Real-time monitoring method and system for sand flow under regional environment characteristics

Abstract

The invention relates to the technical field of sandstorm disaster monitoring, and relates to a sandstorm flow real-time monitoring method and system under regional environment characteristics. According to the method, the soil moisture content, the soil compactness and the vegetation coverage are collected, the initial dusting wind speed threshold value is determined in combination with the real-time wind direction and the historical log, the dusting wind speed threshold value is dynamically corrected through the earth surface wind resistance deviation coefficient, and the dusting wind speed threshold value is compared with the real-time wind speed collected in the to-be-detected area to judge whether the wind sand flowing risk exists or not. When the wind sand flowing risk exists, the collected surface sand layer height change data and the real-time wind speed are subjected to coupling analysis to determine the real-time wind sand index, so that prevention and control personnel visually master the wind sand damage degree, and meanwhile, the current wind sand severity level and the wind sand flowing change trend are determined according to the real-time wind sand index; the prevention and control work is changed from passive disaster response to active prevention in advance, time is won for reinforcing protection facilities in advance and storing prevention and control materials, and the effect of effectively reducing sand storm disaster loss is achieved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of wind-sand disaster monitoring, and relates to a real-time monitoring method and system for wind-sand flow under regional environmental characteristics. BACKGROUND

[0002] With the change of climate and environment and the expansion of human activities, the suddenness and uncertainty of wind-sand flow have significantly increased. The traditional monitoring methods based on fixed-point manual observation or regular remote sensing inversion have been difficult to meet the demand for wind-sand flow due to problems such as data update lag and limited coverage. Therefore, real-time and accurate monitoring of wind-sand flow, timely grasping of wind-sand occurrence risk, intensity level and change trend are the key prerequisites for carrying out wind-sand disaster prevention and control, ensuring regional ecological safety and stable operation of infrastructure.

[0003] The prior art such as Chinese Patent Publication No. CN120352306A discloses a desert photovoltaic sand damage dynamic monitoring method and system based on a UAV. The method uses a UAV to carry multiple modal sensors to obtain dust data in the photovoltaic power station area, obtains a sand dust feature matrix through multi-modal data fusion, and then calculates the particle migration probability distribution to finally generate a three-dimensional dynamic sand dust concentration field model, and outputs the deposition density distribution and erosion risk area. This scheme realizes high-altitude and high-resolution imaging of sand damage around the photovoltaic panel, solves the limitations of single-point and static monitoring, and provides dynamic data support for desert photovoltaic operation and maintenance.

[0004] However, the prior art has the following problems: 1. The prior art only constructs a monitoring model by associating dust data and wind speed characteristics, without considering the influence of environmental parameters in the to-be-measured region on the sand-raising wind speed. Differences in soil and vegetation conditions in different regions or different locations in the same region will directly result in significant differences in actual sand-raising wind speed, and fixed judgment criteria are easy to cause misjudgment of wind-sand flow risk, resulting in insufficient monitoring accuracy.

[0005] 2. The prior art only outputs the deposition density distribution and erosion risk area, and does not construct an index that can quantify the severity of wind-sand. Moreover, it cannot predict the change trend of wind-sand flow, which makes the prevention and control department only be able to respond to the sand damage that has occurred, and it is difficult to develop targeted prevention and control measures in advance, resulting in insufficient timeliness and foresight. SUMMARY

[0006] The present application aims to provide a real-time monitoring method and system for wind-sand flow under regional environmental characteristics to solve the problems of insufficient monitoring accuracy and real-time performance and lack of trend prediction ability in the prior art.

[0007] The technical scheme adopted by the present application to solve its technical problems is: on the one hand, the present application provides a real-time monitoring method for wind-sand flow under regional environmental characteristics, comprising: collecting real-time environmental characteristic parameters of a to-be-measured region in real time, wherein the real-time environmental characteristic parameters include soil moisture content, soil compactness and vegetation coverage.

[0008] According to the real-time wind direction collected in the to-be-measured region, an initial sand-raising wind speed threshold is determined in combination with a historical wind-sand monitoring log of the to-be-measured region, and the initial sand-raising wind speed threshold is dynamically corrected based on the real-time environmental characteristic parameters to obtain a corrected sand-raising wind speed threshold.

[0009] The real-time wind speed collected in the to-be-measured region is compared with the corrected sand-raising wind speed threshold to determine whether the to-be-measured region has a wind-sand flow risk.

[0010] When the to-be-measured region has a wind-sand flow risk, surface sand layer height change data in the to-be-measured region are collected, and the surface sand layer height change data are coupled with the real-time wind speed to determine a real-time wind-sand index.

[0011] According to the real-time wind-sand index, a current wind-sand severity level is determined, and a trend analysis is performed on the real-time wind-sand index and a recent historical wind-sand index to determine a wind-sand flow change trend of the to-be-measured region.

[0012] On the other hand, a real-time monitoring system for wind-sand flow under regional environmental characteristics comprises an environmental characteristic collection module, a sand-raising wind speed correction module, a wind-sand flow risk determination module, a real-time wind speed index analysis module and a change trend determination module.

[0013] The connection relationship between the modules is that the environmental characteristic collection module is in communication connection with the sand-raising wind speed correction module, the wind-sand flow risk determination module is in communication connection with the sand-raising wind speed correction module and the real-time wind speed index analysis module respectively, and the change trend determination module is in communication connection with the real-time wind speed index analysis module.

[0014] The environmental characteristic collection module collects real-time environmental characteristic parameters of a to-be-measured region in real time.

[0015] The sand-raising wind speed correction module determines an initial sand-raising wind speed threshold according to a real-time wind direction collected in the to-be-measured region in combination with a historical wind-sand monitoring log of the to-be-measured region, and dynamically corrects the initial sand-raising wind speed threshold based on real-time environmental characteristic parameters to obtain a corrected sand-raising wind speed threshold.

[0016] The wind-sand flow risk determination module compares a real-time wind speed collected in the to-be-measured region with the corrected sand-raising wind speed threshold to determine whether the to-be-measured region has a wind-sand flow risk.

[0017] A real-time wind speed index analysis module, when the wind-sand flow risk exists in the to-be-measured region, collects the ground sand layer height change data in the to-be-measured region, and determines the real-time wind-sand index by coupling analysis of the ground sand layer height change data and the real-time wind speed.

[0018] A change trend judgment module determines the current wind-sand severity level according to the real-time wind-sand index, and judges the wind-sand flow change trend of the to-be-measured region by trend analysis of the real-time wind-sand index and the recent historical wind-sand index.

[0019] Compared with the prior art, the present application has the following beneficial effects: (1) The present application collects the soil moisture content, soil tightness and vegetation coverage in real time, determines the initial sand-raising wind speed threshold value in combination with the real-time wind direction and historical logs, and dynamically corrects the sand-raising wind speed threshold value through the ground wind deviation resistance coefficient, thereby avoiding the problem of misjudgment due to ignoring the regional environmental differences, making the sand-raising wind speed threshold value accurately adapt to the real-time environmental differences of the to-be-measured region, and making the risk judgment result more in line with the actual situation of the to-be-measured region.

[0020] (2) The present application collects the ground sand layer height change data in the to-be-measured region, couples the ground sand layer height change data with the real-time wind speed, determines the quantitative real-time wind-sand index through standardized processing and a pre-fitted linear regression equation, provides clear data basis for wind-sand severity level division, enables the prevention and control personnel to intuitively master the wind-sand hazard degree, and then formulates differentiated prevention and control measures such as light cleaning and strengthened protection.

[0021] (3) The present application integrates the wind-sand index change sequence of the to-be-measured region in the corresponding recent continuous time period with the real-time wind-sand index, forms a real-time wind-sand index time sequence, judges the change trend by calculating the slope through a smooth curve fitting, thereby being able to predict in advance the strengthening, weakening or stable trend of the wind-sand flow, enabling the prevention and control work to change from passive response to disaster to active prevention in advance, saving time for reinforcing the protection facilities in advance and reserving prevention and control materials, and effectively reducing the loss of wind-sand disaster. BRIEF DESCRIPTION OF DRAWINGS

[0022] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiment description. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without any creative labor.

[0023] Figure 1 The present application is a method step flowchart.

[0024] Figure 2 The present application is an initial sand-raising wind speed threshold value determination step flowchart.

[0025] Figure 3 The fitting step of the wind-sand index linear regression equation in the present application is shown in the figure.

[0026] Figure 4 The connection of the system modules in the present application is shown in the figure. DETAILED DESCRIPTION

[0027] Various exemplary embodiments of the present application will now be described in detail with reference to the accompanying drawings. Note that the relative arrangement, numerical expressions, and numerical values of components and steps set forth in these embodiments are not limiting to the scope of the present application unless otherwise specifically stated. Also, it should be understood that the dimensions of the various parts shown in the drawings are not drawn to scale for the sake of convenience of description.

[0028] The following description of at least one example embodiment is merely illustrative in nature and is in no way limiting to the scope of the application and its applications or uses. Techniques, methods, and devices known to those of ordinary skill in the relevant art can not be discussed in detail, but should be understood to be part of the specification, where appropriate.

[0029] In all examples shown and discussed herein, any specific values should be interpreted as merely illustrative and not as limiting. Thus, other examples of the example embodiments can have different values.

[0030] Referring to Figure 1 As shown, the present application provides a real-time monitoring method for wind-sand flow under regional environmental characteristics, comprising: step one, collecting real-time environmental characteristic parameters of the region to be measured in real time, wherein the real-time environmental characteristic parameters include soil moisture content, soil compactness, and vegetation coverage.

[0031] It should be noted that the real-time environmental characteristic parameters of the region to be measured are collected in the following manner: a plurality of monitoring sub-points are arranged according to a uniform grid in the region to be measured, and soil moisture content sensors and soil compactness sensors are respectively arranged at each monitoring sub-point to collect soil moisture content and soil compactness in a predetermined depth range below the soil surface.

[0032] The soil moisture content and soil compactness collected by each monitoring sub-point are subjected to data mean processing to obtain the soil moisture content and soil compactness of the region to be measured.

[0033] A high-definition remote sensing image of the region to be measured is obtained using an optical remote sensing device, a vegetation-covered area in the high-definition remote sensing image is identified, and the pixel proportion of vegetation coverage is determined as the vegetation coverage of the region to be measured.

[0034] The setting of the preset depth range is based on the fact that, in the starting process of the wind-sand flow, the effect of wind on the ground surface is mainly concentrated in the depth range of 0-30 cm of the soil surface layer, which is the active layer where sand particles are most easily stripped and migrated by wind.

[0035] The essence of the wind-sand flow is the process in which wind overcomes the wind erosion resistance of the ground surface to make sand particles separate from the ground surface and migrate. Soil moisture content, soil compactness, and vegetation coverage are three key factors that directly determine the wind erosion resistance of the ground surface.

[0036] Water in the soil can enhance the cohesion and adhesion between sand particles by forming a water film between the sand particles. When the soil moisture content is high, sand particles are more difficult to be blown away from the ground surface by wind, and a higher wind speed is required to start the wind-sand flow. Conversely, in a drought state, the soil moisture content is extremely low, the adhesion between sand particles is weak, and a slight wind can cause sand to be blown.

[0037] Soil compactness represents the arrangement density and compaction degree of soil particles, and directly determines the ability of the ground surface to resist wind shear. Soil with high compactness has stable structure and it is difficult for wind to destroy the integrity of the ground surface. Loose soil with low compactness is easy to be stripped by wind. For example, the farmland at the edge of the desert has a lower compactness due to ploughing, and its sand blowing risk is much higher than that of the adjacent natural grassland. Therefore, soil compactness must be considered as a key parameter for differentiating the sand blowing conditions.

[0038] The inhibitory effect of vegetation on the wind-sand flow is reflected in two aspects: first, the vegetation canopy can reduce the near-surface wind speed and reduce the direct effect of wind on the ground surface; second, the vegetation roots can fix soil particles and enhance the stability of the ground surface structure. When the vegetation coverage is low, the ground surface lacks effective protection, and the sand blowing risk increases significantly. In areas with high vegetation coverage, even if the wind speed exceeds the conventional sand blowing threshold, no obvious wind-sand flow may occur. Therefore, vegetation coverage is a key indicator for distinguishing between potential sand blowing areas and stable areas.

[0039] In step two, the initial sand blowing wind speed threshold is determined according to the real-time wind direction collected from the to-be-measured area, combined with the historical wind-sand monitoring logs of the to-be-measured area, and the initial sand blowing wind speed threshold is dynamically corrected based on the real-time environmental characteristic parameters to obtain the corrected sand blowing wind speed threshold.

[0040] As shown in Figure 2 The initial sand blowing wind speed threshold is determined as follows: according to the real-time wind direction collected from the to-be-measured area, the same historical monitoring logs as the real-time wind direction are retrieved from the historical wind-sand monitoring logs of the to-be-measured area.

[0041] Discrete degree evaluation is performed on the sand-starting wind speed recorded in each historical monitoring log, and if the discrete degree is less than the set discrete degree, the average value of the sand-starting wind speed of each historical monitoring log is taken as the initial sand-starting wind speed threshold. In the embodiment of the application, the discrete degree is evaluated by using the variance standard formula.

[0042] On the contrary, the maximum sand-starting wind speed and the minimum sand-starting wind speed corresponding to each historical monitoring log are removed, the sand-starting wind speed of the remaining historical monitoring logs is re-evaluated for discrete degree, and the initial sand-starting wind speed threshold is determined.

[0043] The above content finally determines the initial sand-starting wind speed threshold by performing discrete degree evaluation on the historical sand-starting wind speed and optimizing the data by removing extreme values, thereby effectively filtering abnormal values in the historical data, ensuring that the initial threshold can reflect the normal sand-starting level under the wind direction condition, and avoiding distortion of the initial benchmark caused by abnormal data.

[0044] The method further comprises the following steps: screening the historical monitoring log with the closest sand-starting wind speed to the initial sand-starting wind speed threshold from the retrieved historical monitoring logs, and taking the historical environmental characteristic parameter recorded in the historical monitoring log as a reference environmental characteristic parameter. The reference environmental characteristic parameter provides a comparable benchmark for quantifying the influence of real-time environmental differences on the sand-starting wind speed, thereby supporting the calculation of the surface wind deviation resistance coefficient and the accurate correction of the sand-starting wind speed threshold.

[0045] The real-time environmental characteristic parameter and the reference environmental characteristic parameter are subjected to deviation analysis, and the soil water content deviation rate, the soil compactness deviation rate and the vegetation coverage deviation rate are obtained.

[0046] Based on the correlation analysis of the soil water content, the soil compactness, the vegetation coverage and the sand-starting wind speed in the retrieved historical monitoring logs, the influence weight of the soil water content, the soil compactness and the vegetation coverage on the sand-starting wind speed is determined by using the analytic hierarchy process.

[0047] The soil water content deviation rate, the soil compactness deviation rate and the vegetation coverage deviation rate are multiplied by the corresponding influence weight, and the sum is taken to obtain the surface wind deviation resistance coefficient.

[0048] The initial sand-starting wind speed threshold is taken as a benchmark, and a correction formula is established in combination with the surface wind deviation resistance coefficient, and the corrected sand-starting wind speed threshold is calculated.

[0049] The application collects the soil water content, the soil compactness and the vegetation coverage in real time, determines the initial sand-starting wind speed threshold in combination with the real-time wind direction and the historical logs, and dynamically corrects the sand-starting wind speed threshold by using the surface wind deviation resistance coefficient, thereby avoiding the problem of misjudgment caused by neglecting regional environmental differences, making the sand-starting wind speed threshold accurately adapt to the real-time environmental differences of the to-be-tested region, and making the risk judgment result more suitable for the actual situation of the to-be-tested region.

[0050] The formula is corrected as follows in the embodiment: .

[0051] In the formula, represents the corrected sand starting wind speed threshold, represents the initial sand starting wind speed threshold, is a ground surface wind deviation coefficient, represents a key quantitative factor connecting the real-time environmental feature difference and the sand starting wind speed threshold adjustment, and the core role is to proportionally adjust the initial sand starting wind speed threshold according to the change in the wind resistance of the real-time environment of the to-be-measured region relative to the initial threshold corresponding to the historical environment, to ensure that the corrected threshold accurately adapts to the actual sand starting condition of the current environment.

[0052] The soil water content, the soil compactness, and the vegetation coverage deviation rate are obtained by taking the difference between the real-time parameter and the reference parameter, and taking the ratio of the difference to the reference parameter as the deviation rate. When the deviation rate is positive, it means that the real-time environment increases the sand starting difficulty; and when the deviation rate is negative, it means that the real-time environment reduces the sand starting difficulty.

[0053] In a specific embodiment, the determination of the influence weight of the soil water content, the soil compactness, and the vegetation coverage on the sand starting wind speed is as follows: according to the soil water content, the soil compactness, the vegetation coverage, and the sand starting wind speed in each historical monitoring log, the Pearson correlation coefficient method is used to calculate the correlation coefficients of the soil water content and the sand starting wind speed, the soil compactness and the sand starting wind speed, and the vegetation coverage and the sand starting wind speed. The greater the absolute value of the correlation coefficient, the stronger the influence of the parameter on the sand starting wind speed, and the higher the importance of the parameter in weight distribution.

[0054] The importance scale between the corresponding parameters of the soil water content, the soil compactness, and the vegetation coverage is determined by using the scale method commonly used in the analytic hierarchy process method and the correlation coefficients.

[0055] The judgment matrix is constructed according to all the importance scales, and the influence weight of the soil water content, the soil compactness, and the vegetation coverage on the sand starting wind speed is obtained by using the sum product method to solve the judgment matrix. The sum product method is a prior art, and will not be described in detail herein.

[0056] The scale meaning of the scale method is as follows: 1 means that the two factors are equally important, 3 means that the former is slightly more important than the latter, 5 means that the former is obviously more important than the latter, 7 means that the former is strongly more important than the latter, 9 means that the former is extremely more important than the latter, 2 / 4 / 6 / 8 are intermediate values of adjacent scales, and the reciprocal of the scale is the reverse comparison result. The importance scale between the corresponding parameters of the soil water content, the soil compactness, and the vegetation coverage is determined by combining the size relationship of the correlation coefficients of the soil water content and the sand starting wind speed, the soil compactness and the sand starting wind speed, and the vegetation coverage and the sand starting wind speed.

[0057] For example, the correlation coefficient of vegetation coverage and sand-raising wind speed is r3, the correlation coefficient of soil water content and sand-raising wind speed is r1, and the difference between r3 and r1 corresponds to the importance degree, so the importance scale of vegetation coverage relative to soil water content is 3, and the importance scale of soil water content relative to vegetation coverage is .

[0058] According to the importance scale, a 3*3 order judgment matrix M is constructed, and the matrix form is as follows: .

[0059] wherein, represents the importance scale of soil water content relative to soil compaction degree, , represents the importance scale of soil water content relative to vegetation coverage, , represents the importance scale of soil compaction degree relative to vegetation coverage, .

[0060] Step three, compare the real-time wind speed collected in the to-be-measured area with the corrected sand-raising wind speed threshold to determine whether the to-be-measured area has a wind-sand flow risk.

[0061] It should be noted that the determination of whether the to-be-measured area has a wind-sand flow risk is specifically: screening the maximum real-time wind speed from the real-time wind speed monitored by each wind speed monitoring point deployed in the to-be-measured area, if the maximum real-time wind speed of the to-be-measured area is less than the corrected sand-raising wind speed threshold, then the to-be-measured area does not have a wind-sand flow risk, otherwise the to-be-measured area has a wind-sand flow risk. Thus, the risk judgment result can accurately adapt to the real-time environment of the to-be-measured area, avoiding misjudgment caused by environmental differences, and directly improving the accuracy of risk judgment.

[0062] Step four, when the to-be-measured area has a wind-sand flow risk, collect the ground sand layer height change data in the to-be-measured area, and determine the real-time wind-sand index by coupling analysis of the ground sand layer height change data and the real-time wind speed.

[0063] It should be noted that the real-time wind-sand index determination method is: dividing the to-be-measured area into a plurality of monitoring grids, collecting the ground sand layer height change and real-time wind speed through the sand layer height monitoring points and wind speed monitoring points arranged in each monitoring grid, and spatiotemporally aligning the ground sand layer height change and real-time wind speed. The ground sand layer height change is the ground sand layer height of the current period minus the ground sand layer height of the previous period.

[0064] The ground sand layer height change and real-time wind speed of each monitoring grid after spatiotemporal alignment are standardized, the standardized ground sand layer height change and real-time wind speed are substituted into the fitted wind-sand index linear regression equation, and the real-time wind-sand index is output.

[0065] The real-time sand-dust index of each monitoring grid is counted, and the maximum real-time sand-dust index is screened as the real-time sand-dust index of the region to be detected.

[0066] In the embodiment of the present application, the maximum real-time sand-dust index is screened by focusing on extreme risks, solving the monitoring blind area caused by the local difference of sand-dust flow, ensuring that the regional sand-dust risk assessment does not miss the key hazard points, and providing targeted guidance for the efficient allocation of prevention and control resources and the accurate formulation of measures.

[0067] As shown in Figure 3 In a specific embodiment, the fitting method of the sand-dust index linear regression equation is as follows: the surface sand layer height change amount, real-time wind speed and real-time sand-dust index in each sand-dust flow event record are obtained from the historical sand-dust monitoring log of the region to be detected, and a data set is constructed.

[0068] The data set is divided into a training set and a test set according to a set proportion, the surface sand layer height change amount and real-time wind speed of each sand-dust flow event record in the training set are taken as independent variables, the real-time sand-dust index is taken as a dependent variable, and the training set is substituted into a set linear regression equation to train, and the regression coefficients of the surface sand layer height change amount and real-time wind speed and the constant term are obtained by least square fitting, to construct an initial sand-dust index linear regression equation.

[0069] The independent variables of the test set are substituted into the initial sand-dust index linear regression equation for verification, and the accuracy of the real-time sand-dust index output by the verification is optimized to obtain the final sand-dust index linear regression equation.

[0070] The accuracy of the real-time sand-dust index output by the verification represents the proportion of samples whose deviation between the real-time sand-dust index of the equation verification and the actual real-time sand-dust index is within the allowable range.

[0071] When the accuracy is less than the set accuracy, the constant term of the initial sand-dust index linear regression equation is adjusted, the accuracy of the initial sand-dust index linear regression equation is reanalyzed, and the final sand-dust index linear regression equation is output when the accuracy is greater than the set accuracy.

[0072] The present application determines the quantitative real-time sand-dust index by coupling the surface sand layer height change data with the real-time wind speed through standardized processing and pre-fitted linear regression equation, provides clear data basis for sand-dust severity classification, enables the prevention and control personnel to intuitively grasp the sand-dust hazard degree, and then formulates differential prevention and control measures such as light cleaning and strengthened protection.

[0073] Step five, according to the real-time wind-sand index to determine the current wind-sand severity level, and trend analysis of the real-time wind-sand index and the recent historical wind-sand index to determine the wind-sand flow change trend of the to-be-measured region.

[0074] It should be noted that the determination of the wind-sand flow change trend of the to-be-measured region specifically includes: matching the real-time wind-sand index of the to-be-measured region with the preset wind-sand index range corresponding to each wind-sand severity level to determine the current wind-sand severity level, providing a basis for differentiated prevention and control measures, improving the efficiency and accuracy of prevention and control, ensuring the accurate investment of prevention and control resources in high-demand areas, and improving the efficiency of resource utilization.

[0075] Extracting the wind-sand index change sequence of the to-be-measured region in the recent continuous time period, and integrating it with the real-time wind-sand index to form a real-time wind-sand index time sequence.

[0076] The real-time wind-sand index time sequence is subjected to smooth curve fitting processing to obtain the slope of the fitted wind-sand index change curve, and the absolute value and numerical direction of the slope are used to determine the wind-sand flow change trend of the to-be-measured region, to gain key preparation time for prevention and control work, to provide data support for subsequent optimization of prevention and control strategies and review of prevention and control effects, and to improve the foresight and accuracy of wind-sand disaster prevention and control.

[0077] In one specific embodiment, if the numerical direction of the slope is positive and the absolute value is greater than a preset slope threshold, it is determined that the wind-sand flow trend is an enhancement trend, and the prevention and control facilities can be reinforced in advance, the prevention and control materials can be reserved, and the personnel and equipment transfer plan can be developed; if the numerical direction of the slope is negative and the absolute value is greater than a preset slope threshold, it is determined that the wind-sand flow trend is a weakening trend, and the prevention and control resource input intensity can be adjusted to avoid waste; if the absolute value of the change slope is less than or equal to a preset slope threshold, it is determined that the wind-sand flow trend is a stable trend, and regular monitoring and protection can be maintained.

[0078] The present application integrates the wind-sand index change sequence of the to-be-measured region in the recent continuous time period with the real-time wind-sand index to form a real-time wind-sand index time sequence, and calculates the slope by smooth curve fitting to determine the change trend, thereby being able to predict in advance the enhancement, weakening or stable trend of the wind-sand flow, changing the prevention and control work from passive response to disaster to active prevention in advance, gaining time for reinforcing prevention and control facilities and reserving prevention and control materials, and effectively reducing the loss of wind-sand disasters.

[0079] On the other hand, as shown in Figure 4 A wind-sand flow real-time monitoring system under regional environmental characteristics includes an environmental characteristic acquisition module, a sand-raising wind speed correction module, a wind-sand flow risk judgment module, a real-time wind speed index analysis module, and a change trend judgment module.

[0080] The connection relationship between the modules is that the environment feature acquisition module is in communication connection with the sand-raising wind speed correction module, the sand flow risk judgment module is in communication connection with the sand-raising wind speed correction module and the real-time wind speed index analysis module respectively, and the change trend judgment module is in communication connection with the real-time wind speed index analysis module.

[0081] The environment feature acquisition module acquires real-time environment feature parameters of the to-be-measured region in real time.

[0082] The sand-raising wind speed correction module determines an initial sand-raising wind speed threshold value according to the real-time wind direction collected in the to-be-measured region and in combination with a historical wind-sand monitoring log of the to-be-measured region, dynamically corrects the initial sand-raising wind speed threshold value based on the real-time environment feature parameters, and obtains a corrected sand-raising wind speed threshold value.

[0083] The sand flow risk judgment module compares the real-time wind speed collected in the to-be-measured region with the corrected sand-raising wind speed threshold value, and judges whether the to-be-measured region has a sand flow risk.

[0084] The real-time wind speed index analysis module collects surface sand layer height change data in the to-be-measured region when the to-be-measured region has a sand flow risk, and determines a real-time sand index by coupling analysis of the surface sand layer height change data and the real-time wind speed.

[0085] The change trend judgment module determines a current sand severity grade according to the real-time sand index, and judges a sand flow change trend of the to-be-measured region by trend analysis of the real-time sand index and a recent historical sand index.

[0086] The above formulas are all dimensionless values, and the formulas are obtained by software simulation of a large amount of data to obtain a formula of a nearest real situation, and preset parameters in the formula are set by a person skilled in the art according to actual conditions.

[0087] The above embodiments can be realized by software, hardware, firmware or any combination thereof, in whole or in part. When realized by software, the above embodiments can be realized in the form of a computer program product in whole or in part.

[0088] Those skilled in the art can realize that the modules and algorithm steps of the examples described in combination with the embodiments disclosed herein can be realized by electronic hardware or a combination of computer software and electronic hardware. Whether the functions are realized by hardware or software depends on the specific application and design constraints of the technical solutions. A person skilled in the art can use different methods to realize the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0089] In addition, each function module in each embodiment of the present application can be integrated in one processing module, or each module can be physically present alone, or two or more modules can be integrated in one module.

[0090] The above is merely specific embodiments of the present application, but the protection scope of the present application is not limited thereto, any person skilled in the art can easily think of changes or replacements within the technical range disclosed by the present application, which should be covered in the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

[0091] Finally, the above is merely preferred embodiments of the present application, and is not used to limit the present application, any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be covered in the protection scope of the present application.

Claims

1. A real-time monitoring method for wind-sand flow under regional environmental characteristics, characterized in that, The method comprises the following steps: Real-time environmental characteristic parameters of the to-be-measured region are collected in real time, wherein the real-time environmental characteristic parameters include soil water content, soil compaction degree and vegetation coverage; An initial sand-raising wind speed threshold is determined according to the real-time wind direction collected in the to-be-measured region and historical wind-sand monitoring logs of the to-be-measured region, and the initial sand-raising wind speed threshold is dynamically corrected based on the real-time environmental characteristic parameters to obtain a corrected sand-raising wind speed threshold; The real-time wind speed collected in the to-be-measured region is compared with the corrected sand-raising wind speed threshold to determine whether the to-be-measured region has a wind-sand flow risk; When the to-be-measured region has a wind-sand flow risk, the height change data of the ground sand layer in the to-be-measured region are collected, and the height change data of the ground sand layer are coupled with the real-time wind speed to determine a real-time wind-sand index; The current wind-sand severity level is determined according to the real-time wind-sand index, and a trend analysis is performed on the real-time wind-sand index and the recent historical wind-sand index to determine the wind-sand flow trend of the to-be-measured region.

2. The method according to claim 1, wherein: The real-time environmental characteristic parameters of the to-be-measured region are collected in the following manner: A plurality of monitoring sub-points are arranged in the to-be-measured region according to a uniform grid, and soil water content sensors and soil compaction degree sensors are arranged at each monitoring sub-point to collect the soil water content and soil compaction degree in a preset depth range below the soil surface, respectively; The soil water content and soil compaction degree collected by each monitoring sub-point are subjected to data mean processing to obtain the soil water content and soil compaction degree; High-definition remote sensing images of the to-be-measured region are obtained by using optical remote sensing equipment, the vegetation coverage area in the high-definition remote sensing images is identified, and the pixel proportion of the vegetation coverage is determined as the vegetation coverage of the to-be-measured region.

3. The method according to claim 1, wherein: The initial sand-raising wind speed threshold is determined in the following manner: According to the real-time wind direction collected in the to-be-measured region, historical monitoring logs with the same real-time wind direction are retrieved from the historical wind-sand monitoring logs of the to-be-measured region; The sand-raising wind speeds recorded in the retrieved historical monitoring logs are subjected to dispersion degree evaluation, and if the dispersion degree is less than a set dispersion degree, the average value of the sand-raising wind speeds in the historical monitoring logs is taken as the initial sand-raising wind speed threshold; Otherwise, the maximum sand-raising wind speed and the minimum sand-raising wind speed corresponding to each historical monitoring log are removed, and the sand-raising wind speeds in the remaining historical monitoring logs are subjected to dispersion degree evaluation again until the initial sand-raising wind speed threshold is determined.

4. The method according to claim 3, wherein: The corrected sand-raising wind speed threshold is obtained in the following manner: The historical monitoring log with the sand-raising wind speed closest to the initial sand-raising wind speed threshold is selected from the retrieved historical monitoring logs, and the historical environmental characteristic parameters recorded in the historical monitoring log are taken as reference environmental characteristic parameters; Deviation analysis is performed on the real-time environmental characteristic parameters and the reference environmental characteristic parameters to obtain the soil water content deviation rate, the soil compaction degree deviation rate and the vegetation coverage deviation rate, respectively; Based on the correlation analysis of the soil water content, the soil compaction degree, the vegetation coverage and the sand-raising wind speed in the retrieved historical monitoring logs, the influence weight of the soil water content, the soil compaction degree and the vegetation coverage on the sand-raising wind speed is determined by using the analytic hierarchy process; The soil water content deviation rate, the soil compaction degree deviation rate and the vegetation coverage deviation rate are multiplied by the corresponding influence weight, and the sum is taken as the ground wind resistance deviation coefficient. The initial threshold of sand-raising wind speed is corrected by a correction formula based on the threshold and the deviation coefficient of the ground surface.

5. The method according to claim 4, wherein: The weight determination method of the effects of the soil moisture content, the soil compactness, and the vegetation coverage on the sand-raising wind speed is as follows: According to the soil moisture content, the soil compactness, and the vegetation coverage in each historical monitoring log, the correlation coefficient method is used to calculate the correlation coefficients of the soil moisture content and the sand-raising wind speed, the soil compactness and the sand-raising wind speed, and the vegetation coverage and the sand-raising wind speed. The importance scale of the soil moisture content, the soil compactness, and the vegetation coverage is determined by using the scale method of the analytic hierarchy process and the correlation coefficients. The importance scale of the soil moisture content, the soil compactness, and the vegetation coverage is determined by using the scale method of the analytic hierarchy process and the correlation coefficients.

6. The method of claim 1, wherein the method is characterized by: The importance scale of the soil moisture content, the soil compactness, and the vegetation coverage is determined by using the scale method of the analytic hierarchy process and the correlation coefficients. The judgment matrix is constructed according to all the importance scales, and the weight of the effects of the soil moisture content, the soil compactness, and the vegetation coverage on the sand-raising wind speed is obtained by using the sum product method.

7. The method of claim 1, wherein the method is characterized by: The judgment method of whether the to-be-measured region has a wind-sand flow risk is as follows: The maximum real-time wind speed is selected from the real-time wind speeds monitored by the wind speed monitoring points arranged in the to-be-measured region, and if the maximum real-time wind speed is less than the corrected threshold of the sand-raising wind speed, the to-be-measured region does not have a wind-sand flow risk, otherwise, the to-be-measured region has a wind-sand flow risk. The real-time wind-sand index determination method is as follows: The to-be-measured region is divided into a plurality of monitoring grids, the sand layer height variation and the real-time wind speed are collected by the sand layer height monitoring points and the wind speed monitoring points arranged in each monitoring grid, and the sand layer height variation and the real-time wind speed are spatio-temporally aligned.

8. The method according to claim 7, wherein the method is characterized by: The sand layer height variation and the real-time wind speed of each monitoring grid after spatio-temporal alignment are standardized, the standardized sand layer height variation and real-time wind speed are substituted into the fitted wind-sand index linear regression equation, and the real-time wind-sand index is output. The real-time wind-sand index of each monitoring grid is counted, and the maximum real-time wind-sand index is selected as the real-time wind-sand index. The fitting method of the wind-sand index linear regression equation is as follows: The wind-sand flow event records are selected from the historical wind-sand monitoring logs of the to-be-measured region, the sand layer height variation, the real-time wind speed, and the real-time wind-sand index in each wind-sand flow event record are obtained, and a data set is constructed.

9. The method of claim 1, wherein the method is characterized by: The data set is divided into a training set and a test set according to a set proportion, the sand layer height variation and the real-time wind speed of each wind-sand flow event record in the training set are used as independent variables, the real-time wind-sand index is used as a dependent variable, and the independent variables are substituted into a set linear regression equation for training, the regression coefficients of the sand layer height variation and the real-time wind speed and the constant term are obtained by using the least square method fitting, and an initial wind-sand index linear regression equation is constructed. The independent variables of the test set are substituted into the initial wind-sand index linear regression equation for verification, the accuracy of the real-time wind-sand index output by the verification is optimized, and a final wind-sand index linear regression equation is obtained. The judgment method of the wind-sand flow change trend of the to-be-measured region is as follows: The real-time wind-sand index is matched with the wind-sand index range corresponding to each wind-sand severity level to determine the current wind-sand severity level. The wind-sand index change sequence in the recent continuous time period corresponding to the to-be-measured region is extracted, integrated with the real-time wind-sand index to form a real-time wind-sand index time sequence, and the wind-sand flow change trend of the to-be-measured region is determined according to the real-time wind-sand index time sequence. The real-time wind-sand index time sequence is subjected to smooth curve fitting processing to obtain the slope of the fitted wind-sand index change curve, and the wind-sand flow change trend of the to-be-measured region is judged according to the absolute value and numerical direction of the slope.

10. A real-time monitoring system for wind-blown sand flow under regional environmental characteristics, characterized in that: Comprise: An environmental feature acquisition module, which acquires real-time environmental feature parameters of a to-be-measured region in real time; A sand-raising wind speed correction module, which determines an initial sand-raising wind speed threshold according to the real-time wind direction collected in the to-be-measured region, combines historical wind-sand monitoring logs of the to-be-measured region, dynamically corrects the initial sand-raising wind speed threshold based on the real-time environmental feature parameters, and obtains a corrected sand-raising wind speed threshold; A wind-sand flow risk judgment module, which compares the real-time wind speed collected in the to-be-measured region with the corrected sand-raising wind speed threshold to judge whether the to-be-measured region has a wind-sand flow risk; A real-time wind speed index analysis module, which, when the to-be-measured region has a wind-sand flow risk, collects surface sand layer height change data in the to-be-measured region, couples and analyzes the surface sand layer height change data and the real-time wind speed to determine a real-time wind-sand index; A change trend judgment module, which determines a current wind-sand severity level according to the real-time wind-sand index, and performs trend analysis on the real-time wind-sand index and recent historical wind-sand indexes to judge the wind-sand flow change trend of the to-be-measured region.

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