 100W, obtain a second number of historical normal charging powers; in order to obtain the distribution of historical normal charging powers, the second number should not be set too small, for example, the second number is 500. Step S402: Establish a Cartesian coordinate system with historical normal charging power as the horizontal axis data and the number of historical normal charging powers as the vertical axis data, and mark it as the power distribution coordinate system. Step S403: Obtain the coordinate points of the historical normal charging power and the number of historical normal charging powers as the horizontal and vertical coordinates, respectively, and mark them as power distribution coordinate points; Step S404: Plot all power distribution coordinate points in the power distribution coordinate system; Step S405: Perform function fitting on all power distribution coordinate points to obtain the function, which is then labeled as the power distribution function; this is to observe the historical normal charging power distribution under the same predicted charging power. For practical applications, please refer to Figure 2 As shown, the power distribution function is obtained.

[0022] Step S406: Within the range of historical normal charging power, obtain the area enclosed by the lower part of the power distribution function and the horizontal axis of the power distribution coordinate system, and mark it as the overall distribution area. Step S407: Obtain the area of ​​the entire distribution region and mark it as the overall distribution area; Step S408: Obtain the maximum value of the ordinate among all power distribution coordinate points and mark it as the power distribution height; Step S409: Set a length, marked as the first set length; the first set length is for constructing a distribution judgment rectangle, which is used to analyze the distribution of historical normal charging power. Therefore, the first set length should not be too large, for example, the first set length is 1cm; where 1W in the power distribution coordinate system corresponds to 1cm. Step S410: Create a rectangle on the horizontal axis of the power distribution coordinate system with a height equal to the power distribution height and a width equal to the first set length, and be able to move left and right. Mark it as the distribution judgment rectangle. Step S411, obtain the average area of ​​the rectangle as: K2=K1×(F1÷F2;where K2 is the average area of ​​the rectangle, K1 is the overall area of ​​the distribution, F1 is the first set length, and F2 is the length of the historical normal charging power range in the power distribution coordinate system; For practical applications, please refer to Figure 3 As shown, for example, the total area of ​​the distribution is 63 cm². 2 The first setting is a length of 1cm, and F2 is 7cm. The average area of ​​the rectangle is: K2 = 63 × (1 ÷ 7) = 9cm. 2 ; Step S412: Set a ratio value and mark it as the first ratio value; obtain the product of the average area of ​​the rectangle and the first ratio value and mark it as the area anomaly threshold; the area anomaly threshold is set to filter out a small number of historical normal charging powers, so the first ratio value is set to a small value, for example, the first ratio value is 0.1. For practical applications, please refer to Figure 3 As shown, the obtained area anomaly threshold is 0.9 cm. 2 .

[0023] Step S413: Obtain the area of ​​the intersection of the distribution judgment rectangle and the overall distribution area, and mark it as the real-time rectangle area; Step S414: Move the distribution judgment rectangle from the leftmost side of the power distribution function to the right. When the area of ​​the real-time rectangle is greater than or equal to the area anomaly threshold, stop moving the distribution judgment rectangle. Obtain the historical normal charging power corresponding to the minimum horizontal coordinate of the distribution judgment rectangle at this time and mark it as the first power threshold. Filter out the historical normal charging power that is too small, and then obtain the accurate minimum value of historical normal charging power. Step S415: Move the distribution judgment rectangle from the rightmost side of the power distribution function to the left. When the area of ​​the real-time rectangle is greater than or equal to the area anomaly threshold, stop moving the distribution judgment rectangle. Obtain the historical normal charging power corresponding to the largest horizontal coordinate of the distribution judgment rectangle at this time and mark it as the second power threshold. Filter out excessively large historical normal charging power to obtain the accurate maximum value of historical normal charging power. In practical applications, the predicted charging power is 100W. Please refer to [link / reference]. Figure 3 As shown, the distribution judgment rectangle is shifted to the right from the leftmost side of the power distribution function. When the area of ​​the real-time rectangle is greater than or equal to 0.9 cm², the distribution is determined. 2 When the moving distribution is stopped, the first power threshold obtained is 85; similarly, the second power threshold obtained is 90.

[0024] Step S5: Obtain a first threshold function based on a first power threshold; Step S5 includes the following sub-steps: Step S501: Obtain the first power threshold and the second power threshold corresponding to different predicted charging powers; Step S502: Mark the predicted charging power as the historical predicted charging power; Step S503: Using the historical predicted charging power as the horizontal axis value and the first power threshold as the vertical axis value, mark it as the first threshold coordinate system; Step S504: Mark the coordinate points with the historical predicted charging power and the corresponding first power threshold as the first threshold coordinate points, respectively. Step S505: Perform function fitting on all first threshold coordinate points to obtain a function, which is then labeled as the first threshold function; the minimum real-time battery power under normal conditions for different historical predicted charging power. For practical applications, please refer to Figure 4 As shown, the first threshold function is obtained.

[0025] Step S6: Obtain the second threshold function based on the second power threshold; Step S6 includes the following sub-steps: Step S601: Using the historical predicted charging power as the horizontal axis value and the second power threshold as the vertical axis value, mark it as the second threshold coordinate system; Step S602: Mark the coordinate points of the historical predicted charging power and the corresponding second power threshold as the second threshold coordinate points, respectively. Step S603: Perform function fitting on all the second threshold coordinate points to obtain the function, and mark it as the second threshold function; the maximum real-time battery power under normal conditions for different historical predicted charging power; For practical applications, please refer to Figure 5 As shown, the second threshold function is obtained.

[0026] Step S7: Obtain the first real-time power threshold and the second real-time power threshold based on the predicted charging power, the first threshold function, and the second threshold function; Step S7 includes the following sub-steps: Step S701: Substitute the real-time predicted charging power as the horizontal axis into the first threshold function to obtain the vertical axis, and mark it as the first real-time power threshold. Step S702: Substitute the real-time predicted charging power as the horizontal axis into the second threshold function to obtain the vertical axis, and mark it as the second real-time power threshold. In practical applications, substituting 150W as the horizontal axis into the first threshold function yields 127.5W as the vertical axis, thus the first real-time power threshold is 127.5W; substituting 150W as the horizontal axis into the second threshold function yields 135W as the vertical axis, thus the second real-time power threshold is 135W.

[0027] Step S8 involves evaluating the charging process based on real-time battery power, a first real-time power threshold, and a second real-time power threshold, and optimizing the charging control based on the evaluation results. Step S8 includes the following sub-steps: Step S801: If the real-time battery power is not within the range of the first real-time power threshold to the second real-time power threshold, it indicates that the charging is abnormal; if the real-time battery power is within the range of the first real-time power threshold to the second real-time power threshold, it indicates that the charging is normal. In practical applications, a real-time battery power of 132W within the range of the first real-time power threshold of 127.5W to the second real-time power threshold of 135W indicates that the battery is charging normally and no collaborative control optimization is needed.

[0028] Example 2: This application also provides an electronic device, which may include: a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus. The memory stores computer-readable instructions, and the processor can call the instructions in the memory. When the computer-readable instructions are executed by the processor, steps such as those in the IoT-based photovoltaic-storage-charging collaborative control method are performed to achieve the following functions: acquiring real-time light intensity and real-time battery power; acquiring historical photovoltaic power generation data and constructing a power generation prediction function based on the historical photovoltaic power generation data; acquiring real-time predicted charging power based on real-time light intensity and the photovoltaic power generation prediction function; acquiring a first power threshold and a second power threshold for different predicted charging powers; acquiring a first threshold function based on the first power threshold; acquiring a second threshold function based on the second power threshold; acquiring a first real-time power threshold and a second real-time power threshold based on the predicted charging power, the first threshold function, and the second threshold function; evaluating charging based on real-time battery power, the first real-time power threshold, and the second real-time power threshold, and optimizing charging control based on the evaluation results.

[0029] Furthermore, when the logical instructions in the aforementioned memory can be implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0030] Example 3: This application also provides a computer program product, which includes a computer program stored on a computer-readable storage medium. The computer program includes program instructions. When the program instructions are executed by a computer, the computer can execute the IoT-based photovoltaic-storage-charging coordinated control method provided by the above methods. This method includes: acquiring real-time light intensity and real-time battery power; acquiring historical photovoltaic power generation data and constructing a power generation prediction function based on the historical photovoltaic power generation data; acquiring real-time predicted charging power based on real-time light intensity and the photovoltaic power generation prediction function; acquiring a first power threshold and a second power threshold for different predicted charging powers; acquiring a first threshold function based on the first power threshold; acquiring a second threshold function based on the second power threshold; acquiring a first real-time power threshold and a second real-time power threshold based on the predicted charging power, the first threshold function, and the second threshold function; evaluating charging based on real-time battery power, the first real-time power threshold, and the second real-time power threshold, and optimizing charging control based on the evaluation results.

[0031] Example 4: This application also provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it performs the steps of the above-mentioned IoT-based photovoltaic-storage-charging coordinated control method to achieve the following functions: acquiring real-time light intensity and real-time battery power; acquiring historical photovoltaic power generation data and constructing a power generation prediction function based on the historical photovoltaic power generation data; acquiring real-time predicted charging power based on real-time light intensity and the photovoltaic power generation prediction function; acquiring a first power threshold and a second power threshold for different predicted charging powers; acquiring a first threshold function based on the first power threshold; acquiring a second threshold function based on the second power threshold; acquiring a first real-time power threshold and a second real-time power threshold based on the predicted charging power, the first threshold function, and the second threshold function; evaluating charging based on real-time battery power, the first real-time power threshold, and the second real-time power threshold, and optimizing charging control based on the evaluation results.

[0032] Based on the above description of the embodiments, the embodiments of the present invention can be provided as methods, systems, or computer program products. Based on this understanding, the above technical solutions, in essence or in terms of their contribution to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or certain parts of the embodiments.

[0033] In the embodiments provided in this application, it should be understood that the disclosed system or method can be implemented in other ways. The embodiments described above are merely illustrative. For example, the division of modules or units is only a logical functional division, and there may be other division methods in actual implementation. Furthermore, multiple modules or units may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the coupling or direct coupling or communication connection shown or discussed may be through some communication interfaces. The indirect coupling or communication connection between systems, modules, and units may be electrical, mechanical, or other forms.

[0034] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A photoelectric storage-charging coordinated control method based on Internet of Things (IoT) sensing, characterized in that, Includes the following steps: Obtain real-time light intensity and real-time battery power; Obtain historical photovoltaic power generation data and construct a power generation prediction function based on the historical photovoltaic power generation data; Real-time predicted charging power is obtained based on real-time light intensity and photovoltaic power generation prediction function; Obtain a first power threshold and a second power threshold for different predicted charging powers; The first threshold function is obtained based on the first power threshold. The second threshold function is obtained based on the second power threshold. The first real-time power threshold and the second real-time power threshold are obtained based on the predicted charging power, the first threshold function, and the second threshold function. The charging process is evaluated based on real-time battery power, a first real-time power threshold, and a second real-time power threshold, and the charging control is optimized based on the evaluation results.

2. The photoelectric storage and charging coordinated control method based on IoT sensing according to claim 1, characterized in that, Constructing a power generation prediction function based on historical photovoltaic power generation data includes the following sub-steps: Historical photovoltaic power generation data includes different light intensities and their corresponding power generation, which are marked as historical light intensity and historical power generation, respectively. Obtain the first number of historical light intensities and their corresponding historical power generation; A Cartesian coordinate system was established with historical solar intensity as the horizontal axis data and historical power generation as the vertical axis data, and it was marked as the power generation prediction coordinate system. The coordinate points with historical light intensity and corresponding historical power generation as the x and y axes, respectively, are marked as power generation prediction coordinate points; The function is obtained by fitting a function to all the power generation prediction coordinate points, and it is labeled as the power generation prediction function.

3. The photoelectric storage and charging coordinated control method based on IoT sensing according to claim 2, characterized in that, Obtaining the real-time predicted charging power based on real-time light intensity and photovoltaic power generation prediction function includes the following sub-steps: Substitute the real-time light intensity as the horizontal axis data into the power generation prediction function to obtain the vertical value, which is then marked as the real-time predicted power generation. Obtain real-time power consumption; The value obtained by subtracting the real-time power consumption from the predicted power generation is tagged as the predicted charging power.

4. The photoelectric storage and charging coordinated control method based on IoT sensing according to claim 3, characterized in that, Obtaining the first and second power thresholds for different predicted charging powers includes the following sub-steps: Under normal charging conditions with the same predicted charging power, a second number of actual battery charging powers are obtained and marked as historical normal charging powers. A Cartesian coordinate system is established with historical normal charging power as the horizontal axis data and the number of historical normal charging powers as the vertical axis data, and it is marked as the power distribution coordinate system. The coordinates of the historical normal charging power and the number of historical normal charging powers are obtained as the x and y coordinates, respectively, and marked as power distribution coordinate points; Plot all power distribution coordinate points in the power distribution coordinate system; The function is obtained by fitting a function to all the power distribution coordinate points, and it is labeled as the power distribution function.

5. The photoelectric storage-charging coordinated control method based on IoT sensing according to claim 4, characterized in that, Obtaining the first and second power thresholds for different predicted charging powers also includes the following sub-steps: Within the range of historical normal charging power, the area enclosed by the power distribution function and the horizontal axis of the power distribution coordinate system is obtained and marked as the overall distribution area; Obtain the area of ​​the entire distribution region and label it as the total area of ​​the distribution; Obtain the maximum value of the ordinate among all power distribution coordinate points and mark it as the power distribution height; Set a length and mark it as the first set length; Create a rectangle on the horizontal axis of the power distribution coordinate system with a height equal to the power distribution height and a width equal to the first set length, and be able to move left and right. Mark it as the distribution judgment rectangle. The average area of ​​the rectangle is obtained as: K2 = K1 × (F1 ÷ F2); where K2 is the average area of ​​the rectangle, K1 is the overall area of ​​the distribution, F1 is the first set length, and F2 is the length of the historical normal charging power range in the power distribution coordinate system; Set a ratio value, which is marked as the first ratio value; obtain the product of the average area of ​​the rectangle and the first ratio value, and mark it as the area anomaly threshold.

6. The photoelectric storage-charging coordinated control method based on IoT sensing according to claim 5, characterized in that, Obtaining the first and second power thresholds for different predicted charging powers also includes the following sub-steps: Obtain the area of ​​the intersection between the distribution judgment rectangle and the overall distribution region, and mark it as the real-time rectangle area; The distribution judgment rectangle is shifted to the right from the leftmost side of the power distribution function. When the area of ​​the real-time rectangle is greater than or equal to the area anomaly threshold, the movement of the distribution judgment rectangle is stopped. The historical normal charging power corresponding to the minimum x-coordinate of the distribution judgment rectangle at this time is obtained and marked as the first power threshold. The distribution judgment rectangle is shifted to the left from the rightmost side of the power distribution function. When the area of ​​the real-time rectangle is greater than or equal to the area anomaly threshold, the movement of the distribution judgment rectangle is stopped. The historical normal charging power corresponding to the largest horizontal coordinate of the distribution judgment rectangle at this time is obtained and marked as the second power threshold.

7. The photoelectric storage-charging coordinated control method based on IoT sensing according to claim 6, characterized in that, The function for obtaining the first threshold based on the first power threshold includes the following sub-steps: Obtain the first power threshold and the second power threshold corresponding to different predicted charging powers; Mark the predicted charging power as the historical predicted charging power; Using historical predicted charging power as the horizontal axis value and the first power threshold as the vertical axis value, this is marked as the first threshold coordinate system; The coordinate points with the historical predicted charging power and the corresponding first power threshold as the x and y coordinates are marked as the first threshold coordinate points; The function is obtained by fitting a function to all the first threshold coordinate points, and it is marked as the first threshold function.

8. The photoelectric storage-charging coordinated control method based on IoT sensing according to claim 7, characterized in that, The function for obtaining the second threshold based on the second power threshold includes the following sub-steps: The coordinate system is marked with the historical predicted charging power as the horizontal axis value and the second power threshold as the vertical axis value. The coordinate points with the historical predicted charging power and the corresponding second power threshold as the x and y coordinates are marked as the second threshold coordinate points; The function is obtained by fitting all the second threshold coordinate points to a function, and it is marked as the second threshold function.

9. The photoelectric storage-charging coordinated control method based on IoT sensing according to claim 8, characterized in that, Obtaining the first real-time power threshold and the second real-time power threshold based on the predicted charging power, the first threshold function, and the second threshold function includes the following sub-steps: Substitute the real-time predicted charging power as the horizontal axis into the first threshold function to obtain the vertical axis, which is marked as the first real-time power threshold. Substituting the real-time predicted charging power as the horizontal axis into the second threshold function yields the vertical axis, which is then labeled as the second real-time power threshold.

10. The photoelectric storage-charging coordinated control method based on IoT sensing according to claim 9, characterized in that, The charging process is evaluated based on real-time battery power, a first real-time power threshold, and a second real-time power threshold. The optimization of charging control based on the evaluation results includes the following sub-steps: If the real-time battery power is not within the range of the first real-time power threshold to the second real-time power threshold, it indicates that the charging is abnormal. If the real-time battery power is within the range of the first real-time power threshold to the second real-time power threshold, it indicates that the charging is normal.

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