Prediction method for snow freezing and thawing secondary disasters, storage medium and program product

Abstract

The invention provides a snow, ice and thawing secondary disaster prediction method, a storage medium and a program product, and the method comprises the steps: obtaining the current accumulated snow distribution of a target building, and predicting the prediction environment information of the target building within a set time period after the current moment, the predicted environment information comprises the outdoor temperature, the indoor temperature and the illumination intensity of the target building in the set time period; acquiring an accumulated snow melting model matched with the predicted environment information, and performing thermal radiation analysis on the current accumulated snow distribution by adopting the accumulated snow melting model so as to predict the accumulated snow change condition of the target building within the set time period; and carrying out stress analysis on the accumulated snow of the target building according to the accumulated snow change condition, and judging whether the target building has a snow layer sliding risk within the set time period or not according to an analysis result. According to the technical scheme of the invention, the accuracy of predicting the snow, ice and thawing secondary disasters of the building can be improved.

Description

Technical Field

[0001] This invention relates to the field of snow and ice disaster prevention technology, and in particular to a method, storage medium, and program product for predicting secondary disasters caused by snow and ice freezing and melting. Background Technology

[0002] In cases of heavy snowfall, snow accumulates on building roofs. After the snowfall ends, the temperature of the snow layer on the roof fluctuates dynamically due to the combined effects of natural heat exchange and building heating, directly influencing snow degradation. Snow degradation can cause secondary snow and ice melt disasters, such as snow sliding or icicles forming on buildings, both of which can affect the safety of people around the building. Therefore, predicting secondary snow and ice melt disasters is crucial for improving building safety. Summary of the Invention

[0003] This invention provides a method, storage medium, and program product for predicting secondary disasters caused by snow and ice freeze-thaw cycles, which improves the accuracy of predicting such disasters for buildings and thereby enhances building safety.

[0004] Specifically, in a first aspect, the present invention provides a method for predicting snow and ice freeze-thaw cycles and their associated disasters, comprising: The current snow cover distribution of the target building is obtained, and the predicted environmental information of the target building in the future within a set time period is predicted. The predicted environmental information includes the outdoor temperature, indoor temperature and light intensity of the target building in the set time period. A snow melting model matching the predicted environmental information is obtained, and the snow melting model is used to perform thermal radiation analysis on the current snow distribution in order to predict the snow change of the target building within the set time period. Based on the changes in snow accumulation, a stress analysis is performed on the snow accumulation on the target building, and based on the analysis results, it is determined whether the target building will experience snow slippage within the set time period.

[0005] Furthermore, after the step of determining whether the target building will experience snowfall within the set time period based on the analysis results, the method further includes: Obtain weather information after the set time period and determine whether the weather information meets the preset conditions for the formation of icicles on the target building; If so, the location where icicles will form on the target building will be predicted based on the weather information and the structure of the target building.

[0006] Furthermore, the step of obtaining the current snow distribution of the target building includes: Obtain the historical snow cover distribution of the target building at the end of the last snowfall, as well as the historical environmental information for each day since the last snowfall; Based on the historical environmental information, thermal radiation analysis is performed on the historical snow distribution to obtain the current snow distribution of the target building.

[0007] Further, the step of performing thermal radiation analysis on the historical snow distribution based on the historical environmental information to obtain the current snow distribution of the target building includes: A snow melting model matching the historical environmental information is obtained, and the snow melting model is used to perform radiative heat analysis on the historical snow distribution to obtain the current snow distribution.

[0008] Furthermore, the step of obtaining a snow melting model that matches the historical environmental information includes: using a wind, snow and heat full-process joint simulation test system to conduct thermal radiation tests on the snow of the target building under various preset environments, and constructing a snow melting model corresponding to each preset environment based on the test results; Obtain the preset environment with the highest similarity to the historical environmental information, and use the snow melting model corresponding to the preset environment as the snow melting model corresponding to the historical environmental information.

[0009] Furthermore, the step of determining whether the target building will experience snowfall within the set time period based on the analysis results includes: Based on the analysis results, determine whether the snow accumulation on the target building will slide. If so, the impact force caused by the snow sliding is obtained, and if the impact force is greater than the anti-slip threshold of the target building, it is determined whether the target building will experience snow sliding within the set time period.

[0010] Furthermore, the step of using the snow melting model to perform thermal radiation analysis on the current snow distribution to predict the snow changes of the target building within the set time period includes: dividing the set time period into multiple melting cycles, and periodically acquiring the snow changes of the target building based on the predicted environmental information of each melting cycle.

[0011] Furthermore, after the step of determining whether the target building will experience snowfall within the set time period based on the analysis results, the method further includes: If the target building is at risk of snowfall during the set time period, the time of snowfall is determined based on the snow accumulation changes in each melting cycle.

[0012] Secondly, the present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, it implements the steps of the method for predicting snow and ice freeze-thaw cycles as described in any of the above claims.

[0013] Thirdly, the present invention also provides a computer program product, which includes a computer program that, when executed by a processor, implements the steps of the method for predicting snow and ice freeze-thaw disasters as described in any of the above claims.

[0014] In the technical solution of this invention, a snow melting model is obtained based on the predicted environmental information of the target building within a set time period. This model accurately predicts the snow melting situation of the target building within the set time period. Then, based on the snow melting situation, a stress analysis is performed on the snow on the target building, and the analysis results determine whether snowfall will occur within the set time period. Because the technical solution of this invention can accurately predict the snow melting situation of the target building, it can accurately determine whether snowfall will occur within the set time period, thereby improving the accuracy of predicting secondary disasters caused by snow and ice freeze-thaw cycles and ultimately enhancing the safety of the building.

[0015] The above and other objects, advantages and features of the present invention will become more apparent to those skilled in the art from the following detailed description of specific embodiments of the invention in conjunction with the accompanying drawings. Attached Figure Description

[0016] The following sections will describe some specific embodiments of the invention in detail by way of example and not limitation, with reference to the accompanying drawings. The same reference numerals in the drawings denote the same or similar parts or portions. Those skilled in the art should understand that these drawings are not necessarily drawn to scale. In the drawings: Figure 1 This is a schematic flowchart of a method for predicting snow and ice freeze-thaw cycles according to an embodiment of the present invention; Figure 2 This is a schematic flowchart of a method for predicting snow and ice freeze-thaw cycles according to another embodiment of the present invention; Figure 3 This is a schematic flowchart illustrating the process of obtaining the current snow distribution of a target building in a prediction method according to an embodiment of the present invention. Figure 4 This is a schematic flowchart of a prediction method according to an embodiment of the present invention for obtaining a snow melting model corresponding to the historical environmental information of a target building; Figure 5This is a schematic flowchart illustrating a prediction method according to an embodiment of the present invention for determining whether a target building is at risk of snowfall within a set time period based on analysis results; Figure 6 This is a schematic diagram of a computer program product according to an embodiment of the present invention; and Figure 7 This is a schematic diagram of a computer-readable storage medium according to an embodiment of the present invention. Detailed Implementation

[0017] The following reference Figures 1 to 7 This invention describes a method, storage medium, and program product for predicting secondary disasters caused by snow and ice freeze-thaw cycles, according to embodiments of the present invention. In this description, it should be understood that the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature, that is, include one or more of that feature. In the description of this invention, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified. When a feature "includes or contains" one or more of the features it encompasses, unless otherwise specifically described, this indicates that other features are not excluded and may be further included.

[0018] In the description of this embodiment, the terms "one embodiment," "some embodiments," "illustrative embodiment," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0019] Please see Figure 1 , Figure 1 This is a schematic flowchart of a method for predicting snow and ice freeze-thaw cycles according to an embodiment of the present invention. The method generally includes: Step S101: Obtain the current snow distribution of the target building and predict the predicted environmental information of the target building within a set time period in the future; Step S102: Based on the predicted environmental information within a set time period, obtain the snow melting model of the target building in the future set time period, and use the snow distribution model to perform thermal radiation analysis on the current snow distribution of the target building to obtain the snow change of the target building within the set time period. Step S103: Perform a stress analysis on the snow accumulation of the target building based on the snow accumulation changes of the target building within a set time period, and determine whether there is a risk of snow slippage on the target building within the set time period based on the analysis results.

[0020] In step S101 above, the current snow distribution of the target building can be obtained by detecting the pressure caused by snow accumulation on the roof of the target building. For example, in this embodiment, pressure sensors can be installed at multiple preset locations on the roof of the target building. Each pressure sensor can detect the pressure caused by snow accumulation at its corresponding preset location, and the snow thickness at each preset location can be obtained based on the pressure. The current snow distribution of the target building can then be obtained based on the snow thickness at each preset location. In other embodiments, other methods can be used to obtain the current snow distribution of the target building.

[0021] The predicted environmental information of the target building over a set period of time includes the light intensity, outdoor temperature, and indoor temperature of the target building during that period. In this embodiment, multiple indoor temperatures of the target building over a set historical period can be obtained first, and the average of these multiple indoor temperatures can be used as the indoor temperature of the target building during the set period. Additionally, weather forecast information for the area where the target building is located can be obtained from a weather station during the set period, and the outdoor temperature and light intensity of the target building during the set period can be obtained based on this weather forecast information.

[0022] In step S102 above, the snow melting model of the target building over a set time period can be a neural network model based on a generative adversarial network. The neural network model includes a generator and a discriminator. The generator can generate a predicted output result that matches the input data, and the discriminator can identify the probability value of the predicted output result as a true result. If the probability value is greater than a set probability value, the predicted output result is taken as the true output result corresponding to the input data.

[0023] This embodiment can obtain a training dataset by detecting snow melting data of target buildings under the predicted environmental information within a set time period, and use the training dataset to train a neural network model to obtain a snow melting model for the set time period.

[0024] The snow melting model in this embodiment treats the snow on the target building as a growing object, and environmental information as a growth factor for this object. It can obtain the snow growth result under the influence of this growth factor, i.e., predict the changes in snow cover. Therefore, a snow melting model matching the predicted environmental information of the target building within a set time period can be obtained first. The current snow distribution of the target building is then input into this snow melting model, thereby obtaining the changes in snow cover on the target building within that set time period.

[0025] In step S103 above, the frictional force between the snow and the roof of the target building can be calculated based on the snow melting situation, and the snow can be determined whether it will slide off based on the frictional force and the weight of the snow; if it will, it is determined that there is a risk of snow sliding off the target building within a set time period.

[0026] In this embodiment, it is assumed that the tilt angle of the target building's roof is θ, the weight of the snow on the roof is G, and the coefficient of friction between the snow and the target building's roof is... The impact force F caused by the snow sliding off the target building is... If the impact force F is greater than 0, it can be determined that the target building is at risk of snow falling within a set time period.

[0027] As described above, the prediction method of this embodiment can obtain a snow melting model based on the predicted environmental information of the target building within a set time period, and accurately predict the snow melting situation of the target building within the set time period based on the snow melting model. Then, based on the snow melting situation, a stress analysis is performed on the snow on the target building, and the analysis results are used to determine whether the target building will experience snowfall within the set time period. Since the technical solution of this embodiment can accurately predict the snow melting situation of the target building, it can accurately determine whether the target building will experience snowfall within the set time period based on the snow melting situation, thereby improving the accuracy of predicting secondary disasters caused by snow and ice freeze-thaw cycles and achieving the goal of improving building safety.

[0028] In some embodiments of the present invention, such as Figure 2 As shown, after determining whether the target building has a risk of snowfall within a set time period based on the analysis results in step S103 above, the following steps are also included: Step S104: Obtain weather information after a set time period and determine whether the weather information meets the preset conditions for the formation of icicles on the target building; If so, proceed to step S105; Step S105: Predict the location and size of the icicles generated on the target building.

[0029] In step S104 above, the conditions for icicle formation can be preset and modified according to the environmental requirements for icicle formation; then, the environmental information after the set time period can be obtained based on the weather information after the set time period, and the target building can be judged based on the environmental information to determine whether it meets the conditions for icicle formation.

[0030] In this embodiment, the preset conditions for generating icicles include an ambient temperature below 0°C for a duration greater than a set duration, and the amount of water accumulated on the roof of the target building being greater than a set preset water volume threshold.

[0031] In step S105 above, the location of icicle formation on the target building and the size of the icicle formed at that location can be predicted based on the icicle formation mechanism and the structure of the target building.

[0032] In this embodiment, the location of water flowing on the roof of the target building, such as the drain outlet or the lowest point, can be obtained first, and this location can be used as the location for generating icicles. Then, an icicle generation model can be used to predict the size of the icicles.

[0033] The icicle generation model can be a neural network model based on generative adversarial networks. This icicle generation model treats icicles as growable objects and uses the width of the generated icicle location, water flow velocity, water accumulation, and ambient temperature as growth factors. Then, a training dataset is constructed using multiple preset growth factors and their corresponding icicle sizes, and this training dataset is used to train the neural network model to obtain the icicle growth model.

[0034] Then, the growth factor of the icicle generation location on the target building is input into the icicle growth model to obtain the size of the icicle generated at that location.

[0035] In this embodiment, after a set time period, it is also determined whether icicles will form on the target building. This can prevent the formation of icicles from reducing the safety of the building. Furthermore, if the preset conditions for the formation of icicles are met, the location and size of the icicles can be predicted, which helps staff to discover and clear the icicles from the target building, thereby further improving the safety of the target building.

[0036] In some embodiments of the present invention, the method for obtaining the current snow distribution of the target building in step S102 above is as follows: Figure 3 As shown, it includes the following steps: Step S111: Obtain the historical snow cover distribution of the target building at the end of the last snowfall, as well as the historical environmental information for each day after the last snowfall; Step S112: Perform thermal radiation analysis on the historical snow distribution of the target building based on the historical environmental information of each day since the last snowfall, so as to obtain the current snow distribution of the target building at the current moment.

[0037] In step S111 above, the snowfall amount and snowfall wind speed during the previous snowfall process can be obtained, and wind-induced snow drift analysis can be performed based on the snowfall amount and snowfall wind speed to obtain the historical snow distribution of the target building after the last snowfall.

[0038] This embodiment employs a snow distribution prediction model. Based on the snowfall amount and wind speed during the previous snowfall, wind-induced snow drift analysis is performed to obtain the historical snow distribution of the target building after the previous snowfall. Specifically, the snow distribution prediction model can be a neural network model based on a generative adversarial network. This model treats snow as a growable object and uses snowfall amount and wind speed as growth factors. Then, a training dataset constructed using multiple preset growth factors and their corresponding snow distributions is used to train the neural network model to obtain the snow distribution prediction model.

[0039] In this embodiment, a temperature sensor can be installed inside the target building to detect the indoor temperature information of the target building after the last snowfall, and historical weather information of the area where the target building is located can be obtained from a meteorological station.

[0040] In step S112 above, thermal radiation analysis can be performed on the historical snow distribution of the target building based on the daily indoor temperature and daily historical weather information to obtain the daily melting degree of snow on the roof of the target building, and thus obtain the current snow distribution on the roof of the target building.

[0041] In this embodiment, the current snow distribution of the target building is accurately predicted based on the historical snow distribution of the target building at the end of the last snowfall and the historical environmental information of each day after the last snowfall. Therefore, by performing thermal radiation analysis on the snow of the target building in a set time period based on the current snow distribution, the snow change of the target building in the set time period can be accurately obtained. Then, based on the snow change, it can be determined whether the snow on the target building will slide off, which can ensure the accuracy of the prediction of snow slide.

[0042] In some embodiments of the present invention, the method for performing thermal radiation analysis on the historical snow cover distribution of the target building in step S112 above includes: A snow melting model matching historical environmental information is obtained, and this snow melting model is used to perform thermal radiation analysis on historical snow distribution to obtain the current snow distribution of the target building.

[0043] In this embodiment, a snow melting model that matches historical environmental information is used to perform thermal radiation analysis on historical snow distribution in order to obtain the current snow distribution of the target building. This ensures the timeliness of obtaining the current snow distribution and thus improves the accuracy of predicting snow-ice-thaw cycles of disasters affecting the target building.

[0044] In some embodiments of the present invention, the method for obtaining a snow melting model corresponding to the historical environmental information of the target building is as follows: Figure 4 As shown, it includes the following steps: Step S121: Using a combined wind, snow and heat simulation test system, thermal radiation tests are conducted on the snow accumulation of the template building under various preset environments, and a snow melting model corresponding to each preset environment is constructed based on the test results. Step S122: Obtain the preset environment with the highest similarity to historical environmental information, and use the snow melting model corresponding to the preset environment as the snow melting model corresponding to the historical environmental information.

[0045] In this embodiment, the preset environment includes preset outdoor temperature, preset light intensity and preset indoor temperature. Furthermore, a preset wind, snow and heat full process joint simulation system can be used to conduct thermal radiation experiments under different weather conditions on various preset snow distributions to obtain experimental result data. A training dataset is constructed based on the experimental result data, and the preset neural network model is trained using the training dataset to obtain a snow melting model.

[0046] The aforementioned historical environmental information includes historical outdoor temperature, historical light intensity, and historical indoor temperature. Taking day i as an example, let the average outdoor temperature in the i-th hour of that day be... The average light intensity is The average indoor temperature is In one of the preset environments, the average preset outdoor temperature during the i-th hour is: The average preset light intensity is The average preset indoor temperature is Furthermore, the similarity s between the historical weather information and indoor temperature information of day i and the preset environment is... Where α is the outdoor temperature weight, β is the light intensity coefficient, and γ is the indoor temperature weight.

[0047] By conducting experiments on a combined wind, snow, and heat simulation test system, the thermal radiation test results of snow accumulation on target buildings under various preset environments are obtained. Then, a training dataset is obtained based on the test results, which ensures the accuracy of the training dataset. Furthermore, the snow melting model can be obtained using this training dataset, thereby improving the accuracy of the snow melting model.

[0048] In some embodiments of the present invention, the method for determining whether there is a risk of snowfall on the target building within a set time period based on the analysis results in step S103 is as follows: Figure 5 As shown, it includes the following steps: Step S131: Determine whether the target building has experienced snow layer sliding based on the stress analysis results of the snow accumulation on the target building; If so, proceed to step S132; Step S132: Obtain the impact force caused by the snow layer sliding, and determine whether the impact force is greater than the anti-slip threshold of the target building; If so, proceed to step S133; Step S133: It is determined that there is a risk of snow falling on the target building within a set time period.

[0049] Because buildings are designed to withstand snowfall and compromise their safety, protective railings are installed to enhance their resistance to snowfall. Therefore, in this embodiment, the anti-slip threshold of the target building can be obtained based on the structure and material of the protective railings.

[0050] In this embodiment, it is assumed that the tilt angle of the target building's roof is θ, the weight of the snow on the roof is G, and the coefficient of friction between the snow and the target building's roof is... The impact force F caused by the snow sliding off the target building is... If the impact force caused by the snow sliding off the target building is less than the target building's anti-slip threshold, it is determined that the target building will not experience snow sliding; conversely, if the impact force is greater than the target building's anti-slip threshold, it is determined that the target building will experience snow sliding.

[0051] In this embodiment, the determination of whether the target building will experience snow slippage is based on the impact force caused by the snow layer sliding on the target building and the anti-slip threshold of the target building, which can improve the accuracy of the determination.

[0052] In some embodiments of the present invention, the method for performing thermal radiation analysis on the current snow distribution of the target building using the snow distribution model in step S102 above includes: The set time period is divided into multiple melting cycles, and the snow accumulation of the target building is periodically acquired based on the predicted environmental information of each melting cycle.

[0053] In this embodiment, the duration of the aforementioned set time period is set to T, and the set time period is divided into m melting cycles. The duration of each melting cycle is T / m. The method for obtaining the predicted environmental information within each melting cycle and obtaining the snow distribution corresponding to the i-th melting cycle includes: First, obtain the snow distribution corresponding to the (i-1)th melting cycle, and use this snow distribution as the starting snow distribution for the ith melting cycle, and the starting snow distribution for the first melting cycle is the current snow distribution of the target building; Then, obtain the snow melting model corresponding to the predicted environmental information of the i-th melting cycle, and input the initial snow distribution of the i-th melting cycle into the snow melting model to obtain the snow distribution corresponding to the i-th melting cycle.

[0054] Since the weather can change significantly within a given time period, using a single snowmelt model to predict snowmelt during that period could lead to substantial errors in the prediction results. This embodiment divides the given time period into multiple melting cycles and periodically predicts the snow accumulation on the target building within each melting cycle based on the predicted environmental information. This prevents inaccurate predictions due to weather changes within the given time period, thereby improving the accuracy of snowfall predictions for the target building.

[0055] In some embodiments of the present invention, after determining whether there is a risk of snowfall on the target building within a set time period based on the analysis results in step S103, the method further includes: If the target building is at risk of snowfall within a set time period, the time of snowfall will be determined based on the changes in snow accumulation during each melting cycle.

[0056] In this embodiment, after obtaining the snow distribution corresponding to each melting cycle, it is predicted whether snow slippage will occur in each melting cycle based on the snow distribution. If snow slippage occurs in the i-th melting cycle and does not occur in the (i-1)-th melting cycle, it is determined that snow slippage occurred in the (i-1)-th melting cycle.

[0057] Since this embodiment can not only predict whether there is a risk of snow sliding off the target building, but also predict the time when snow sliding will occur, the accuracy of snow sliding prediction can be improved.

[0058] The flowcharts provided in this embodiment are not intended to indicate that the operations of the method will be performed in any particular order, or that all operations of the method are included in every case. Furthermore, the methods described above may include additional operations. Within the scope of the technical concept provided by the methods in this embodiment, additional variations can be made to the methods described above.

[0059] It should be understood that in some embodiments, the components may be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods may be implemented using software or firmware stored in memory and executed by a suitable instruction execution system.

[0060] This embodiment also provides a computer program product 10 and a computer-readable storage medium 20. Figure 6 This is a schematic diagram of a computer program product 10 according to an embodiment of the present invention. Figure 7 This is a schematic diagram of a computer-readable storage medium 20 according to an embodiment of the present invention. The computer program product 10 includes a computer program 11, which, when executed by a processor 32, implements the steps of any of the above-described methods for predicting secondary disasters caused by snow and ice freeze-thaw cycles. The computer-readable storage medium 20 stores the aforementioned computer program 11, which, when executed by the processor 32, implements the steps of a data table management method with a large number of object columns according to any of the above-described embodiments.

[0061] The computer program 11 used to perform the operations of this invention can be assembly instructions, Instruction Set Architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, status setting data, integrated circuit configuration data, or source code or object code written in any combination of one or more programming languages ​​and procedural programming languages. The computer program 11 can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the latter case, the remote computer can connect to the user's computer via any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or it can connect to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, to perform aspects of the invention, electronic circuits, including, for example, programmable logic circuits, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), can execute computer-readable program instructions to personalize the electronic circuits by utilizing state information of computer-readable program instructions.

[0062] For the purposes of this embodiment, computer program product 10 is a related product that includes computer program 11.

[0063] For the purposes of this embodiment, computer-readable storage medium 20 is a tangible device capable of holding and storing a computer program 11. It can be any device capable of containing, storing, communicating, propagating, or transmitting the program 11 for use by or in conjunction with an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable storage medium 20 include: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable optical disc read-only memory (CD-ROM), digital versatile disc (DVD), memory stick, floppy disk, mechanical encoding device, and any suitable combination thereof.

[0064] Therefore, those skilled in the art should recognize that although numerous exemplary embodiments of the present invention have been shown and described in detail herein, many other variations or modifications conforming to the principles of the present invention can be directly determined or derived from the disclosure of the present invention without departing from the spirit and scope of the invention. Thus, the scope of the present invention should be understood and construed as covering all such other variations or modifications.

Claims

1. A method for predicting a snow-ice freeze-thaw secondary disaster, characterized in that, include: The current snow cover distribution of the target building is obtained, and the predicted environmental information of the target building in the future within a set time period is predicted. The predicted environmental information includes the outdoor temperature, indoor temperature and light intensity of the target building in the set time period. A snow melting model matching the predicted environmental information is obtained, and the snow melting model is used to perform thermal radiation analysis on the current snow distribution in order to predict the snow change of the target building within the set time period. Based on the changes in snow accumulation, a stress analysis is performed on the snow accumulation on the target building, and based on the analysis results, it is determined whether the target building will experience snow slippage within the set time period.

2. The method for predicting snow and ice freeze-thaw cycles according to claim 1, characterized in that, After the step of determining whether the target building will experience snowfall within the set time period based on the analysis results, the method further includes: Obtain weather information after the set time period and determine whether the weather information meets the preset conditions for the formation of icicles on the target building; If so, the location where icicles will form on the target building will be predicted based on the weather information and the structure of the target building.

3. The method for predicting snow and ice freeze-thaw cycles according to claim 1, characterized in that, The step of obtaining the current snow distribution of the target building includes: Obtain the historical snow cover distribution of the target building at the end of the last snowfall, as well as the historical environmental information for each day since the last snowfall; Based on the historical environmental information, thermal radiation analysis is performed on the historical snow distribution to obtain the current snow distribution of the target building.

4. The method for predicting snow and ice freeze-thaw cycles according to claim 3, characterized in that, The step of performing thermal radiation analysis on the historical snow distribution based on the historical environmental information to obtain the current snow distribution of the target building includes: A snow melting model matching the historical environmental information is obtained, and the snow melting model is used to perform radiative heat analysis on the historical snow distribution to obtain the current snow distribution.

5. The method for predicting snow and ice freeze-thaw cycles according to claim 4, characterized in that, The step of obtaining a snow melt model that matches the historical environmental information includes: A combined wind, snow and heat simulation test system was used to conduct thermal radiation tests on the snow accumulation of the target building under various preset environments, and a snow melting model corresponding to each preset environment was constructed based on the test results. Obtain the preset environment with the highest similarity to the historical environmental information, and use the snow melting model corresponding to the preset environment as the snow melting model corresponding to the historical environmental information.

6. The method for predicting snow and ice freeze-thaw cycles according to claim 1, characterized in that, The step of determining whether the target building will experience snowfall within the set time period based on the analysis results includes: Based on the analysis results, determine whether the snow accumulation on the target building will slide. If so, the impact force caused by the snow sliding is obtained, and if the impact force is greater than the anti-slip threshold of the target building, it is determined whether the target building will experience snow sliding within the set time period.

7. The method for predicting snow and ice freeze-thaw cycles according to claim 1, characterized in that, The step of using the snow melting model to perform thermal radiation analysis on the current snow distribution to predict the snow cover changes of the target building within the set time period includes: The set time period is divided into multiple melting cycles, and the snow accumulation changes of the target building are periodically obtained based on the predicted environmental information of each melting cycle.

8. The method for predicting snow and ice freeze-thaw cycles according to claim 7, characterized in that, After the step of determining whether the target building will experience snowfall within the set time period based on the analysis results, the method further includes: If the target building is at risk of snowfall during the set time period, the time of snowfall is determined based on the snow accumulation changes in each melting cycle.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the method for predicting snow and ice freeze-thaw disasters as described in any one of claims 1-8.

10. A computer program product comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the method for predicting snow and ice freeze-thaw disasters as described in any one of claims 1-8.

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