A random simulation method and system for daily ground snow load sequence based on Poisson process

Abstract

A Poisson process-based random simulation method and system for daily ground snow load series belongs to the field of civil engineering technology. It solves the problem that the duration of each snowfall cannot be obtained from the measurement results, and therefore the duration of the event cannot be modeled in the simulation. Based on the actual evolution of the snow load, this method abandons the concept of event duration on the basis of the above-mentioned Poisson process model, introduces snow attenuation, and constructs the time series of the ground snow load based on the occurrence time of the snowfall event, the snowfall intensity and the snow attenuation, thereby completing the simulation of the daily ground snow load time series. The present invention is suitable for application scenarios such as building risk assessment in snowy areas and weather station data analysis.

Description

Technical Field

[0001] The invention belongs to the technical field of civil engineering, and in particular relates to a random simulation method for daily ground snow load sequences. Background Art

[0002] Snowfall is an important form of precipitation and a significant source of fresh water. However, it is a natural disaster for buildings, potentially damaging their structures, causing economic losses, and threatening public safety.

[0003] It can be seen that the design of building structures in snowy areas, especially the design of large-span, lightweight roof structures such as conference and exhibition centers and stadiums, must pay sufficient attention to snow loads.

[0004] The Code for Loads on Building Structures points out in its clause explanation that for large-span, lightweight roof structures, snow load is "often the controlling load". Therefore, the correct value of snow load is particularly important for such structures.

[0005] The ultimate limit state design of a structure requires the use of combinations of loads. Currently, the common practice for load combinations is to assume that a particular load reaches its ultimate limit state value, and the other loads in the combination adopt their point-in-time values, known as the Turkstra criterion. The Code for Loads on Building Structures recommends a combination coefficient of 0.7 for snow loads, but its explanatory note states, "This code does not yet specify the method for determining the combination coefficient; it is primarily determined conservatively within the scope of engineering design experience." The Code for Seismic Design of Buildings recommends a snow load combination coefficient of 0.5 when calculating representative gravity loads for buildings, but "the combination coefficient essentially follows the value of the 1978 code," indicating that no reliability analysis has been performed. Therefore, future revisions to the relevant codes may involve the determination of load combination coefficients based on reliability analysis. This may require time series simulations of variable loads, including snow loads, to determine the probability of exceedance of the load combination effects within the reference period.

[0006] The methods for simulating the time series of ground snow load can be divided into two types: the model based on the Bernoulli process and the model based on the Poisson process. Figure 1) divides the entire snow season into several equal periods. The probability of a non-zero snow load in each period is p, and the number of snowfall events (i.e., the number of periods with non-zero snow load) follows a binomial distribution. This model assumes that the snow loads between periods are independent and identically distributed, while the snow loads within the same period remain constant. When applying the Bernoulli model, determining the time period division is a particularly challenging issue. If the time period is too short, such as one day, then considering that snowfall can sometimes last for multiple days, the snow loads in different time periods are unlikely to be independent of each other, as they may be caused by the same snowfall. If the time period is too long, such as one or two weeks, the Bernoulli model assumes that the snow load is constant within the same time period, so the model shows that there will be snow accumulation every day and the amount of snow is consistent. However, in areas with less snowfall, there may only be one or two days of snow accumulation in a given time period, which is significantly different from the model and causes large errors. In snowy areas, the independence of the snow loads between time periods cannot be met. Because snow loads in snowy areas are often the result of continuous snow accumulation, the snow loads in later time periods are affected by the snow loads in earlier time periods and cannot be considered independent. Therefore, the application of the Bernoulli model is significantly limited.

[0007] In contrast, the Poisson model (see Figure 2 ) can better reflect the physical process of snow load accumulation. The core of this model is to assume that the occurrence of snowfall events is a Poisson process (that is, the time interval between events follows an exponential distribution), and the snowfall intensity of each event conforms to the independent and identically distributed assumption. After a snowfall event, the amount of snow accumulation will remain for a period of time, during which the ground snow load remains unchanged. The duration of different snowfall events can overlap. With the occurrence time, snowfall intensity, and duration of snowfall events, these events can be superimposed to form a time series of snow load.

[0008] Existing methods for simulating ground snow loads based on the Poisson process involve the concept of snow event duration. This concept is well understood in a physical sense, as the length of time the snow accumulated from each snowfall remains on the ground. However, it is difficult to identify during data processing. This is because the snow pressure measured by meteorological stations is the cumulative snow pressure on the ground, resulting from several previous snowfalls. However, surveyors do not observe the number of snow layers in the snowpack (one layer per snowfall) and their corresponding ages. Therefore, the duration of each snowfall cannot be obtained from the measurement results, making it impossible to model the event duration in simulations. Existing simulation methods primarily use iterative neural network algorithms to approximate this concept, making it a purely mathematical approximation problem that loses the physical meaning of duration. Summary of the Invention

[0009] The present invention proposes a random simulation method and system for daily ground snow load sequences based on a Poisson process, with the aim of solving the problem that the duration of each snowfall cannot be obtained from the measurement results, and therefore the duration of the event cannot be modeled in the simulation.

[0010] The present invention proposes a random simulation method for daily ground snow load sequence based on Poisson process, which includes:

[0011] S1: Collect historical snow cover data, obtain historical snow cover variables, and determine the probability distribution model of each variable;

[0012] S2: Simulate a daily snow depth sequence within a snow year, including:

[0013] S201: randomly obtain simulated snowfall information for the current year based on the probability distribution model of each variable, wherein the snowfall information includes: the snow accumulation start date SD, the snowfall period duration T, the snowfall occurrence rate v, and the snowfall interval L.

[0014] S202: Setting the conditions for the snow accumulation start date SD, the snowfall period duration T, and the snowfall occurrence rate v of the current year;

[0015] S203: Set the snowfall counter i=1, and randomly generate the snowfall intensity A for the i-th snowfall i and snowfall interval L i , and then get the occurrence time t of the i+1th snow i+1 =t i +L i ;

[0016] S204: Set the snowfall counter i=i+1, and repeat S203 until t i >t end , and generate the snowfall intensity A of the last snow of the year end ;

[0017] S205: Initialize the snow depth of each day to 0, set the snowfall counter i = 1, the date counter j = t1, and set the snow depth of the jth day h j =A1;

[0018] S206: let j=j+1;

[0019] S207: If j <t i+1 , randomly generate the snow attenuation d for the day j , then the snow depth h on day j j =h j-1 -d j , when h j <0, take h j =0;

[0020] S208: If j = t i+1 , then let h j =h j-1 +A i+1 , and let i=i+1;

[0021] S209: Repeat S206 to S208 until t i =t end ;

[0022] S210: Repeat S206 to S207 until h j =0;

[0023] S3: Obtain daily snow depth data and complete the simulation of daily snow depth sequence within a snow accumulation year.

[0024] Furthermore, a preferred solution is provided: the snow accumulation variables over the years include: the snow accumulation start date over the years, the snowfall period duration over the years, the snowfall occurrence rate over the years, the snowfall intensity over the years, the snowfall interval over the years and the snow attenuation over the years.

[0025] Furthermore, a preferred solution is provided: the snow attenuation is the difference between the accumulated snow on the ground on the current day and the accumulated snow on the previous day.

[0026] Furthermore, a preferred solution is provided: in the above S202, the snow accumulation start date SD is set to satisfy the condition of 1≤SD≤212, and the time of occurrence of the first snowfall in the year is set to t1=SD.

[0027] Furthermore, a preferred solution is provided: in the above S202, the snowfall period T of the year is set to satisfy the condition SD+T≤212, and the time of the last snowfall of the year is set to t end =SD+T.

[0028] Furthermore, a preferred solution is provided: in S202, the snowfall interval L is subject to the parameter exponential distribution.

[0029] Furthermore, a preferred solution is provided: S3 further includes:

[0030] S301: extracting the maximum simulated snow depth of the year from the simulated daily snow depth data;

[0031] S302: randomly generate an annual maximum snow depth according to the local annual maximum snow depth probability distribution model;

[0032] S303: Multiply the simulated daily snow depth data by a coefficient α, where α is the ratio of the maximum snow depth of the year to the maximum simulated snow depth of the year.

[0033] The present invention also proposes a computer device, which includes a memory and a processor, wherein a computer program is stored in the memory. When the processor runs the computer program stored in the memory, the processor executes a random simulation method of daily ground snow load sequences based on a Poisson process according to any combination of the above-mentioned schemes.

[0034] The present invention also proposes a computer-readable storage medium, which is used to store a computer program, and the computer program executes the steps of a random simulation method of daily ground snow load sequence based on a Poisson process as described in any combination of the above-mentioned schemes.

[0035] The present invention further proposes a random simulation system for daily ground snow load sequences based on a Poisson process. The system is implemented based on a random simulation method for daily ground snow load sequences based on a Poisson process described in any combination of the above solutions. The system comprises:

[0036] Data processing module: used to collect snow cover data over the years, obtain snow cover variables over the years, and determine the probability distribution model of each variable;

[0037] Simulation control module: used to simulate the daily snow depth sequence within a snow year. The simulation process includes:

[0038] S201: randomly obtain simulated snowfall information for the current year based on the probability distribution model of each variable, wherein the snowfall information includes: the snow accumulation start date SD, the snowfall period duration T, the snowfall occurrence rate v, and the snowfall interval L.

[0039] S202: Setting the conditions for the snow accumulation start date SD, the snowfall period duration T, and the snowfall occurrence rate v of the current year;

[0040] S203: Set the snowfall counter i=1, and randomly generate the snowfall intensity A for the i-th snowfall i and snowfall interval L i , and then get the occurrence time t of the i+1th snow i+1 =t i +L i ;

[0041] S204: Set the snowfall counter i=i+1, and repeat S203 until t i >t end , and generate the snowfall intensity A of the last snow of the year emd ;

[0042] S205: Initialize the snow depth of each day to 0, set the snowfall counter i = 1, the date counter j = t1, and set the snow depth of the jth day hj =A1;

[0043] S206: let j=j+1;

[0044] S207: If j <t i+1 , randomly generate the snow attenuation d for the day j , then the snow depth h on day j j =h j-1 -d j , when h j <0, take h j =0;

[0045] S208: If j = t i+1 , then let h j =h j-1 +A i+1 , and let i=i+1;

[0046] S209: Repeat S206 to S208 until t i =t end ;

[0047] S210: Repeat S206 to S207 until h j =0;

[0048] Data output module: used to obtain daily snow depth data and complete the simulation of daily snow depth sequence within a snow accumulation year.

[0049] Based on the actual evolution of snow load, this method, based on the aforementioned Poisson process model, abandons the concept of event duration and introduces snow attenuation. This method constructs a time series of ground snow load based on the occurrence time of snowfall events, snowfall intensity, and snow attenuation, thereby simulating a daily ground snow load time series. The snow load time series can be either a daily snow depth series or a daily snow pressure series. This method uses the snow depth series as an example, but the snow pressure series can be used as a reference.

[0050] The method described in this paper can simulate daily snow depth series for both intermittent snowfall regions (where snow does not accumulate) and cumulative snowfall regions (where snow does accumulate). Furthermore, by forcing the annual maximum values ​​in the simulated results to follow a probability distribution model that mirrors the actual annual maximum values, the statistical characteristics of the simulated results are guaranteed to be consistent with actual observations.

[0051] Compared with the original simulation method, the physical meaning of each variable in the present invention is clearer (the original method assumes that the snow accumulation after each snowfall lasts for many days without a decay process, such as Figure 2This method takes into account the decay of snow accumulation, and the simulation process is more consistent with the physical process of snow accumulation. Furthermore, while existing simulation methods involve the use of network neural algorithms (which are black-box operations and have unclear concepts), this invention does not involve this, relying solely on traditional methods for generating random numbers. As a result, the technology is simpler and the concepts are clearer.

[0052] The present invention is suitable for application scenarios such as building risk assessment in snowy areas and weather station data analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the embodiments or descriptions of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0054] Figure 1 This is a schematic diagram of a model based on the Bernoulli process described in the background technology of the present invention;

[0055] Figure 2 This is a schematic diagram of a model based on a Poisson process described in the background technology of the present invention;

[0056] Figure 3 This is a schematic diagram of model parameters described in the first embodiment of the present invention;

[0057] Figure 4 This is a flowchart of daily snow depth simulation for a single snow year according to the first embodiment of the present invention;

[0058] Figure 5 A comparison chart of the simulation results (bottom) and the observed values ​​(top) of station A described in the second embodiment of the present invention;

[0059] Figure 6 A comparison chart of some simulation results (bottom) and observation values ​​(top) of Station B according to the second embodiment of the present invention;

[0060] Figure 7 This is a comparison chart of the simulation results (bottom) and the observation values ​​(top) of the C station described in the second embodiment of the present invention;

[0061] Figure 8 A comparison chart of the simulation results (bottom) and the observation values ​​(top) of the D station described in the second embodiment of the present invention;

[0062] Figure 9 This is a comparison chart of the probability distribution of the simulated and measured annual maximum snow water equivalent values ​​at the four representative stations described in the second embodiment of the present invention;

[0063] Figure 10 This is a comparison diagram of the 50-year maximum snow pressure based on simulation values ​​and measured values ​​as described in the second specific embodiment of the present invention. DETAILED DESCRIPTION

[0064] In the following description, specific details such as specific system structures and technologies are provided for purposes of illustration rather than limitation to facilitate a thorough understanding of the embodiments of the present application. However, it should be apparent to those skilled in the art that the present application may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid obscuring the description of the present application with unnecessary detail.

[0065] It will be understood that when used in this specification and the appended claims, the term "comprising" indicates the presence of described features, integers, steps, operations, elements and / or components, but does not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or groups thereof.

[0066] It should also be understood that the terms used in this specification are only for the purpose of describing specific embodiments and are not intended to limit the present application. As used in this specification and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms unless the context clearly indicates otherwise.

[0067] The following is a clear and complete description of the technical solutions in the embodiments of this application in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments of this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0068] In the following description, many specific details are set forth to facilitate a full understanding of the present application. However, the present application may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present application. Therefore, the present application is not limited to the specific implementation methods disclosed below.

[0069] Implementation method one:

[0070] Reference Figure 3 、 Figure 4 This embodiment will be described.

[0071] Before simulation, this implementation method needs to analyze the snow data provided by local meteorological stations over the years, and extract the snow start date SD, snowfall period duration T, snowfall intensity A, snow attenuation d, snowfall occurrence rate v, and annual maximum snow depth h from the daily snow depth data over the years. max The sample values ​​are then analyzed for probability statistics to determine the probability distribution model of each variable.

[0072] After the probability distribution model of each variable is determined, a simulation of the daily snow depth sequence within a snow accumulation year is carried out, specifically:

[0073] A random simulation method for daily ground snow load sequence based on a Poisson process, the method comprising:

[0074] S1: Collect historical snow cover data, obtain historical snow cover variables, and determine the probability distribution model of each variable; the historical snow cover variables include: the snow cover start date, the snowfall period duration, the snowfall incidence rate, the snowfall intensity, the snowfall interval, and the snow cover decay; the snow cover decay is the difference between the accumulated snow cover on the ground on the current day and the accumulated snow cover on the previous day;

[0075] S2: Simulate a daily snow depth sequence within a snow year, including:

[0076] S201: randomly obtain simulated snowfall information for the current year based on the probability distribution model of each variable, wherein the snowfall information includes: the snow accumulation start date SD, the snowfall period duration T, the snowfall occurrence rate v, and the snowfall interval L.

[0077] S202: Set the conditions for the snow accumulation start date SD, the snowfall duration T, and the snowfall occurrence rate v of the year, that is, set the snow accumulation start date SD to 1≤SD≤212, and set the occurrence time of the first snowfall of the year t1=SD; set the snowfall duration T to SD+T≤212, and set the occurrence time of the last snowfall of the year t end =SD+T; the snowfall interval L is subject to the parameter exponential distribution of

[0078] S203: Set the snowfall counter i=1, and randomly generate the snowfall intensity A for the i-th snowfall i (That is, the new snow depth on that day is A i ) and snowfall interval L i , and then get the occurrence time t of the i+1th snow i+1 =t i +L i ;

[0079] S204: Set the snowfall counter i=i+1, and repeat S203 until ti >t end , and generate the snowfall intensity A of the last snow of the year end ;

[0080] S205: Initialize the snow depth of each day to 0, set the snowfall counter i = 1, the date counter j = t1, and set the snow depth of the jth day h j =A1;

[0081] S206: Let j = j + 1:

[0082] S207: If j <t i+1 , randomly generate the snow attenuation d on the day according to the probability distribution model of snow attenuation d j , then the snow depth h on day j j =h j-1 -d j , when h j <0, take h j =0;

[0083] S208: If j = t i+1 (i.e. the day of the i+1 snowfall), let h j =h j-1 +A i+1 , and let i=i+1;

[0084] S209: Repeat S206 to S208 until t i =t end ;

[0085] S210: Repeat S206 to S207 until h j =0;

[0086] S3: Obtain daily snow depth data and complete the simulation of daily snow depth sequence within a snow accumulation year

[0087] S301: Extract the maximum simulated snow depth h of the year from the simulated daily snow depth data max,s ;

[0088] S302: According to the local annual maximum snow depth h max The probability distribution model randomly generates an annual maximum snow depth h max,r ;

[0089] S303: Multiply the simulated daily snow depth data by a coefficient α, where α is the ratio of the maximum snow depth of the year to the maximum simulated snow depth of the year, i.e.

[0090] Specifically:

[0091] The snow cover year is defined as the period from October 1st of each calendar year to April 40th of the following year. October 1st is defined as the first day of the current snow cover year, and April 30th of the following year is defined as the 212th day of the current snow cover year, with the days in between being numbered sequentially.

[0092] The snow accumulation start date is the date corresponding to the first snowfall event in the snow accumulation year, recorded as SD;

[0093] The duration of the snowfall period is the time interval between the first snowfall event and the last snowfall event in a snowy year, denoted as T;

[0094] The snowfall occurrence rate is the frequency of snowfall events, which is the ratio of the total number of snowfalls in a snowy year to the duration of the snowfall period, denoted as v;

[0095] The snowfall interval is the duration between two adjacent snowfall events, denoted as L;

[0096] The snowfall intensity is the increased snow depth per snowfall, denoted as A;

[0097] The snow attenuation is the amount by which the accumulated snow on the ground on that day decreases compared to the snow on the previous day, recorded as d;

[0098] The annual maximum snow depth is the maximum value of all snow depth values ​​in the snow accumulation year, denoted as h max .

[0099] In this embodiment, S301 to S303 are intended to make the annual maximum value corresponding to the simulated snow depth record obey the probability distribution model of the local annual maximum snow depth, thereby ensuring that the simulated annual maximum snow depth has the same probability statistical characteristics as the actual observation value.

[0100] The above simulation process generates a daily snow depth record for a snow year. Users can repeat the operation as needed to carry out snow depth simulation for multiple snow years. The simulation process for a single snow year is as follows: Figure 4 shown.

[0101] Implementation method 2:

[0102] Reference Figures 5 to 10 This embodiment will be described.

[0103] This embodiment is a further example of the random simulation method of daily ground snow load sequence based on the Poisson process described in the first embodiment.

[0104] This implementation simulates the daily snow pressure data of 99 meteorological stations in a certain area.

[0105] Before the simulation, daily snow depth, precipitation, wind speed and atmospheric temperature data from each station from 1971 to 2018 were collected. The snow depth data were converted into snow water equivalent (SWE) data according to the method reported in the literature (considering that the density of water is 1000 kg / m 3 , snow water equivalent can be equivalent to snow pressure).

[0106] First, probabilistic modeling was performed for each variable involved in the model. The results are shown in Table 1. Different probability distribution models may be used for the snow cover start date (SD) and snowfall incidence (v) at different stations: a normal distribution was used when the normal distribution provided a better fit, while a lognormal distribution was used when the lognormal distribution provided a better fit. For snowfall intensity A and snow cover decay d, since no suitable probability distribution model accurately fits their probability distributions, their empirical probability distributions were used for modeling. Given their strong temporal dependence, each month was divided into the first and second ten-day periods, and separate empirical probabilities were established within each ten-day period to ensure that the simulation results are closer to reality (for example, snow cover decay in March and April is generally greater than in November and December).

[0107] Table 1: Probability distribution model of each model variable

[0108] variable name Probability distribution model Snow accumulation start date SD Normal distribution or lognormal distribution, different for each station Snowfall duration T normal distribution Snowfall incidence v Normal distribution or lognormal distribution, different for each station Snowfall intensity A Empirical probability distribution Snow attenuation d Empirical probability distribution

[0109] Afterwards, the daily snow pressure at all 99 stations was simulated according to the method described in Implementation Method 1.

[0110] Four representative stations were selected for the purpose of presenting the results. These stations, from north to south, are Station A, Station B, Station C, and Station D. Snowfall amounts decrease accordingly, representing different snowfall characteristics (Station A shows heavy snow accumulation, Station D shows intermittent snow accumulation, and Stations B and C fall somewhere in between). Figures 5 to 8 Selected simulation results from four representative stations are shown. As can be seen, the simulations successfully replicate the various snow accumulation patterns observed in actual observations, demonstrating that the proposed simulation method can effectively simulate daily snow pressure variations.

[0111] Figure 9 The comparison results of the probability distribution of the simulated and measured annual maximum snow water equivalent values ​​at four representative stations are given. It can be found that the simulated results are generally close to the measured results, indicating that the simulation method of the present invention can better simulate the distribution of the annual maximum value.

[0112] The simulated daily snow pressure data were used to estimate the maximum snow pressure at 99 stations (this is the definition of basic snow pressure) and compared with the maximum snow pressure at 99 stations based on the measured values. The results are as follows: Figure 10As shown in the figure, the horizontal axis represents the 50-year maximum snow pressure based on measured values, while the vertical axis represents the results based on simulated values. As can be seen, all the data points in the figure are located on both sides of the 1:1 diagonal line, indicating that the simulated values ​​are close to the measured values ​​and that the simulation method can effectively estimate the 50-year maximum snow pressure.

[0113] Implementation method three:

[0114] A system for stochastic simulation of daily ground snow load sequences based on a Poisson process is provided. The system is implemented based on the method for stochastic simulation of daily ground snow load sequences based on a Poisson process described in Embodiment 1. The system comprises:

[0115] Data processing module: used to collect snow cover data over the years, obtain snow cover variables over the years, and determine the probability distribution model of each variable;

[0116] Simulation control module: used to simulate the daily snow depth sequence within a snow year. The simulation process includes:

[0117] S201: randomly obtain simulated snowfall information for the current year based on the probability distribution model of each variable, wherein the snowfall information includes: the snow accumulation start date SD, the snowfall period duration T, the snowfall occurrence rate v, and the snowfall interval L.

[0118] S202: Setting the conditions for the snow accumulation start date SD, the snowfall period duration T, and the snowfall occurrence rate v of the current year;

[0119] S203: Set the snowfall counter i=1, and randomly generate the snowfall intensity A for the i-th snowfall i and snowfall interval L i , and then get the occurrence time t of the i+1th snow i+1 =t i +L i ;

[0120] S204: Set the snowfall counter i=i+1, and repeat S203 until t i >t end , and generate the snowfall intensity A of the last snow of the year end ;

[0121] S205: Initialize the snow depth of each day to 0, set the snowfall counter i = 1, the date counter j = t1, and set the snow depth of the jth day h j =A1;

[0122] S206: Let j = j + 1:

[0123] S207: If j <t i+1 , randomly generate the snow attenuation d for the dayj , then the snow depth h on day j j =h j-1 -d j , when h j <0, take h j =0;

[0124] S208: If j = t i+1 , then let h j =h j-1 +A i+1 , and let i=i+1;

[0125] S209: Repeat S206 to S208 until t i =t end ;

[0126] S210: Repeat S206 to S207 until h j =0;

[0127] Data output module: used to obtain daily snow depth data and complete the simulation of daily snow depth sequence within a snow accumulation year.

[0128] Those skilled in the art will understand that the above description is only a preferred embodiment of the present invention, and the features described in the various embodiments and / or claims of the present disclosure may be combined or coupled in various ways, even if such a combination or coupling is not explicitly described in the present disclosure. It is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art may still modify the technical solutions described in the aforementioned embodiments or 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 shall be included in the scope of protection of the present invention.

[0129] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0130] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

[0131] Those skilled in the art will appreciate that embodiments of the present disclosure may be provided as methods, systems, or computer program products. Thus, the present disclosure may take the form of a fully hardware implementation, a fully software implementation, or an implementation combining software and hardware. Furthermore, the present disclosure may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0132] The present disclosure is described with reference to flowcharts and / or block diagrams of methods, devices (systems) and computer program products according to embodiments of the present disclosure. It should be understood that each process and / or block in the flowchart and / or block diagram and the combination of processes and / or blocks in the flowchart and / or block diagram can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 These computer program instructions can also be stored in a computer-readable memory that can guide a computer or other programmable data processing device to work in a specific way, so that the instructions stored in the computer-readable memory produce a product including the instruction device, which implements the function specified in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0133] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0134] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present disclosure and are not intended to limit its scope of protection. Although the present disclosure has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that after reading the present disclosure, those skilled in the art can still make various changes, modifications or equivalent substitutions to the specific embodiments of the invention, but these changes, modifications or equivalent substitutions are all within the scope of protection of the disclosed claims.

Claims

1. A random simulation method for daily ground snow load sequence based on Poisson process, characterized in that: The method comprises: S1: Collect historical snow cover data, obtain historical snow cover variables, and determine the probability distribution model of each variable; S2: Simulate a daily snow depth sequence within a snow year, including: S201: randomly obtain simulated snowfall information for the current year based on the probability distribution model of each variable, wherein the snowfall information includes: the snow accumulation start date SD, the snowfall period duration T, the snowfall occurrence rate v, and the snowfall interval L. S202: Setting the conditions for the snow accumulation start date SD, the snowfall period duration T, and the snowfall occurrence rate v of the current year; S203: Set the snowfall counter i=1, and randomly generate the snowfall intensity A for the i-th snowfall i and snowfall interval L i , and then get the occurrence time t of the i+1th snow i+1 =t i +L i ; S204: Set the snowfall counter i=i+1, and repeat S203 until t i >t end , and generate the snowfall intensity A of the last snow of the year end ; S205: Initialize the snow depth of each day to 0, set the snowfall counter i = 1, the date counter j = t1, and set the snow depth of the jth day h j =A1; S206: let j=j+1; S207: If j < t i+1 , randomly generate the snow attenuation d for the day j , then the snow depth h on day j j =h j-1 -d j , when h j When <0, take h j =0; S208: If j = t i+1 , then let h j =h j-1 +A i+1 , and let i=i+1; S209: Repeat S206 to S208 until t i =t end ; S210: Repeat S206 to S207 until h j =0; S3: Obtain daily snow depth data and complete the simulation of daily snow depth sequence within a snow accumulation year.

2. The method for stochastic simulation of daily ground snow load sequence based on Poisson process according to claim 1 is characterized in that: The snow accumulation variables over the years include: the start date of snow accumulation over the years, the duration of snowfall over the years, the snowfall incidence over the years, the snowfall intensity over the years, the snowfall interval over the years and the snow attenuation over the years.

3. A random simulation method for daily ground snow load sequence based on Poisson process according to claim 1 or 2, characterized in that: The snow attenuation is the difference between the accumulated snow on the ground on the current day and the accumulated snow on the previous day.

4. The method for stochastic simulation of daily ground snow load sequence based on Poisson process according to claim 1 is characterized in that: In the above-mentioned S202, the snow accumulation start date SD is set to satisfy the condition of 1≤SD≤212, and the occurrence time of the first snowfall in the year is set to t1=SD.

5. The method for stochastic simulation of daily ground snow load sequence based on Poisson process according to claim 4 is characterized in that: In the above S202, the snowfall duration T of the year is set to satisfy the condition SD+T≤212, and the occurrence time of the last snowfall of the year is set to t end =SD+T.

6. The method for stochastic simulation of daily ground snow load sequence based on Poisson process according to claim 1 is characterized in that: In S202, the snowfall interval L is subject to the parameter exponential distribution.

7. The method for stochastic simulation of daily ground snow load sequence based on Poisson process according to claim 1 is characterized in that: Said S3 further comprises: S301: extracting the maximum simulated snow depth of the year from the simulated daily snow depth data; S302: randomly generate an annual maximum snow depth according to the local annual maximum snow depth probability distribution model; S303: Multiply the simulated daily snow depth data by a coefficient α, where α is the ratio of the maximum snow depth of the year to the maximum simulated snow depth of the year.

8. Computer equipment, characterized in that The method comprises a memory and a processor, wherein a computer program is stored in the memory. When the processor runs the computer program stored in the memory, the processor executes a random simulation method of daily ground snow load sequence based on a Poisson process according to any one of claims 1 to 7.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium is used to store a computer program, and the computer program executes the steps of a random simulation method of daily ground snow load sequence based on a Poisson process according to any one of claims 1 to 7.

10. A random simulation system for daily ground snow load sequence based on Poisson process, characterized in that: The system is implemented based on a random simulation method of daily ground snow load sequence based on a Poisson process according to any one of claims 1 to 7, and the system comprises: Data processing module: used to collect snow cover data over the years, obtain snow cover variables over the years, and determine the probability distribution model of each variable; Simulation control module: used to simulate the daily snow depth sequence within a snow year. The simulation process includes: S201: randomly obtain simulated snowfall information for the current year based on the probability distribution model of each variable, wherein the snowfall information includes: the snow accumulation start date SD, the snowfall period duration T, the snowfall occurrence rate v, and the snowfall interval L. S202: Setting the conditions for the snow accumulation start date SD, the snowfall period duration T, and the snowfall occurrence rate v of the current year; S203: Set the snowfall counter i=1, and randomly generate the snowfall intensity A for the i-th snowfall i and snowfall interval L i , and then get the occurrence time t of the i+1th snow i+1 =t i +L i ; S204: Set the snowfall counter i=i+1, and repeat S203 until t i >t end , and generate the snowfall intensity A of the last snow of the year end ; S205: Initialize the snow depth of each day to 0, set the snowfall counter i = 1, the date counter j = t1, and set the snow depth of the jth day h j =A1; S206: let j=j+1; S207: If j < t i+1 , randomly generate the snow attenuation d for the day j , then the snow depth h on day j j =h j-1 -d j , when h j When <0, take h j =0; S208: If j = t i+1 , then let h j =h j-1 +A i+1 , and let i=i+1; S209: Repeat S206 to S208 until t i =t end ; S210: Repeat S206 to S207 until h j =0; Data output module: used to obtain daily snow depth data and complete the simulation of daily snow depth sequence within a snow accumulation year.

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