Poisson process-based day-by-day ground snow load sequence stochastic simulation method and system

Abstract

The invention discloses a Poisson process-based day-by-day ground snow load sequence stochastic simulation method and system, belongs to the technical field of civil engineering, and solves the problem that the duration corresponding to each snowfall cannot be obtained from a measurement result, so that modeling cannot be performed on the duration of an event in simulation. According to the actual evolution process of the snow load, on the basis of the Poisson process model, the concept of the event duration is abandoned, the accumulated snow attenuation is introduced, and the time sequence of the ground snow load is constructed according to the occurrence time point of the snowfall event, the snowfall strength and the accumulated snow attenuation. Therefore, simulation of the day-by-day ground snow load time sequence is completed. The method is suitable for application scenes such as building risk assessment and meteorological station data analysis in snowy areas.

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 sequence. Background Art

[0002] Snowfall is an important form of precipitation and one of the important sources of fresh water. However, snowfall is a natural disaster for buildings, which may cause structural damage to buildings, cause economic losses and threaten the personal safety of the masses.

[0004] 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.

[0005] 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.

[0006] The ultimate limit state design of a structure requires the use of combinations of loads. At present, the common practice of load combination is to assume that a certain load reaches its ultimate limit state value, and the other loads participating in the combination adopt their point-in-time values, that is, the Turkstra criterion. The Code for Loads on Building Structures recommends that the combination value coefficient of snow loads can be taken as 0.7, but its clause explanation points out that "this code does not yet specify the method for determining the combination value, and it is mainly determined conservatively within the experience of engineering design." The Code for Seismic Design of Buildings recommends that when calculating the representative value of the gravity load of a building, the combination value coefficient of snow loads is 0.5, but "the combination value coefficient basically follows the value of the 78 code", which shows that no reliability analysis calculation has been performed. Therefore, future revisions to relevant codes may involve the determination of load combination value coefficients based on reliability analysis. At this time, it may be necessary to simulate the time series of variable loads including snow loads to determine the probability of exceedance of the load combination effect in the reference period.

[0007] 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-length periods, with the probability of non-zero snow load in each period being p, and the number of snowfall events (i.e., the number of periods with non-zero snow load) obeying a binomial distribution; the model assumes that the size of the snow load between each period satisfies the independent and identically distributed conditions, and the size of the snow load in the same period remains unchanged. In the application of the Bernoulli model, how to divide the periods is a very difficult problem. If the divided time period is too short, for example, one day is one time period, then considering that snowfall sometimes lasts for several days, the size of snow loads in different time periods is obviously difficult to be independent of each other, because they may be formed by the same snowfall; if the divided time period is too long, for example, one week or two weeks, since the Bernoulli model assumes that the size of snow load in the same time period is constant, there will be snow accumulation every day in the model and the amount of snow accumulation will be consistent; but for areas with relatively sparse snowfall, in a certain period of time, there may only be one or two days of snow accumulation in reality, which is quite different from the situation in the model, resulting in large errors; and for snowy areas, there is a problem that the independence of snow load size between different time periods cannot be satisfied, because the snow load in snowy areas is usually the result of continuous accumulation of snow, and the size of snow load in the latter period is affected by the previous period, so it cannot be considered independent of each other. Therefore, the application of the Bernoulli model is greatly limited.

[0008] 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 assumption of independent and identical distribution; the amount of snow accumulation will remain for a period of time after the snowfall event occurs, and during this period, the ground snow load remains unchanged; the duration of different snowfall events can overlap with each other. With the occurrence time, snowfall intensity and duration of the snowfall event, the time series of snow load can be formed by superimposing these events.

[0009] The existing ground snow load simulation method based on the Poisson process involves the concept of the duration of snowfall events. This concept is well understood in the physical sense, which is the length of time that the snow formed by each snowfall remains on the ground. However, it is difficult to identify it during data processing; this is because the snow pressure measured by the meteorological station is the accumulated snow pressure on the ground, which is caused by several previous snowfalls, but the surveyors do not observe the number of snow layers in the snow accumulation (one snow layer for each snowfall) and the corresponding age. Therefore, it is impossible to obtain the duration corresponding to each snowfall from the measurement results, so it is impossible to model the duration of the event in the simulation. In this regard, the existing simulation method mainly uses neural network algorithms to iteratively approximate it, making it a purely mathematical approximation problem, and losing the physical meaning that the duration should have. Summary of the invention

[0010] The present invention proposes a random simulation method and system for daily ground snow load sequence based on Poisson process, which aims to solve 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.

[0011] The present invention proposes a random simulation method for daily ground snow load sequence based on Poisson process, which includes:

[0012] S1: Collect snow cover data over the years, obtain snow cover variables over the years, and determine the probability distribution model of each variable;

[0013] S2: Simulate a daily snow depth sequence within a snow year, including:

[0014] S201: randomly obtain simulated snowfall information of the current year according to the probability distribution model of each variable, wherein the snowfall information includes: the snow accumulation start date SD of the current year, the snowfall period duration T of the current year, the snowfall occurrence rate v of the current year, and the snowfall interval L;

[0015] S202: Setting the conditions for the snow accumulation start date SD, the snowfall period T, and the snowfall occurrence rate v of the current year;

[0016] S203: Set the snowfall counter i=1, and randomly generate the snowfall intensity A of the i-th snowfall i and snowfall interval L i , and then get the occurrence time t of the i+1th snowfall i+1 =t i +L i ;

[0017] 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 snowfall of the year end ;

[0018] 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 h on the jth day j =A1;

[0019] S206: let j=j+1;

[0020] S207: If <t i+1 , randomly generate the snow attenuation d for the day j , then the snow depth h on the jth day j =h j-1 -d j , when h j <0, take h j =0;

[0021] S208: If j = t i+1 , then let h j =h j-1 +A i+1 , and let i=i+1;

[0022] S209: Repeat S206 to S208 until t i =t end ;

[0023] S210: Repeat S206 to S207 until h j =0;

[0024] S3: Obtain daily snow depth data and complete the simulation of daily snow depth sequence within a snow accumulation year.

[0025] 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 incidence rate over the years, the snowfall intensity over the years, the snowfall interval over the years and the snow attenuation.

[0026] Furthermore, a preferred solution is provided: the snow attenuation amount is: the difference between the accumulated snow on the ground on the current day and the accumulated snow on the ground on the previous day.

[0027] 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 of the year is set to t1=SD.

[0028] Furthermore, a preferred solution is provided: 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.

[0029] Furthermore, a preferred solution is provided: in S202, the snowfall interval L is subject to the parameter The exponential distribution of .

[0030] Furthermore, a preferred solution is provided: S3 further includes:

[0031] S301: extracting the maximum simulated snow depth of the year from the simulated daily snow depth data;

[0032] S302: randomly generate an annual maximum snow depth according to the probability distribution model of the local annual maximum snow depth;

[0033] 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.

[0034] 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 sequence based on a Poisson process according to any combination of the above-mentioned schemes.

[0035] 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.

[0036] The present invention further proposes a random simulation system for daily ground snow load sequence based on Poisson process, the system is implemented based on a random simulation method for daily ground snow load sequence based on Poisson process described in any one of the above scheme combinations, and the system comprises:

[0037] 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;

[0038] Simulation control module: used to simulate the daily snow depth sequence in a snow accumulation year. The simulation process includes:

[0039] S201: randomly obtain simulated snowfall information of the current year according to the probability distribution model of each variable, wherein the snowfall information includes: the snow accumulation start date SD of the current year, the snowfall period duration T of the current year, the snowfall occurrence rate v of the current year, and the snowfall interval L;

[0040] S202: Setting the conditions for the snow accumulation start date SD, the snowfall period T, and the snowfall occurrence rate v of the current year;

[0041] S203: Set the snowfall counter i=1, and randomly generate the snowfall intensity A of the i-th snowfall i and snowfall interval L i , and then get the occurrence time t of the i+1th snowfall i+1 =t i +L i ;

[0042] 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 snowfall of the year emd ;

[0043] 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 h on the jth dayj =A1;

[0044] S206: let j=j+1;

[0045] S207: If <t i+1 , randomly generate the snow attenuation d for the day j , then the snow depth h on the jth day j =h j-1 -d j , when h j <0, take h j =0;

[0046] S208: If j = t i+1 , then let h j =h j-1 +A i+1 , and let i=i+1;

[0047] S209: Repeat S206 to S208 until t i =t end ;

[0048] S210: Repeat S206 to S207 until h j =0;

[0049] Data output module: used to obtain daily snow depth data and complete the simulation of daily snow depth sequence within a snow accumulation year.

[0050] According to the actual evolution of snow load, this method abandons the concept of event duration and introduces snow attenuation based on the above-mentioned Poisson process model. The time series of ground snow load is constructed by the occurrence time of snowfall events, snowfall intensity and snow attenuation, thereby completing the simulation of the daily ground snow load time series. The snow load time series can be a daily snow depth series or a daily snow pressure series. This method takes the snow depth series as an example when describing it, and the snow pressure series can be executed as a reference.

[0051] The method of the present invention can simulate the daily snow depth sequence in intermittent snowfall areas (snow does not accumulate) and can also simulate the daily snow depth sequence in cumulative snowfall areas (snow accumulates). At the same time, since the annual maximum value of the simulation result is forced to obey the probability distribution model of the actual annual maximum value, the statistical characteristics of the simulation result can be guaranteed to be consistent with the actual observation results.

[0052] 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 2, this method takes into account the attenuation of snow accumulation), and the simulation process is more consistent with the physical process of snow accumulation. At the same time, the original simulation method involves the use of network neural algorithms (black box operation, unclear concept), which is not involved in the present invention. The traditional method of generating random numbers is used, so the technology is simpler and the concept is clearer.

[0053] 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

[0054] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. 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 paying any creative work.

[0055] Figure 1 It is a schematic diagram of a model based on a Bernoulli process described in the background technology of the present invention;

[0056] Figure 2 It is a schematic diagram of a model based on a Poisson process described in the background technology of the present invention;

[0057] Figure 3 This is a schematic diagram of model parameters described in the first specific implementation mode of the present invention;

[0058] Figure 4 A flowchart of daily snow depth simulation for a single snow year according to the first specific implementation mode of the present invention;

[0059] Figure 5 It is a comparison diagram of the partial simulation results (lower) and the observed values ​​(upper) of station A described in the second specific implementation mode of the present invention;

[0060] Figure 6 A comparison chart of the partial simulation results (lower) and the observed values ​​(upper) of Station B according to the second specific implementation mode of the present invention;

[0061] Figure 7 It is a comparison diagram of the simulation results (lower) and the observed values ​​(upper) of the C station part described in the second specific implementation mode of the present invention;

[0062] Figure 8 It is a comparison diagram of the simulation results (lower) and the observed values ​​(upper) of the D station part described in the second specific implementation mode of the present invention;

[0063] Fig. 9 A comparison chart of the probability distribution of the simulated and measured annual maximum snow water equivalent values ​​of the four representative stations described in the second specific implementation mode of the present invention;

[0064] Fig.10 This is a comparison diagram of the maximum snow pressure once in 50 years based on the simulation value and the measured value as described in the second specific implementation mode of the present invention. DETAILED DESCRIPTION

[0065] In the following description, specific details such as specific system structures and technologies are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may also 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 prevent unnecessary details from obstructing the description of the present application.

[0066] It should 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 exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or groups thereof.

[0067] It should also be understood that the terms used in this application specification are only for the purpose of describing specific embodiments and are not intended to limit the present application. As used in this application specification and the appended claims, the singular forms "a", "an" and "the" are intended to include plural forms unless the context clearly indicates otherwise.

[0068] The following is a clear and complete description of the technical solutions in the embodiments of the present application in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0069] In the following description, many specific details are set forth to facilitate a full understanding of the present application, but the present application may also be implemented in other ways different from those described herein, and 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.

[0070] Implementation method 1:

[0071] Reference Figure 3 , Figure 4 This embodiment will be described.

[0072] This implementation method needs to analyze the snow accumulation data of previous years provided by the local meteorological station before simulation, and extract the snow accumulation start date SD, snowfall period duration T, snowfall intensity A, snow accumulation attenuation d, snowfall occurrence rate v, and annual maximum snow depth h from the daily snow depth data of previous years. max The sample values ​​are then analyzed for probability statistics to determine the probability distribution model of each variable.

[0073] 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:

[0074] A random simulation method for daily ground snow load sequence based on Poisson process, the method comprising:

[0075] S1: Collect snow accumulation data over the years, obtain snow accumulation variables over the years, and determine the probability distribution model of each variable; 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 incidence rate over the years, the snowfall intensity over the years, the snowfall interval over the years and the snow accumulation attenuation; the snow accumulation attenuation is: the difference between the accumulated snow accumulation on the ground on the day and the accumulated snow accumulation on the previous day;

[0076] S2: Simulate a daily snow depth sequence within a snow year, including:

[0077] S201: randomly obtain simulated snowfall information of the current year according to the probability distribution model of each variable, wherein the snowfall information includes: the snow accumulation start date SD of the current year, the snowfall period duration T of the current year, the snowfall occurrence rate v of the current year, and the snowfall interval L;

[0078] 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 first snowfall time of the year to t1=SD; set the snowfall duration T to SD+T≤212, and set the last snowfall time to t end =SD+T; the snowfall interval L is subject to the parameter exponential distribution of

[0079] S203: Set the snowfall counter i=1, and randomly generate the snowfall intensity A of 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 snowfall i+1 =t i +L i ;

[0080] S204: Set the snowfall counter i=i+1, and repeat S203 until ti >t end , and generate the snowfall intensity A of the last snowfall of the year end ;

[0081] 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 h on the jth day j =A1;

[0082] S206: let j=j+1:;

[0083] S207: If <t i+1 , the snow attenuation d of the day is randomly generated according to the probability distribution model of snow attenuation d j , then the snow depth h on the jth day j =h j-1 -d j , when h j <0, take h j =0;

[0084] S208: If j = t i+1 (i.e. the day of the i+1th snowfall), let h j =h j-1 +A i+1 , and let i=i+1;

[0085] S209: Repeat S206 to S208 until t i =t end ;

[0086] S210: Repeat S206 to S207 until h j =0;

[0087] S3: Obtain daily snow depth data and complete the simulation of daily snow depth sequence within a snow accumulation year

[0088] S301: Extract the maximum simulated snow depth h of the year from the simulated daily snow depth data max,s ;

[0089] 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 ;

[0090] 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, that is,

[0091] Specifically:

[0092] The snow year is from October 1st to April 40th of the following year in the calendar year. October 1st is defined as the first day of the current snow year, and April 30th of the following year is defined as the 212th day of the current snow year, and the days in between are numbered sequentially.

[0093] The snow accumulation start date is the date corresponding to the first snowfall event in the snow accumulation year, recorded as SD;

[0094] The duration of the snowfall period is the time interval between the first snowfall event and the last snowfall event in the snowy year, denoted as T;

[0095] 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;

[0096] The snowfall interval is the duration between two adjacent snowfall events, denoted as L;

[0097] The snowfall intensity is the increased snow depth per snowfall, denoted as A;

[0098] The snow attenuation is the decrease in the accumulated snow on the ground on that day compared with the snow on the previous day, recorded as d;

[0099] The annual maximum snow depth is the maximum value of all snow depths in the snow accumulation year, denoted as h max .

[0100] 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.

[0101] The above simulation process generates a daily snow depth record for a snow year. Users can repeat the operation as needed to simulate the snow depth for multiple snow years. The simulation process for a single snow year is as follows: Figure 4 shown.

[0102] Implementation method 2:

[0103] Reference Figures 5 to 10 This embodiment will be described.

[0104] This embodiment is a further example of the random simulation method of daily ground snow load sequence based on Poisson process described in the first embodiment.

[0105] This implementation method simulates the daily snow pressure data of 99 meteorological stations in a certain area.

[0106] Before the simulation, the daily snow depth, precipitation, wind speed and atmospheric temperature data of each station from 1971 to 2018 were collected, and 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 is equivalent to snow pressure).

[0107] First, probability modeling was performed for each variable involved in the model, and the results are shown in Table 1. Among them, the snow start date SD and the snowfall occurrence rate v may use different probability distribution models at different stations: the normal distribution is used when the normal distribution fits better, and the log-normal distribution is used when the log-normal distribution fits better. For the snowfall intensity A and the snow attenuation d, since there is no suitable probability distribution model that can accurately fit their probability distribution, their empirical probability distribution is used for modeling; considering that they have strong time dependence, each month is divided into the upper and lower ten days, and their empirical probabilities are established separately in each ten days to ensure that the simulation results are closer to the actual situation (for example, the snow attenuation in March / April is generally greater than that in November / December).

[0108] Table 1: Probability distribution model of each model variable

[0109] Variable Name Probability distribution model Snow accumulation start date SD Normal or lognormal distribution, different for each station Snowfall duration T normal distribution Snowfall incidence v Normal or lognormal distribution, different for each station Snowfall intensity A Empirical Probability Distribution Snow attenuation d Empirical Probability Distribution

[0110] Thereafter, the daily snow pressure at all 99 stations is simulated according to the method described in Implementation 1.

[0111] In order to present the results, four representative stations were selected. The four stations are Station A, Station B, Station C and Station D from north to south, and the snowfall amounts are from large to small, representing different snowfall characteristics (Station A is severe snow accumulation, Station D is close to intermittent snow accumulation, and Station B and Station C are between the two). Figures 5 to 8 Some simulation results of four representative stations are shown. As can be seen from the figure, the simulation results can successfully simulate the different snow accumulation forms in the actual observation results. This shows that the simulation method can well simulate the daily changes in snow pressure.

[0112] Fig. 9 The comparison results of the probability distribution of the simulated values ​​and the measured values ​​of the annual maximum snow water equivalent of four representative stations are given. It can be found that the simulation 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.

[0113] Using the simulated daily snow pressure data, the maximum snow pressure of 50 years at 99 stations (this is the definition of basic snow pressure) was estimated and compared with the maximum snow pressure of 50 years based on measured values. The results are as follows: Fig.10As shown in the figure, the horizontal axis is the maximum snow pressure of 50 years based on the measured value, and the vertical axis is the result based on the simulated value. It can be seen that all the data points in the figure are located on both sides of the 1:1 diagonal line, indicating that the simulated value is close to the measured value, and that the simulation method can better simulate the estimation of the maximum snow pressure of 50 years.

[0114] Implementation method three:

[0115] A random simulation system for daily ground snow load sequence based on a Poisson process, the system is implemented based on a random simulation method for daily ground snow load sequence based on a Poisson process described in implementation mode 1, and the system includes:

[0116] 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;

[0117] Simulation control module: used to simulate the daily snow depth sequence in a snow accumulation year. The simulation process includes:

[0118] S201: randomly obtain simulated snowfall information of the current year according to the probability distribution model of each variable, wherein the snowfall information includes: the snow accumulation start date SD of the current year, the snowfall period duration T of the current year, the snowfall occurrence rate v of the current year, and the snowfall interval L;

[0119] S202: Setting the conditions for the snow accumulation start date SD, the snowfall period T, and the snowfall occurrence rate v of the current year;

[0120] S203: Set the snowfall counter i=1, and randomly generate the snowfall intensity A of the i-th snowfall i and snowfall interval L i , and then get the occurrence time t of the i+1th snowfall i+1 =t i +L i ;

[0121] 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 snowfall of the year end ;

[0122] 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 h on the jth day j =A1;

[0123] S206: let j=j+1:;

[0124] S207: If <t i+1 , randomly generate the snow attenuation d for the dayj , then the snow depth h on the jth day j =h j-1 -d j , when h j <0, take h j =0;

[0125] S208: If j = t i+1 , then let h j =h j-1 +A i+1 , and let i=i+1;

[0126] S209: Repeat S206 to S208 until t i =t end ;

[0127] S210: Repeat S206 to S207 until h j =0;

[0128] Data output module: used to obtain daily snow depth data and complete the simulation of daily snow depth sequence within a snow accumulation year.

[0129] Those skilled in the art will appreciate that the above are only preferred embodiments of the present invention, and the various embodiments of the present disclosure and / or the features described in the claims may be combined or combined in various ways, even if such combinations or combinations are 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 perform equivalent substitutions on 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 protection scope of the present invention.

[0130] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other 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.

[0131] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

[0132] It will be appreciated by those skilled in the art that the embodiments of the present disclosure may be provided as methods, systems or computer program products. Therefore, the present disclosure may be provided in the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware. Furthermore, the present disclosure may be provided in the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0133] 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 an instruction device, which implements the functions specified in the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0134] 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 instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0135] Finally, it should be noted that the above implementation modes are only used to illustrate the technical solutions of the present disclosure rather than to limit its protection scope. Although the present disclosure has been described in detail with reference to the above implementation modes, 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 implementation modes of the invention, but these changes, modifications or equivalent substitutions are all within the protection scope of the disclosed claims to be approved.

Claims

1. A random simulation method for daily ground snow load sequence based on Poisson process, characterized in that: The method comprises: S1: Collect snow cover data over the years, obtain snow cover variables over the years, 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 of the current year according to the probability distribution model of each variable, wherein the snowfall information includes: the snow accumulation start date SD of the current year, the snowfall period duration T of the current year, the snowfall occurrence rate v of the current year, and the snowfall interval L; S202: Setting the conditions for the snow accumulation start date SD, the snowfall period 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 of the i-th snowfall i and snowfall interval L i , and then get the occurrence time t of the i+1th snowfall 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 snowfall 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 h on the jth day 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 the jth day 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 snow accumulation start date over the years, the snowfall period duration over the years, the snowfall incidence rate 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 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 of 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 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 The exponential distribution of .

7. The method for stochastic simulation of daily ground snow load sequence based on Poisson process according to claim 1 is characterized in that: The S3 further includes: 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 probability distribution model of the local annual maximum snow depth; 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 device, characterized in that It comprises a memory and a processor, wherein a computer program is stored in the memory, and 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 1-7.

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 of 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 as described in 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 in a snow accumulation year. The simulation process includes: S201: randomly obtain simulated snowfall information of the current year according to the probability distribution model of each variable, wherein the snowfall information includes: the snow accumulation start date SD of the current year, the snowfall period duration T of the current year, the snowfall occurrence rate v of the current year, and the snowfall interval L; S202: Setting the conditions for the snow accumulation start date SD, the snowfall period 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 of the i-th snowfall i and snowfall interval L i , and then get the occurrence time t of the i+1th snowfall 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 snowfall 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 h on the jth day 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 the jth day 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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