Self-heating regulation and control method for photovoltaic snow protection shed tunnel for highway tunnel

Abstract

The invention relates to the technical field of highway tunnel photovoltaic snow protection shed tunnels, in particular to a highway tunnel photovoltaic snow protection shed tunnel self-heating regulation and control method which comprises the steps that the actual value of the accumulated snow thickness is collected, the estimated accumulated snow thickness value and the expected value are obtained, and when the actual expected value is smaller than the estimated expected value, the photovoltaic snow protection shed tunnel self-heating regulation and control method is started. And carrying out heating power adjustment on the photovoltaic panel partition based on the difference value. Through combination of multi-dimensional accumulated snow detection and regional accurate heating, the accumulated snow removing efficiency is improved, the heating energy consumption is optimized, stable operation in extreme weather is guaranteed, and the complex working condition requirements are met. In addition, the invention also provides a storage medium, an electronic device and a computer program product to implement the method.

Description

Technical Field

[0001] The present invention relates to the field of highway tunnels and new energy application technology, and in particular to a self-heating control method for a photovoltaic snowproof shed tunnel used in a highway tunnel. Background Art

[0002] When photovoltaic snow shelters in highway tunnels are covered in snow in winter, their power generation efficiency is significantly affected. Currently, commonly used methods for measuring snow thickness rely on single sensors or image recognition technology. However, due to the uneven distribution of snow and interference from environmental factors, detection results often contain large errors, making it difficult for heating systems to accurately match actual needs. Furthermore, traditional heating methods with electric heating wires consume high energy and have uneven temperature distribution, which can easily lead to localized overheating or insufficient heating.

[0003] To address these issues, various improvements have been proposed. For example, adding a heating coating to the surface of photovoltaic panels or implementing a zoned heating control strategy have been proposed. However, these approaches have limited adaptability to complex operating conditions, particularly during extreme snowfall, when the photovoltaic system's power supply capacity decreases, potentially leading to heating interruptions. Furthermore, some solutions attempt to recover excess heat through thermal storage devices, but the design of the energy management interface still needs to be optimized to achieve rapid response and efficient switching.

[0004] Therefore, there is an urgent need for a self-heating control method for photovoltaic snow sheds that can comprehensively improve the snow thickness detection accuracy, optimize the heating efficiency, and ensure stable operation in extreme weather conditions. Summary of the Invention

[0005] The purpose of the present invention is to provide a self-heating control method for photovoltaic snow shelters for highway tunnels. By combining multi-dimensional snow detection with precise regional heating, the method improves the accuracy of snow thickness detection, optimizes heating efficiency, and ensures stable operation in extreme weather conditions.

[0006] In order to achieve the above-mentioned purpose, the present invention provides a self-heating control method for a photovoltaic snowproof shed tunnel for a highway tunnel, the self-heating control method comprising: collecting a first cumulative actual value of the snow thickness of multiple partitions on the surface of a photovoltaic panel as of the current moment in the current period of a target date; obtaining an estimated snow thickness value in the current period of the target date based on the target snow removal amount on the surface of the photovoltaic panel and the snow thickness data on the surface of the photovoltaic panel in the current period of multiple preset dates before the target date; and obtaining an estimated snow thickness value in the current period of the target date when the first cumulative actual value is less than the estimated snow thickness value. the actual expected value of the snow thickness on the photovoltaic panel surface at the current moment in the target date; based on the target snow removal amount on the photovoltaic panel surface and the snow thickness data on the photovoltaic panel surface in each time period in the multiple preset dates, obtain the estimated expected value of the snow thickness on the photovoltaic panel surface at the current moment in the current time period in the target date; and in a case where the actual expected value of the snow thickness on the photovoltaic panel surface is less than the estimated expected value of the snow thickness on the photovoltaic panel surface, adjust the heating power of the to-be-controlled zone on the photovoltaic panel surface based on the difference between the estimated expected value and the actual expected value of the snow thickness on the photovoltaic panel surface.

[0007] In at least one embodiment, obtaining the estimated snow thickness value in the current time period in the target date includes: determining the estimated snow thickness value in the current time period based on the target snow removal amount on the photovoltaic panel surface in the target date and the snow thickness data on the photovoltaic panel surface in the current time period in the multiple preset dates, wherein the target snow removal amount on the photovoltaic panel surface is associated with the snow thickness data on the photovoltaic panel surface in the multiple preset dates; updating the estimated snow thickness value in the current time period based on the expected snow thickness value in the current time period determined at the previous moment of the current moment and the first accumulated actual value to obtain the estimated snow thickness value in the current time period corresponding to the current moment.

[0008] In at least one embodiment, obtaining the actual expected value of the snow thickness on the photovoltaic panel surface at the current moment in the current period of the target date includes: obtaining the expected value of the snow thickness on the photovoltaic panel surface based on the snow thickness values on the photovoltaic panel surface at each partition at the current moment and the average value of the snow thickness on the photovoltaic panel surface at each partition in each preset date of the multiple preset dates; and determining the actual expected value of the snow thickness on the photovoltaic panel surface based on the ambient temperature, wind speed and the expected value of the snow thickness on the photovoltaic panel surface.

[0009] In at least one embodiment, obtaining the estimated expected value of the snow thickness on the photovoltaic panel surface at the current moment in the current period in the target date includes: obtaining the estimated probability of the snow thickness on the photovoltaic panel surface in the current period in the target date based on the target snow removal amount on the photovoltaic panel surface and the snow thickness data on the photovoltaic panel surface in the current period in the multiple preset dates; obtaining the second cumulative actual snow thickness value on the photovoltaic panel surface in the target date up to the current moment in the current period; obtaining the second cumulative estimated snow thickness value on the photovoltaic panel surface in the target date up to the current moment in the current period; and determining the estimated expected value of the snow thickness on the photovoltaic panel surface at the current moment in the current period based on the estimated probability of snow thickness on the photovoltaic panel surface, the second cumulative actual snow thickness value, the second cumulative estimated snow thickness value and the global thermal energy distribution state of the photovoltaic panel surface.

[0010] In at least one embodiment, obtaining the second cumulative estimated snow thickness value of the photovoltaic panel surface as of the current moment in the current period in the target date includes: determining the estimated snow thickness value of the photovoltaic panel surface in each time period based on the target snow removal amount on the photovoltaic panel surface in the target date and the snow thickness data on the photovoltaic panel surface in each time period in the multiple preset dates before the target date, wherein the target snow removal amount on the photovoltaic panel surface is associated with the snow thickness data on the photovoltaic panel surface in the multiple preset dates; determining the third cumulative actual snow thickness value of the photovoltaic panel surface as of each moment in each time period before the current period and the third cumulative actual snow thickness value of the photovoltaic panel surface Calculate the estimated snow thickness value and the fourth cumulative actual snow thickness value and the fourth cumulative estimated snow thickness value of the photovoltaic panel surface in the current period up to the current moment, update the estimated snow thickness values of the photovoltaic panel surface in each period before the current period and in the current period to obtain the updated estimated snow thickness values of the photovoltaic panel surface in each period before the current period and in the current period; and determine the second cumulative estimated snow thickness value of the photovoltaic panel surface up to the current moment in the current period in the target date based on the updated estimated snow thickness values of the photovoltaic panel surface in each period before the current period and in the current period.

[0011] In at least one embodiment, determining the estimated expected value of the snow thickness on the photovoltaic panel surface at the current moment in the current time period includes: determining the control expected value of the snow thickness on the photovoltaic panel surface at the current moment in the current time period based on the estimated probability of the snow thickness on the photovoltaic panel surface, the second cumulative actual snow thickness value and the second cumulative estimated snow thickness value; and determining the estimated expected value of the snow thickness on the photovoltaic panel surface at the current moment in the current time period based on the global thermal energy distribution state of the photovoltaic panel surface and the control expected value of the snow thickness on the photovoltaic panel surface at the current moment in the current time period.

[0012] In at least one embodiment, the zones of the photovoltaic panel surface include at least one of: a photovoltaic panel top region, a photovoltaic panel middle region, and a photovoltaic panel bottom region.

[0013] In at least one embodiment, adjusting the heating power of the to-be-regulated zone on the surface of the photovoltaic panel includes: determining the to-be-regulated zone based on the difference between the estimated expected value and the actual expected value of the snow thickness on the surface of the photovoltaic panel; and adjusting the heating power of the to-be-regulated zone.

[0014] In at least one embodiment, the heating power adjustment for the partition to be regulated on the surface of the photovoltaic panel also includes: determining the expected value of the snow thickness on the surface of the photovoltaic panel after adjustment and counting the number of adjustments; determining the expected loss of snow thickness on the surface of the photovoltaic panel based on the actual expected value of snow thickness in each partition of the surface of the photovoltaic panel and the expected value of snow thickness on the surface of the photovoltaic panel after adjustment; and continuing to adjust the heating power for the partition to be regulated on the surface of the photovoltaic panel when the counted number of adjustments is less than the preset number and the expected loss of snow thickness on the surface of the photovoltaic panel is less than the loss threshold.

[0015] Through the above technical solution, the present invention creatively obtains the actual expected value of the snow thickness on the photovoltaic panel surface at the current moment in the current period when the first cumulative actual value of the snow thickness of multiple partitions on the photovoltaic panel surface in the current period of the target date is less than the estimated snow thickness value in the current period; then, based on the target snow removal amount on the photovoltaic panel surface and the snow thickness data on the photovoltaic panel surface in each period of the multiple preset dates, obtains the estimated expected value of the snow thickness on the photovoltaic panel surface at the current moment in the current period of the target date; then, when the actual expected value of the snow thickness on the photovoltaic panel surface is less than the estimated expected value of the snow thickness on the photovoltaic panel surface, the heating power of the partition to be controlled on the photovoltaic panel surface is adjusted based on the difference between the estimated expected value and the actual expected value of the snow thickness on the photovoltaic panel surface. Therefore, the present invention can accurately meet the demand for efficient snow removal of photovoltaic snow sheds under complex working conditions.

[0016] A second aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the method for controlling the self-heating of a photovoltaic snow shed used in a highway tunnel is implemented.

[0017] The third aspect of the present invention provides an electronic device, which includes: a processor; a memory for storing executable instructions of the processor; the processor is used to read the executable instructions from the memory and execute the instructions to implement the above-mentioned photovoltaic snow shed tunnel self-heating control method for highway tunnels.

[0018] A fourth aspect of the present invention provides a computer program product, which includes a computer program. When the computer program is executed by a processor, it implements the above-mentioned photovoltaic snow shed tunnel self-heating control method for highway tunnels. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 Schematic diagram of the process of the self-heating control method of the photovoltaic snowproof shed cave in an embodiment of the present invention;

[0020] Figure 2 Schematic diagram of the photovoltaic panel surface partitioning in an embodiment of the present invention;

[0021] Figure 3 2 is a structural block diagram of an electronic device in an embodiment of the present invention.

[0022] In the figure: 1. Top area of photovoltaic panel; 2. Middle area of photovoltaic panel; 3. Bottom area of photovoltaic panel; 4. Processor; 5. Memory. DETAILED DESCRIPTION

[0023] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0024] The present invention provides a self-heating control method for a photovoltaic snowproof shed used in a highway tunnel. Figure 1 To the attached Figure 3 Detailed description is given. In practical applications, photovoltaic snow shelters are usually installed at the entrance or exit of highway tunnels to provide clean energy for the power supply system in the tunnel. Since snow accumulation in winter will have a significant impact on the power generation efficiency of photovoltaic panels, an efficient snow removal method is needed. The present invention collects snow thickness data from multiple partitions on the surface of photovoltaic panels and analyzes it in combination with historical data, ultimately achieving adjustment of the heating power of the partition to be controlled on the surface of the photovoltaic panel. The implementation process of this method will be described in detail below.

[0025] First, if Figure 1 As shown, the first step of the present invention is to collect the first accumulated actual value of the snow thickness of multiple sub-regions of the photovoltaic panel surface within the current period of the target date up to the current moment. The photovoltaic panel surface is divided into multiple sub-regions, including the photovoltaic panel top region 1, the photovoltaic panel middle region 2, and the photovoltaic panel bottom region 3, as shown in FIG. Figure 2 As shown. Each partition is equipped with a snow thickness sensor, which is installed in the corresponding area of the photovoltaic panel for real-time monitoring of the snow thickness of each partition. The snow thickness sensor is connected to the processor 4 via a signal line, and the processor 4 receives and stores data from the sensor. In this embodiment, the first accumulated actual value refers to the cumulative value of the snow thickness data collected by the snow thickness sensors of each partition from the current period to the current moment. For example, at a certain moment, the snow thickness detected by the snow thickness sensor in the top area 1 of the photovoltaic panel is 2 mm, the snow thickness in the middle area 2 of the photovoltaic panel is 3 mm, and the snow thickness in the bottom area 3 of the photovoltaic panel is 4 mm. The first accumulated actual value is the sum of these three values.

[0026] Next, an estimated snow thickness value for the current period on the target date is obtained based on the target snow removal amount on the photovoltaic panel surface and the snow thickness data for the photovoltaic panel surface during the current period on multiple preset dates prior to the target date. The target snow removal amount refers to the maximum allowable snow thickness set based on the design and operational requirements of the photovoltaic panel. For example, if the design requirements for the photovoltaic panel require a maximum allowable snow thickness of 10 mm, then the target snow removal amount is 10 mm. The snow thickness data for the photovoltaic panel surface during the current period on multiple preset dates refers to the snow thickness data for the same period on several days selected prior to the target date. This data can be stored in memory 5 and accessed by processor 4 for calculation. In this embodiment, processor 4 retrieves historical data from memory 5, combines it with the target snow removal amount, and uses a weighted average algorithm to calculate the estimated snow thickness value for the current period. For example, if the historical data indicates that the snow thickness for the current period was 8 mm, 9 mm, and 10 mm, respectively, then the weighted average algorithm can be used to calculate the estimated snow thickness value to be 9 mm.

[0027] After obtaining the estimated snow thickness value, it is necessary to determine whether the first accumulated actual value is less than the estimated snow thickness value. If the first accumulated actual value is less than the estimated snow thickness value, the next step is to obtain the actual expected snow thickness value on the photovoltaic panel surface at the current time within the current time period of the target date. The actual expected value is obtained as follows: First, the expected snow thickness value on the photovoltaic panel surface is obtained based on the snow thickness value on the photovoltaic panel surface in each sub-area at the current time and the average snow thickness value on the photovoltaic panel surface in each sub-area for each of the multiple preset dates. For example, if the snow thickness on the top area 1 of the photovoltaic panel is 2 mm, the snow thickness on the middle area 2 of the photovoltaic panel is 3 mm, and the snow thickness on the bottom area 3 of the photovoltaic panel is 4 mm, the average values calculated based on historical data are 2.5 mm, 3.5 mm, and 4.5 mm, respectively. Next, the actual expected snow thickness value on the photovoltaic panel surface is determined based on the ambient temperature, wind speed, and the expected snow thickness value on the photovoltaic panel surface. For example, if the current ambient temperature is -5 degrees Celsius and the wind speed is 3 meters per second, the actual expected value calculated according to the preset mathematical model is 3 millimeters.

[0028] After obtaining the actual expected value, it is also necessary to obtain an estimated expected value for the snow thickness on the photovoltaic panel surface at the current time within the current time period of the target date. The estimated expected value is obtained as follows: First, based on the target snow removal amount on the photovoltaic panel surface and the snow thickness data on the photovoltaic panel surface during the current time period of multiple preset dates, an estimated probability of the snow thickness on the photovoltaic panel surface during the current time period of the target date is obtained. For example, if the target snow removal amount is 10 mm, and historical data shows snow thicknesses of 8 mm, 9 mm, and 10 mm during the current time period, the calculated estimated probability is 0.3. Next, the second cumulative actual snow thickness value and the second cumulative estimated snow thickness value on the photovoltaic panel surface from the target date up to the current time period are obtained. For example, if the second cumulative actual snow thickness value at the current time period is 12 mm and the second cumulative estimated snow thickness value at the current time period is 15 mm, the estimated expected value for the snow thickness on the photovoltaic panel surface at the current time period of the target date is determined by combining the estimated probability and the global thermal energy distribution state. For example, if the global heat energy distribution state is uniform, the estimated expected value is calculated to be 4 mm.

[0029] After obtaining the actual expected value and the estimated expected value, it is necessary to determine whether the actual expected value is less than the estimated expected value. If the actual expected value is less than the estimated expected value, the heating power for the selected zone on the photovoltaic panel surface to be controlled is adjusted based on the difference between the estimated expected value and the actual expected value of the snow thickness on the photovoltaic panel surface. The specific adjustment process is as follows: First, the zone to be controlled is determined based on the difference between the estimated expected value and the actual expected value. For example, if the estimated expected value is 4 mm and the actual expected value is 3 mm, the difference is 1 mm. Based on the size of the difference, the zone to be heated is determined. For example, if the difference is large, heating priority is given to the top area 1 of the photovoltaic panel; if the difference is small, heating priority is given to the bottom area 3 of the photovoltaic panel. Next, the heating power for the selected zone to be controlled is adjusted. For example, if the top area 1 of the photovoltaic panel is selected, the processor 4 controls the heating device to heat this area, and the heating power is dynamically adjusted based on the size of the difference. For example, if the difference is 1 mm, the heating power is adjusted to 100 watts; if the difference is 2 mm, the heating power is adjusted to 200 watts.

[0030] After completing the heating power adjustment, the adjustment process needs to be further optimized. The specific optimization process is as follows: First, determine the expected value of the snow thickness on the surface of the photovoltaic panel after adjustment and count the number of adjustments. For example, if the adjusted expected value is 3.5 mm, the current number of adjustments is recorded as 1. Then, based on the actual expected value of the snow thickness of each partition on the photovoltaic panel surface and the expected value of the snow thickness on the photovoltaic panel surface after adjustment, determine the expected loss of snow thickness on the photovoltaic panel surface. For example, if the actual expected value is 3 mm and the adjusted expected value is 3.5 mm, the expected loss is 0.5 mm. Finally, determine whether the counted number of adjustments is less than the preset number and whether the expected loss of snow thickness on the photovoltaic panel surface is less than the loss threshold. For example, if the preset number is 5 times and the loss threshold is 1 mm, then if the number of adjustments is less than 5 times and the expected loss is less than 1 mm, continue to adjust the heating power of the partition to be regulated on the photovoltaic panel surface.

[0031] In the above process, processor 4 and memory 5 play a key role. Processor 4 is responsible for receiving data from the snow thickness sensor, calling historical data in memory 5 for calculation, and controlling the operation of the heating device according to the calculation results. Memory 5 is used to store information such as historical data, calculation results, and adjustment records. In addition, the partition design of the photovoltaic panel surface is also crucial. Figure 2 As shown, the division of the PV panel into top region 1, middle region 2, and bottom region 3 allows for more precise snow detection and heating control. For example, top region 1 typically has thicker snow and therefore requires higher heating power, while bottom region 3 has thinner snow and therefore requires lower heating power.

[0032] Through the above-mentioned specific embodiments, the present invention achieves efficient snow removal from the surface of photovoltaic panels. In practical applications, this method can significantly improve the accuracy of snow thickness detection, optimize heating efficiency, and ensure stable operation in extreme weather conditions. For example, during a winter snowfall, the thickness of snow on the surface of the photovoltaic panel increased rapidly. However, through the method of the present invention, the system can promptly detect the change in snow thickness and adjust the heating power of different partitions according to the actual situation, thereby effectively clearing the snow and ensuring the normal operation of the photovoltaic panel.

[0033] In order to better enable relevant personnel in this technical field to fully understand and implement the present invention, the specific implementation principle of the present invention is further supplemented below with reference to a specific application scenario.

[0034] During a snowfall in winter, the photovoltaic panels of the photovoltaic snow shed were gradually covered with snow. To ensure the normal operation of the photovoltaic system, the system started the self-heating control method. Figure 1As shown, processor 4 collects the first accumulated actual value for the current period via snow thickness sensors connected to the top, middle, and bottom regions 1, 2, and 3 of the photovoltaic panel. These sensors monitor the snow thickness in each region in real time and transmit the data to processor 4 for aggregation. For example, at a certain moment, if the snow thickness in the top region 1 of the photovoltaic panel is 5 mm, in the middle region 2 is 3 mm, and in the bottom region 3 is 2 mm, then the first accumulated actual value is 10 mm.

[0035] Next, processor 4 accesses historical data stored in memory 5 and, combined with the target snow removal volume, calculates an estimated snow thickness for the current time period. The target snow removal volume is set at 8 mm, and the historical data indicates snow thicknesses of 6 mm, 7 mm, and 8 mm during the current time period. Using a weighted average algorithm, processor 4 calculates an estimated snow thickness of 7 mm. Because the first accumulated actual value of 10 mm is greater than the estimated snow thickness of 7 mm, the system determines that further analysis of the snow distribution is necessary.

[0036] Processor 4 then obtains the actual expected snow thickness on the photovoltaic panel surface at the current moment. Based on the current snow thickness values for each subarea and the average value from historical data, the system calculates the expected snow thickness for the top region 1 of the photovoltaic panel to be 4 mm, for the middle region 2 to be 3 mm, and for the bottom region 3 to be 2 mm. Based on the current ambient temperature of -10 degrees Celsius and a wind speed of 5 meters per second, the system determines the actual expected snow thickness to be 3 mm using a pre-set mathematical model.

[0037] At the same time, processor 4 also obtains an estimated expected snow thickness on the photovoltaic panel surface. Based on the target snow removal volume of 8 mm and historical data, the system calculates an estimated snow thickness probability of 0.4 for the current time period. Furthermore, the system obtains a second cumulative actual snow thickness value of 12 mm and a second cumulative estimated snow thickness value of 15 mm. Combined with the global thermal energy distribution, the system ultimately determines the current expected snow thickness to be 4 mm.

[0038] After comparing the actual expected value with the estimated expected value, the system discovered that the actual expected value of 3 mm was less than the estimated expected value of 4 mm. Therefore, the system needed to adjust the heating power for the target area on the PV panel surface. Based on the 1 mm difference between the two, the system prioritized Area 1, the top of the PV panel with the thickest snow, as the target area. Processor 4 controlled the heating device in this area, dynamically adjusting the heating power to 100 watts to accelerate snow melting.

[0039] After completing the initial adjustments, the system further optimizes the control process. First, processor 4 records the adjusted expected snow thickness as 3.5 mm and counts the number of adjustments as 1. Next, based on the actual expected values for each zone and the adjusted expected values, the system calculates the expected loss in snow thickness to be 0.5 mm. Because the number of adjustments is less than the preset number of 5 and the expected loss is less than the loss threshold of 1 mm, the system decides to continue adjusting the heating power for zone 1 on top of the photovoltaic panel.

[0040] Throughout the entire process, the processor 4 and the memory 5 work together to ensure efficient processing and storage of data. Figure 3 As shown, the processor 4 is responsible for receiving the data from the snow thickness sensor and calling the historical data in the memory 5 for calculation, and controlling the operation of the heating device according to the calculation results. The memory 5 is used to store information such as historical data, calculation results, and adjustment records. The partition design of the photovoltaic panel surface also plays an important role, such as Figure 2 As shown, the division of the PV panel into top region 1, middle region 2, and bottom region 3 allows for more precise snow detection and heating control. For example, top region 1 of the PV panel typically has thicker snow and therefore requires higher heating power, while bottom region 3 of the PV panel typically has thinner snow and therefore requires lower heating power.

[0041] Through the above-mentioned specific steps, the present invention achieves efficient snow removal from photovoltaic panel surfaces. In practical applications, this method significantly improves the accuracy of snow thickness detection, optimizes heating efficiency, and ensures stable operation in extreme weather conditions. For example, during this snowfall, the system was able to promptly detect changes in snow thickness and adjust heating power to different zones based on actual conditions, effectively clearing snow and ensuring the normal operation of the photovoltaic panels.

[0042] Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments, or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A self-heating control method for a photovoltaic snowproof shed tunnel for a highway tunnel, characterized in that: The self-heating control method comprises: Obtaining first accumulated actual values of snow thickness in multiple subareas on the photovoltaic panel surface within a current period of a target date up to a current moment; Obtaining an estimated snow thickness value for the current period on the target date based on a target snow removal amount on the photovoltaic panel surface and snow thickness data on the photovoltaic panel surface for the current period on multiple preset dates before the target date; When the first accumulated actual value is less than the estimated snow thickness value, obtaining an actual expected value of the snow thickness on the photovoltaic panel surface at the current time within the current period of the target date; Obtaining an estimated expected value of the snow thickness on the photovoltaic panel surface at a current time in a current time period on the target date based on a target snow removal amount on the photovoltaic panel surface and snow thickness data on the photovoltaic panel surface in each time period on a plurality of preset dates; and When the actual expected value of the snow thickness on the photovoltaic panel surface is less than the estimated expected value of the snow thickness on the photovoltaic panel surface, the heating power of the to-be-controlled zone on the photovoltaic panel surface is adjusted based on the difference between the estimated expected value and the actual expected value of the snow thickness on the photovoltaic panel surface.

2. The photovoltaic snowproof shed cave self-heating control method according to claim 1 is characterized in that: Obtaining the estimated snow thickness value for the current period on the target date includes: determining an estimated snow thickness value on the photovoltaic panel surface for the current period based on a target snow removal amount on the photovoltaic panel surface for the target date and snow thickness data on the photovoltaic panel surface for the current period on a plurality of preset dates, wherein the target snow removal amount on the photovoltaic panel surface is associated with the snow thickness data on the photovoltaic panel surface for the plurality of preset dates; Based on the estimated snow thickness value for the current period determined at the previous moment before the current moment and the first accumulated actual value, the estimated snow thickness value for the current period is updated to obtain the estimated snow thickness value for the current period corresponding to the current moment.

3. The photovoltaic snowproof shed cave self-heating control method according to claim 1, characterized in that: The step of obtaining the actual expected value of the snow thickness on the photovoltaic panel surface at the current time within the current period of the target date includes: Obtaining an expected value of the snow thickness on the photovoltaic panel surface based on the snow thickness value on the photovoltaic panel surface at each subarea at the current moment and an average value of the snow thickness on the photovoltaic panel surface at each subarea on each of a plurality of preset dates; Based on the ambient temperature, wind speed, and the expected value of the snow thickness on the surface of the photovoltaic panel, an actual expected value of the snow thickness on the surface of the photovoltaic panel is determined.

4. The photovoltaic snowproof shed cave self-heating control method according to claim 1, characterized in that: The method of obtaining the estimated expected value of the snow thickness on the photovoltaic panel surface at the current time within the current period of the target date includes: Obtaining an estimated probability of snow thickness on the photovoltaic panel surface during the current time period on the target date based on a target snow removal amount on the photovoltaic panel surface and snow thickness data on the photovoltaic panel surface during the current time period on multiple preset dates; Obtain a second accumulated actual snow thickness value on the photovoltaic panel surface as of the current moment within the current period on the target date; Obtain a second cumulative estimated snow thickness value on the photovoltaic panel surface up to the current time within the current period on the target date; Based on the estimated probability of snow thickness on the photovoltaic panel surface, the second cumulative actual snow thickness value and the second cumulative estimated snow thickness value and the global thermal energy distribution state of the photovoltaic panel surface, the estimated expected value of snow thickness on the photovoltaic panel surface at the current moment in the current time period is determined.

5. The photovoltaic snowproof shed cave self-heating control method according to claim 1, characterized in that: The step of adjusting the heating power of the photovoltaic panel surface area to be controlled includes: Based on the difference between the expected value and the actual expected value of the snow thickness on the photovoltaic panel surface, the zone to be regulated is determined; Adjust the heating power of the selected zone to be regulated.

6. An electronic device, characterized in that: The electronic device comprises: Processor (4); a memory (5) for storing processor executable instructions; The processor (4) is used to read executable instructions from the memory (5) and execute the instructions to implement the photovoltaic snow shelter cave self-heating control method according to any one of claims 1 to 5.

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