Ice and snow disaster prediction method under wind and snow thermal coupling effect and related product

Abstract

The invention provides an ice and snow disaster prediction method under a wind and snow thermal coupling effect and a related product, and the method comprises the steps: carrying out the thermal radiation analysis of the historical accumulated snow distribution of a target building before snowfall according to the daily historical weather information and the indoor temperature information of the building after the previous snowfall; the current accumulated snow distribution of the roof of the target building is obtained, and on the basis of the current accumulated snow distribution, the predicted accumulated snow distribution of the target building in the next snowfall process is predicted by analyzing the wind-induced snow drifting of the next snowfall process; and determining whether the next snowfall causes ice and snow disasters to the target building according to the predicted accumulated snow distribution. According to the technical scheme of the invention, the accuracy of the predicted accumulated snow distribution can be ensured, and the accuracy of ice and snow disaster prediction is improved.

Description

Technical Field

[0001] This invention relates to the field of snow disaster prediction technology, and in particular to a method and related products for predicting snow and ice disasters under the combined effects of wind, snow and heat. Background Technology

[0002] Extreme snow and ice weather brings numerous negative impacts to society, economy, and environment in various regions, triggering secondary disasters such as building collapses, traffic jams, and communication and power outages. Therefore, in order to reduce the disasters caused by snow and ice weather, it is necessary to predict potential snow and ice disasters before snowfall and take timely safety measures before snow and ice disasters occur to reduce the severity of these disasters.

[0003] Current methods for predicting snow and ice disasters analyze wind-induced snow drift to determine the snow distribution on building roofs. This distribution pattern is then used to determine the pressure exerted on the building by the snow, and this pressure is used to assess whether the snowfall will cause snow and ice disasters. However, in regions with persistently low temperatures, unmelted snow from the previous snowfall often accumulates during the current snowfall. If existing methods for predicting snow and ice disasters are applied directly, this unmelted snow will affect the distribution of snow in the next snowfall, reducing the accuracy of snow and ice disaster predictions. Summary of the Invention

[0004] This invention provides a method and related products for predicting snow and ice disasters under the combined effects of wind, snow and heat, which are used to improve the accuracy of snow and ice disaster prediction, thereby improving the safety of buildings.

[0005] Specifically, in a first aspect, the present invention provides a method for predicting snow and ice disasters under the combined effects of wind, snow, and heat, including: Obtain weather forecast information for the area where the target building is located, and obtain the snowfall amount and snowfall wind speed for the next snowfall based on the weather forecast information; Detect whether there is snow on the roof of the target building. If so, obtain the historical snow distribution of the roof after the last snowfall, as well as the historical weather information for each day after the last snowfall and the indoor temperature information of the target building. Based on the historical weather information and the indoor temperature information, thermal radiation analysis is performed on the historical snow cover distribution to obtain the current snow cover distribution on the roof. Based on the current snow distribution, wind-induced snow drift analysis is performed on the next snowfall according to the snowfall amount and snowfall wind speed to obtain the predicted snow distribution of the target building in the next snowfall, and the predicted snow distribution is used to determine whether the next snowfall will cause ice and snow disasters to the target building.

[0006] Furthermore, the step of performing wind-induced snow drift analysis on the next snowfall based on the current snow cover distribution and the snowfall amount and wind speed includes: Obtain the predicted duration of the next snowfall and determine whether the predicted duration is greater than a preset duration threshold; If so, the falling snowfall is divided into multiple snowfall cycles according to the predicted duration, and the snowfall amount and snowfall wind speed of the snowfall cycle are used to perform periodic wind-induced snow drift analysis for the next snowfall, so as to obtain the snow distribution corresponding to each snowfall cycle. The predicted snow cover distribution corresponding to the last snowfall cycle is taken as the predicted snow cover distribution.

[0007] Furthermore, after the step of determining whether the target building will experience snow and ice disasters during the next snowfall based on the predicted snow distribution, the method further includes: If present, the timing of the next snowfall disaster is predicted based on the snow distribution corresponding to each snowfall cycle.

[0008] Further, the step of performing thermal radiation analysis on the historical snow distribution based on the historical weather information and the indoor temperature information to obtain the current snow distribution on the roof includes: Obtain a snow melting model corresponding to the historical weather information and indoor temperature information, and use the snow melting model to perform radiative heat analysis on the historical snow distribution to obtain the current snow distribution.

[0009] Furthermore, the step of obtaining the snow melting model corresponding to the historical weather information and indoor temperature information includes: A combined simulation test system for the entire process of wind, snow and heat was used to conduct thermal radiation tests on snow 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 weather information and the indoor temperature information, and use the snow melting model corresponding to the preset environment as the snow melting model corresponding to the historical weather information and the indoor temperature information.

[0010] Furthermore, after the step of detecting whether there is snow on the roof of the target building, the method further includes: If not, wind-induced snow drift analysis is performed on the next snowfall based on the snowfall amount and the snowfall wind speed to obtain the predicted snow distribution of the target building in the next snowfall.

[0011] Furthermore, the step of determining whether the target building will experience snow and ice disasters during the next snowfall based on the predicted snow distribution includes: Based on the predicted snow distribution, the pressure borne by each preset detection location on the target building during the next snowfall is obtained; A finite element simulation model of the target building is obtained, and the pressure is applied to the corresponding position in the finite element simulation model to perform stress analysis on the target building and determine whether it can withstand the pressure of snow accumulation.

[0012] Furthermore, after the step of determining whether the target building will experience snow and ice disasters during the next snowfall based on the predicted snow distribution, the method further includes: After the snowfall ends, the time interval between the current moment and the end of the snowfall is obtained, and the snow distribution of the target building is monitored based on the weather information within this interval to determine whether there are any safety hazards in the target building.

[0013] Secondly, the present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any of the above-described methods for predicting snow and ice disasters.

[0014] Thirdly, the present invention also provides a computer program product comprising a computer program that, when executed by a processor, implements the steps of any of the above-described methods for predicting snow and ice disasters.

[0015] The technical solution of this invention involves performing thermal radiation analysis on the historical snow distribution of a target building before snowfall, based on historical weather information and indoor temperature information of the building each day since the last snowfall. This analysis yields the current snow distribution on the roof of the target building. Based on this current snow distribution, wind-induced snow drift analysis is used to predict the predicted snow distribution of the target building during the next snowfall. This predicted snow distribution is then used to determine whether the next snowfall will cause ice and snow disasters to the target building. Because this invention accurately obtains the current snow distribution of the target building before snowfall through thermal radiation analysis of historical snow distribution, the accuracy of the predicted snow distribution for the next snowfall can be guaranteed, thereby improving the accuracy of ice and snow disaster prediction.

[0016] 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

[0017] The following sections will describe some specific embodiments of the invention in a detailed manner 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 snow and ice disaster prediction method under the action of wind and snow thermal coupling according to an embodiment of the present invention; Figure 2 This is a schematic flowchart of a method for predicting snow cover distribution of a target building in the next snowfall according to an embodiment of the present invention; Figure 3 This is a schematic flowchart of a method for determining whether a target building will be subject to ice and snow disasters in the next snowfall, according to an embodiment of the present invention; Figure 4 This is a schematic diagram of a computer program product according to an embodiment of the present invention; and Figure 5 This is a schematic diagram of a computer-readable storage medium according to an embodiment of the present invention. Detailed Implementation

[0018] The following reference Figures 1 to 5 This invention describes a method and related equipment for predicting snow and ice disasters under the combined effects of wind, snow, and heat, according to an embodiment 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. Therefore, a feature defined with "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 the present invention, "multiple" 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.

[0019] 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.

[0020] Please see Figure 1 , Figure 1This is a schematic flowchart of a method for predicting snow and ice disasters under the combined effects of wind, snow, and heat, according to an embodiment of the present invention. The method generally includes the following steps: Step S101: Obtain weather forecast information for the area where the target building is located, and obtain the snowfall amount and snowfall wind speed for the next snowfall based on the weather forecast information; Step S102: Detect whether there is snow on the roof of the target building; If so, proceed to step S103; Step S103: Obtain the historical snow accumulation distribution on the roof of the target building after the last snowfall, as well as the historical weather information for each day after the last snowfall and the indoor temperature information of the target building. Step S104: Perform thermal radiation analysis on the historical snow cover distribution of the target building based on the historical weather information and indoor temperature information of the building every day since the last snowfall, so as to obtain the current snow cover distribution of the target building. Step S105: Based on the current snow distribution of the target building, perform wind-induced snow drift analysis on the next snowfall according to the snowfall amount and snowfall wind speed to obtain the predicted snow distribution of the target building in the next snowfall. Step S106: Based on the predicted snow distribution of the target building in the next snowfall, determine whether the target building will be subject to ice and snow disasters during the next snowfall.

[0021] In step S101 above, weather forecast information can be obtained from the meteorological station in the area where the target building is located, and it can be determined whether snowfall will occur in the area in the time period after the current moment based on the weather forecast information; if not, no processing is required; if so, the snowfall amount and snowfall wind speed of the next snowfall can be obtained from the weather forecast information.

[0022] In step S102 above, a camera can be set up within a preset range around the target building to capture images of the roof of the target building to determine whether there is snow accumulation on the roof. Alternatively, a pressure sensor can be installed on the roof of the target building. If there is snow accumulation on the roof, the snow will exert pressure on the pressure sensor, and the presence of snow accumulation on the roof can be determined by the pressure signal detected by the pressure sensor.

[0023] In this embodiment, the snow distribution on the roof of the target building is acquired and stored after each snowfall. The snow distribution includes the snow thickness at multiple preset locations on the roof of the target building. In step S103, the snow distribution on the roof of the target building after the last snowfall can be read from the stored data; this snow distribution is the historical snow distribution of the target building's roof after the last snowfall. Then, historical weather information for each day after the last snowfall can be obtained from the meteorological station in the area where the target building is located. This historical weather information may include light intensity and outdoor temperature.

[0024] In step S104 above, since light intensity and outdoor temperature affect the melting rate of the surface layer of snow, while indoor temperature affects the melting rate of the bottom layer of snow, and light intensity, outdoor temperature, and indoor temperature are all positively correlated with the melting rate of snow, this embodiment can first fit the correspondence between light intensity, outdoor temperature, and indoor temperature and the melting rate of snow, and then use this correspondence to perform thermal radiation analysis on the historical snow distribution of the target building based on the daily indoor temperature and historical weather information of the target building, so as to obtain the current snow distribution on the roof of the target building.

[0025] In step S105 above, the method for wind-induced snow drift in the next snowfall includes: using a snow distribution prediction model, performing wind-induced snow drift analysis based on the snowfall amount and snowfall wind speed during the next snowfall process, in order to predict the historical snow distribution of the target building after the next snowfall ends.

[0026] The snow distribution prediction model in this embodiment can be a neural network model based on generative adversarial networks. 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.

[0027] In this embodiment, the snow distribution prediction model treats snow as a growable object and uses snowfall amount and snowfall wind speed as growth factors. It then uses a training dataset constructed from multiple preset growth factors and their corresponding snow distributions to train the neural network model, resulting in a snow distribution prediction model corresponding to the snowfall amount and snowfall wind speed of the next snowfall. The current snow distribution is then input into this snow distribution prediction model to perform wind-induced snow drift analysis based on the current snow distribution, obtaining the predicted snow distribution for the target building in the next snowfall.

[0028] In step S106 above, the pressure exerted by the snow accumulation on the roof of the target building after the next snowfall can be calculated based on the predicted snow distribution of the target building during the next snowfall, and it can be determined whether the pressure exceeds the tolerance range of the target building; if so, it is determined that the target building is subject to ice and snow disasters during the next snowfall.

[0029] For example, the pressure borne by each reference point can be calculated based on the snow thickness at multiple designated reference locations on the roof of the target building. Then, it can be determined whether the pressure borne by each reference point is greater than its corresponding pressure threshold. If the number of reference points with pressures greater than their corresponding pressure thresholds is greater than a set number, it is determined that the pressure exerted by the snow on the target building after the next snowfall exceeds its tolerable range, and the target building will suffer from snow and ice disasters in the next snowfall.

[0030] As described above, the snow disaster prediction method of this embodiment performs thermal radiation analysis on the historical snow distribution of the target building before snowfall, based on historical weather information and indoor temperature information of the building each day since the last snowfall, to obtain the current snow distribution on the roof of the target building. Based on this current snow distribution, it predicts the snow distribution of the target building during the next snowfall by analyzing wind-induced snow drift. Based on this predicted snow distribution, it determines whether the next snowfall will cause snow disasters to the target building. Because the technical solution of this embodiment accurately obtains the current snow distribution of the target building before snowfall through thermal radiation analysis of historical snow distribution, obtaining the predicted snow distribution for the next snowfall based on this current snow distribution ensures the accuracy of the predicted snow distribution, thereby improving the accuracy of snow disaster prediction.

[0031] In some embodiments of the present invention, the method for predicting the snow cover distribution of the target building in the next snowfall in step S105 is as follows: Figure 2 As shown, it includes the following steps: Step S111: Obtain the predicted duration of the next snowfall and determine whether the predicted duration is greater than the preset duration threshold. If so, proceed to step S112; Step S112: Divide the next snowfall into multiple snowfall cycles according to the predicted duration of the next snowfall, and perform periodic wind-induced snow drift analysis on the next snowfall based on the snowfall amount and snowfall wind speed of each snowfall cycle, so as to obtain the snow distribution corresponding to each snowfall cycle. Step S113: Use the predicted snowfall distribution corresponding to the last snowfall cycle as the predicted snowfall distribution of the target building in the next snowfall.

[0032] In step S111 above, assuming the predicted duration of the next snowfall is T, the preset duration threshold is... ,if Then proceed to step S112.

[0033] In S112, assuming the next snowfall is divided into m snowfall cycles, the duration of each snowfall cycle is T / m, and the snowfall amount and snowfall wind speed in each snowfall cycle are obtained based on weather forecast information. The current snow cover distribution of the target building is then used as the initial snow cover distribution for the first snowfall cycle. Wind-induced snow drift analysis is performed on the snowfall within the first snowfall cycle to obtain the snow cover distribution of the target building at the end of the first snowfall cycle. This snow cover distribution corresponds to the snow cover distribution for the first snowfall cycle. The snow cover distribution corresponding to the first snowfall cycle is then used as the initial snow cover distribution for the second snowfall cycle. Wind-induced snow drift analysis is performed on the snowfall within the second snowfall cycle to obtain the snow cover distribution of the target building at the end of the second snowfall cycle. This snow cover distribution corresponds to the snow cover distribution for the second snowfall cycle. This process continues, with the snow cover distribution corresponding to each snowfall cycle serving as the initial snow cover distribution for the next snowfall cycle. Based on the initial snow cover distribution for each snowfall cycle, the snow cover distribution for each snowfall cycle is predicted according to the snowfall amount and wind speed within each cycle. After obtaining the snow cover distribution for the last snowfall cycle, this snow cover distribution is used as the predicted snow cover distribution for the next snowfall cycle.

[0034] Since the amount of snowfall and the wind speed may vary at different times during a long period of snowfall, in this embodiment, when the predicted duration of the next snowfall is long, the next snowfall is divided into multiple snowfall cycles, and wind-induced snow drift analysis is performed on each snowfall cycle to obtain the predicted snow cover distribution for the next snowfall, which can improve the accuracy of the predicted snow cover distribution.

[0035] In one embodiment of the present invention, after determining whether the target building will experience ice and snow disasters in the next snowfall based on the predicted snow distribution of the target building in the next snowfall in step S106, the method further includes: If the target building is likely to experience snow and ice disasters in the next snowfall, the time when the snow and ice disasters will occur in the next snowfall will be determined based on the snow distribution corresponding to each snowfall cycle.

[0036] In this embodiment, after obtaining the snow distribution corresponding to each snowfall cycle, it is predicted whether there will be ice and snow disasters in each snowfall cycle based on the snow distribution. If there are ice and snow disasters in the i-th snowfall cycle, but not in the (i-1)-th snowfall cycle, then the time period corresponding to the i-th snowfall cycle is the time period in which ice and snow disasters occur.

[0037] In this embodiment, the timing of the next snowfall and snow disaster is predicted based on the snow distribution corresponding to each snowfall cycle, which can improve the accuracy of snow disaster prediction and enable staff to take timely safety measures.

[0038] In some embodiments of the present invention, the method for performing thermal radiation analysis on the historical snow cover distribution of the target building based on historical weather information and indoor temperature information of the building on each day since the last snowfall includes: Obtain a snow melting model corresponding to historical weather information and indoor temperature information of the target building, and use the snow melting model to perform radiative heat analysis on the historical snow distribution of the target building to obtain the current snow distribution of the target building.

[0039] The snow melting model in this embodiment can be a neural network model based on generative adversarial networks. This neural network model treats the snow on the target building as a growing object, and environmental information as the growth factor of this object. It can obtain the snow growth result under the influence of the growth factor, that is, predict the change of snow accumulation. Therefore, a snow melting model that matches the historical weather information and indoor temperature information of the target building every day after the last snowfall can be obtained first. The historical snow distribution of the target building is then input into the snow melting model, thereby obtaining the current snow distribution of the target building through the snow melting model.

[0040] Taking the i-th day after the last snowfall as an example, firstly, the initial snow cover distribution of the i-th day is obtained. This initial snow cover distribution is the historical snow cover distribution of the (i-1)-th day, and the initial snow cover distribution of the 1st day is the historical snow cover distribution after the last snowfall. Then, the historical weather information of the i-th day, the indoor temperature information of the target building, and the initial snow cover distribution are input into the preset snow melting model to obtain the historical snow cover distribution of the i-th day.

[0041] In this embodiment, a snow melting model corresponding to historical weather information and indoor temperature information of the target building is first obtained. Then, the snow melting model is used to obtain the current snow distribution of the target building based on the historical snow distribution of the target building. This can improve the convenience of obtaining the current snow distribution and thus improve the efficiency of predicting snow and ice disasters.

[0042] In some embodiments of the present invention, the method for obtaining a preset snow melting model includes: A combined simulation test system for the entire process of wind, snow and heat 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 historical weather information and indoor temperature information, and use the snow melting model corresponding to the preset environment as the snow melting model corresponding to historical weather information and indoor temperature information of the target building.

[0043] In this embodiment, a pre-defined joint simulation system for the entire process of wind, snow and heat can be used to conduct thermal radiation experiments on various pre-defined snow distributions under different weather conditions. A training dataset can be constructed based on the experimental results data, and then the pre-defined neural network model can be trained using the training dataset to obtain a snow melting model.

[0044] Historical weather information includes historical outdoor temperature and historical light intensity. Taking day i as an example, let the average outdoor temperature of that day in the i-th hour be... The average light intensity is The average indoor temperature is In one of the preset environments, the average outdoor temperature during the i-th hour is... The average light intensity is The average 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.

[0045] 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.

[0046] In some embodiments of the present invention, after determining whether there is snow on the roof of the target building in step S102, the method further includes: If not, wind-induced snow drift analysis is performed on the next snowfall based on the snowfall amount and wind speed to obtain the predicted snow distribution of the target building in the next snowfall.

[0047] In this embodiment, when there is no snow accumulation on the roof of the target building, wind-induced snow drift analysis is performed directly on the next snowfall, which can quickly predict the snow distribution of the target building during the next snowfall.

[0048] In some embodiments of the present invention, the method for determining whether the target building will experience ice and snow disasters in the next snowfall based on the predicted snow distribution of the target building during the next snowfall in step S106 is as follows: Figure 3 As shown, it includes the following steps: Step S121: Based on the predicted snow distribution of the target building, obtain the pressure borne by multiple preset detection locations on the target building during the next snowfall. Step S122: Obtain the finite element simulation model of the target building, and apply the pressure borne by each preset detection position to the corresponding position in the finite element simulation model; Step S123: Perform stress analysis on the finite element simulation model of the target building to determine whether the target building can withstand the snow pressure of the next snowfall.

[0049] In step S121 above, the weight of snow at each preset location can be calculated based on the snow height at multiple preset locations on the roof of the target building, and the pressure borne at each preset location can be obtained based on the weight.

[0050] In step S122 above, the size information, material information and structural information of the target building can be obtained, and a finite element simulation model of the target building can be constructed in the finite element simulation software based on the size information, material information and structural information. Then, pressure is applied to the position corresponding to the preset position in the finite element simulation model.

[0051] In step S123 above, it can be determined by finite element simulation model whether the snow accumulation will cause damage to the target building; if so, it is determined that the target building cannot withstand the snow pressure of the next snowfall.

[0052] In this embodiment, by performing stress analysis on the finite element simulation model of the target building, the stress situation of the target building during the next snowfall can be accurately simulated, thereby accurately determining whether the snow accumulation during the next snowfall will cause ice and snow disasters to the target building.

[0053] In some embodiments of the present invention, the method for predicting snow and ice disasters further includes: After the snowfall ends, the time interval between the day and the snowfall is obtained, and the snow distribution of the target building is monitored based on the weather information within that time interval.

[0054] Taking the current day as i after snowfall as an example, this embodiment can use a snow melting model to perform thermal radiation analysis on the snow distribution on day i-1 based on the weather information and indoor temperature of the day, so as to obtain the snow distribution of the target building after day i ends. This snow distribution is the snow distribution on day i. The snow distribution of the target building on day 1 is the snow distribution of the target building after the snowfall ends, thereby realizing the monitoring of snow accumulation on the target building.

[0055] This embodiment continuously monitors the snow distribution of the target building after the snowfall ends to obtain the changes in the snow distribution of the target building, thereby improving the safety of the target building.

[0056] 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.

[0057] 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.

[0058] This embodiment also provides a computer program product 10 and a computer-readable storage medium 20. Figure 4 This is a schematic diagram of a computer program product 10 according to an embodiment of the present invention. Figure 5 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 the snow and ice disaster prediction method under the wind-snow thermal coupling effect described above. The computer-readable storage medium 20 stores the aforementioned computer program 11, which, when executed by the processor 32, implements the steps of the snow and ice disaster prediction method under the wind-snow thermal coupling effect described above.

[0059] 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.

[0060] For the purposes of this embodiment, computer program product 10 is a related product that includes computer program 11.

[0061] 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.

[0062] 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 snow and ice disasters under the combined effects of wind, snow, and heat, characterized in that, include: Obtain weather forecast information for the area where the target building is located, and obtain the snowfall amount and snowfall wind speed for the next snowfall based on the weather forecast information; Detect whether there is snow on the roof of the target building. If so, obtain the historical snow distribution of the roof after the last snowfall, as well as the historical weather information for each day after the last snowfall and the indoor temperature information of the target building. Based on the historical weather information and the indoor temperature information, thermal radiation analysis is performed on the historical snow cover distribution to obtain the current snow cover distribution on the roof. Based on the current snow distribution, wind-induced snow drift analysis is performed on the next snowfall according to the snowfall amount and snowfall wind speed to obtain the predicted snow distribution of the target building in the next snowfall, and the predicted snow distribution is used to determine whether the next snowfall will cause ice and snow disasters to the target building.

2. The method for predicting snow and ice disasters according to claim 1, characterized in that, The step of performing wind-induced snow drift analysis on the next snowfall based on the current snow distribution and the snowfall amount and wind speed to obtain the predicted snow distribution of the target building in the next snowfall includes: Obtain the predicted duration of the next snowfall and determine whether the predicted duration is greater than a preset duration threshold; If so, the falling snowfall is divided into multiple snowfall cycles according to the predicted duration, and the snowfall amount and snowfall wind speed of the snowfall cycle are used to perform periodic wind-induced snow drift analysis for the next snowfall, so as to obtain the snow distribution corresponding to each snowfall cycle. The predicted snow cover distribution corresponding to the last snowfall cycle is taken as the predicted snow cover distribution.

3. The method for predicting snow and ice disasters according to claim 2, characterized in that, After the step of determining whether the target building will experience snow and ice disasters during the next snowfall based on the predicted snow distribution, the method further includes: If present, the timing of the next snowfall disaster is predicted based on the snow distribution corresponding to each snowfall cycle.

4. The method for predicting snow and ice disasters according to claim 1, characterized in that, The step of performing thermal radiation analysis on the historical snow cover distribution based on the historical weather information and the indoor temperature information to obtain the current snow cover distribution on the roof includes: Obtain a snow melting model corresponding to the historical weather information and indoor temperature information, and use the snow melting model to perform radiative heat analysis on the historical snow distribution to obtain the current snow distribution.

5. The method for predicting snow and ice disasters according to claim 4, characterized in that, The step of obtaining the snow melting model corresponding to the historical weather information and indoor temperature 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 weather information and the indoor temperature information, and use the snow melting model corresponding to the preset environment as the snow melting model corresponding to the historical weather information and the indoor temperature information.

6. The method for predicting snow and ice disasters according to claim 1, characterized in that, Following the step of detecting whether there is snow on the roof of the target building, the method further includes: If not, wind-induced snow drift analysis is performed on the next snowfall based on the snowfall amount and the snowfall wind speed to obtain the predicted snow distribution of the target building in the next snowfall.

7. The method for predicting snow and ice disasters according to claim 1, characterized in that, The step of determining whether the target building will suffer from snow and ice disasters during the next snowfall based on the predicted snow distribution includes: Based on the predicted snow distribution, the pressure borne by each preset detection location on the target building during the next snowfall is obtained; A finite element simulation model of the target building is obtained, and the pressure is applied to the corresponding position in the finite element simulation model to perform stress analysis on the target building and determine whether it can withstand the pressure of snow accumulation.

8. The method for predicting snow and ice disasters according to claim 1, characterized in that, After the step of determining whether the target building will experience snow and ice disasters during the next snowfall based on the predicted snow distribution, the method further includes: After the snowfall ends, the time interval between the current moment and the end of the snowfall is obtained, and the snow distribution of the target building is monitored based on the weather information within this interval.

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 snow and ice disaster prediction method according to 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 snow and ice disaster prediction method according to any one of claims 1-8.

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