Intelligent wind and snow flow monitoring system based on artificial intelligence

Abstract

The invention relates to the technical field of wind and snow flow monitoring, in particular to a wind and snow flow intelligent monitoring system based on artificial intelligence, which comprises a server, a wind and snow supervision analysis unit, an aging analysis unit, an early warning management unit, a risk assessment unit and an efficiency analysis unit, according to the invention, environmental data of a road are collected and wind and snow hazards are evaluated and analyzed, on one hand, a supervision effect on the wind and snow flow is realized, and on the other hand, the hazard condition of the wind and snow flow is conveniently judged, so that early warning is timely carried out, the hazard influence degree of the wind and snow is reduced, and the supervision and early warning effect on the wind and snow flow is improved; meanwhile, the transmission data of the monitoring points are collected, aging influence assessment analysis is carried out, and whether aging abnormity exists in data transmission of the monitoring points or not is judged, so that the stability and the timeliness of data transmission are improved, that is, deep analysis is carried out on the collection equipment and the collection object, and the accuracy and the effectiveness of an assessment analysis result are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of wind and snow flow monitoring, and in particular to an intelligent wind and snow flow monitoring system based on artificial intelligence. Background Art

[0002] Blowing snow refers to the natural phenomenon of strong winds carrying snow, also known as wind and snow flow. It is a relatively complex special fluid with great harm. Blowing snow often occurs in snowy areas with high latitudes, high altitudes and large terrain fluctuations. Blowing snow not only causes visibility obstruction, but also causes serious snow disasters. It has long been a problem that has plagued highway transportation in blowing snow areas.

[0003] However, the wind and snow flow monitoring methods in the existing technology cannot accurately monitor the wind and snow flow, the monitoring efficiency is low, and there are delays in the transmission of monitoring data, which in turn causes large errors in the monitoring data analysis results. In addition, the existing delays cannot be accurately managed according to the delay level, and the efficiency of the detection points cannot be deeply monitored, which affects the effectiveness of the analysis data, resulting in poor warning effects, large data errors and low supervision efficiency. Summary of the invention

[0004] The purpose of the present invention is to provide an intelligent monitoring system for wind and snow flows based on artificial intelligence to solve the technical defects mentioned above. It collects environmental data of highways and conducts wind and snow hazard assessment and analysis. On the one hand, it realizes the supervision effect of wind and snow flows, and on the other hand, it is convenient to judge the hazard of wind and snow flows, so as to make timely warnings, reduce the degree of harmful impact of wind and snow, and improve the supervision and warning effect of wind and snow flows. At the same time, the transmission data of the monitoring point is collected and the timeliness impact assessment and analysis is carried out to judge whether there is timeliness anomaly in the data transmission of the monitoring point, so as to improve the stability and timeliness of data transmission. That is, by conducting in-depth analysis from the two perspectives of collection equipment and collection objects, it helps to improve the accuracy and effectiveness of the evaluation and analysis results.

[0005] The object of the present invention can be achieved by the following technical solutions: an artificial intelligence-based wind and snow flow intelligent monitoring system, comprising a server, a wind and snow supervision analysis unit, a time efficiency analysis unit, an early warning management unit, a risk assessment unit and an efficiency analysis unit;

[0006] When the server generates an operation management instruction, it sends the operation management instruction to the wind and snow supervision and analysis unit and the time analysis unit. After receiving the operation management instruction, the wind and snow supervision and analysis unit immediately collects environmental data of the highway, including environmental wind speed, snow particle content per unit volume, and snow particle characteristic image, and performs wind and snow hazard assessment and analysis on the environmental data to obtain risk signals and normal signals, and sends the obtained risk signals to the early warning management unit;

[0007] After receiving the operation management instruction, the timeliness analysis unit immediately collects the transmission data of the monitoring point. The transmission data includes data missing values and delay risk values, and conducts a timeliness impact assessment and analysis on the transmission data. The obtained timeliness signals are sent to the early warning management unit through the efficiency analysis unit, and the obtained failure signals are sent to the early warning management unit and the risk assessment unit through the efficiency analysis unit;

[0008] After receiving the failure signal, the risk assessment unit immediately conducts an in-depth analysis of the transmission data. The obtained primary impact signal, secondary impact signal, and tertiary impact signal are sent to the early warning management unit through the efficiency analysis unit. After receiving the primary impact signal, secondary impact signal, and tertiary impact signal, the early warning management unit immediately performs the corresponding early warning operations for the primary impact signal, secondary impact signal, and tertiary impact signal;

[0009] After receiving the timeliness signal, the efficiency analysis unit immediately collects the working data of the monitoring point. The working data includes the data transmission times and operation duration, and conducts an efficiency assessment and analysis operation on the working data. The obtained maintenance signal and replacement signal are sent to the early warning management unit.

[0010] Preferably, the snowstorm hazard assessment and analysis process of the snowstorm supervision and analysis unit is as follows:

[0011] SS1: Mark the highway as the analysis area. There are monitoring points set in the analysis area. The duration from the start operation time to the end operation time of the monitoring point is collected and marked as the time threshold. The time threshold is divided into i sub-time periods, where i is a natural number greater than zero. The environmental wind speed, snow particle content per unit volume, and snow particle characteristic image in each sub-time period are obtained, and the snow particle characteristic image is analyzed and processed to obtain the snow instantaneous flux value from it. The environmental wind speed, snow particle content per unit volume, and snow instantaneous flux value are respectively labeled as HFi, DXi, and XSi;

[0012] SS12: Obtain according to the formula Obtain the snowstorm hazard assessment coefficient, where a1, a2, and a4 are respectively the preset proportional factor coefficients of the environmental wind speed, the snow particle content per unit volume, and the instantaneous snow flux value. a1, a2, and a4 are all positive numbers greater than zero. a3 is the preset compensation factor coefficient with a value of 2.432. Pi is the snowstorm hazard assessment coefficient. Establish a rectangular coordinate system with time as the X-axis and the snowstorm hazard assessment coefficient Pi as the Y-axis. Draw the snowstorm hazard assessment coefficient curve by means of anchor points. Obtain the sum of the differences between the two endpoints of the rising segment from the snowstorm hazard assessment coefficient curve and mark it as the growth trend value. Obtain the sum of the differences between the two endpoints of the falling segment from the snowstorm hazard assessment coefficient curve and mark it as the decline trend value. Then mark the ratio of the growth trend value to the decline trend value as the risk trend value and compare and analyze the risk trend value with the preset risk trend value threshold stored in it:

[0013] If the risk trend value is greater than the preset risk trend value threshold, generate a risk signal;

[0014] If the risk trend value is less than or equal to the preset risk trend value threshold, generate a normal signal.

[0015] Preferably, when the snowstorm supervision and analysis unit obtains a normal signal, immediately collect the average snow depth value of the analysis area in each sub-time period, thereby obtaining the snow accumulation value per unit time of the analysis area in each sub-time period, and compare and analyze the snow accumulation value per unit time with the preset snow accumulation value threshold per unit time. If the snow accumulation value per unit time is greater than the preset snow accumulation value threshold per unit time, mark the number of sub-time periods corresponding to the snow accumulation value per unit time being greater than the preset snow accumulation value threshold per unit time as the hazard time period value, and compare and analyze the hazard time period value with the preset hazard time period value threshold stored in it:

[0016] If the hazard time period value is less than or equal to the preset hazard time period value threshold, do not generate any signal;

[0017] If the hazard time period value is greater than the preset hazard time period value threshold, generate a hazard signal. When obtaining the hazard signal and the normal signal, obtain the influence signal and send the influence signal to the early warning management unit.

[0018] Preferably, the time effect impact assessment analysis process of the time effect analysis unit is as follows:

[0019] Step 1: Obtain the historical transmission record list of the monitoring point. From the historical transmission record list, obtain the transmission duration, reception duration, and operation duration of the monitoring point. The transmission duration refers to the duration between the start transmission moment of the monitoring point and the start reception moment of the receiver. The reception duration refers to the duration between the start reception moment and the end reception moment of the receiver. The operation duration refers to the duration between the start transmission moment of the monitoring point and the end reception moment of the receiver. Then, compare and analyze the transmission duration, reception duration, and operation duration with the preset transmission duration threshold, preset reception duration threshold, and preset operation duration threshold respectively. Thus, obtain the sum of the parts of the transmission duration, reception duration, and operation duration that are greater than the preset transmission duration threshold, preset reception duration threshold, and preset operation duration threshold, and mark it as the delay risk value;

[0020] Step 2: Divide the operation duration into o sub-time periods, where o is a natural number greater than zero. Obtain the data missing value of the receiver within each sub-time period. The data missing value refers to the ratio of the number of lost data during transmission to the total number of data transmissions in this sub-time period. Then, compare and analyze the data missing value with the preset data missing value threshold. If the data missing value is greater than the preset data missing value threshold, mark the sub-time period corresponding to the data missing value greater than the preset data missing value threshold as the data missing segment. Obtain the ratio of the data missing segment to the total sub-time periods, and mark it as the missing risk ratio. Then, compare and analyze the delay risk value and the missing risk ratio with the preset delay risk value threshold and preset missing risk ratio threshold stored in the internal record:

[0021] If the delay risk value is less than or equal to the preset delay risk value threshold, and the missing risk ratio is less than or equal to the preset missing risk ratio threshold, generate a timeliness signal;

[0022] If the delay risk value is greater than the preset delay risk value threshold, or the missing risk ratio is greater than the preset missing risk ratio threshold, generate a failure signal.

[0023] Preferably, the in-depth analysis process of the transmitted data of the risk assessment unit is as follows:

[0024] Step 1: Retrieve the delay risk value and the missing risk ratio from the timeliness analysis unit. Thus, obtain the parts of the delay risk value and the missing risk ratio that exceed the preset delay risk value threshold and preset missing risk ratio threshold respectively, and mark the parts of the delay risk value and the missing risk ratio that exceed the preset delay risk value threshold and preset missing risk ratio threshold as the delay impact value YW and the missing impact value QS respectively;

[0025] Step 2: Obtain the mean value of the snow and wind hazard assessment coefficients corresponding to the operation duration, and mark it as the average snow and wind hazard assessment coefficient PF. Obtain the delay impact risk coefficient W according to the formula, and compare and analyze the delay impact risk coefficient W with the preset delay impact risk coefficient range stored in it:

[0026] If the delay impact risk coefficient W is greater than the maximum value in the preset delay impact risk coefficient range, generate a first-level impact signal; if the delay impact risk coefficient W is within the preset delay impact risk coefficient range, generate a second-level impact signal; if the delay impact risk coefficient W is less than the minimum value in the preset delay impact risk coefficient range, generate a third-level impact signal.

[0027] Preferably, the efficiency evaluation and analysis operation process of the efficiency analysis unit is as follows:

[0028] S1: Obtain the number of data transmissions within the time threshold, and then obtain the number of transmission repetitions from the number of data transmissions. The number of transmission repetitions refers to the situation where data is missing in the first transmission and is transmitted again the second time, repeating once in this way, and so on. Mark the ratio of the number of transmission repetitions to the number of data transmissions as the transmission risk ratio CB;

[0029] S12: Obtain the operation duration within the time threshold, and compare and analyze the operation duration with the preset operation duration threshold. If the operation duration is greater than the preset operation duration threshold, mark the part where the operation duration is greater than the preset operation duration threshold as the delay value, and then mark the ratio of the delay value to the operation duration as the duration risk ratio SB;

[0030] S13: Obtain the efficiency evaluation coefficient G according to the formula, and compare and analyze the efficiency evaluation coefficient G with the preset efficiency evaluation coefficient threshold stored in it:

[0031] If the efficiency evaluation coefficient G is less than or equal to the preset efficiency evaluation coefficient threshold, do not generate any signal;

[0032] If the efficiency evaluation coefficient G is greater than the preset efficiency evaluation coefficient threshold, generate a supervision signal;

[0033] S14: After generating the supervision signal, immediately obtain the part where the efficiency evaluation coefficient G is greater than the preset efficiency evaluation coefficient threshold, and mark it as the risk efficiency value, and compare and analyze the risk efficiency value with the preset risk efficiency value threshold stored in it:

[0034] If the risk efficiency value is less than or equal to the preset risk efficiency value threshold, generate a maintenance signal;

[0035] If the risk efficiency value is greater than the preset risk efficiency value threshold, generate a replacement signal.

[0036] The beneficial effects of the present invention are as follows:

[0037] (1) The present invention collects environmental data of highways and conducts assessment and analysis of snow and wind hazards. On the one hand, it realizes the supervision effect of snow and wind currents. On the other hand, it facilitates the judgment of the hazard situation of snow and wind currents, so as to give early warnings in a timely manner, reduce the harmful impact of snow and wind, improve the supervision and early warning effect of snow and wind currents. At the same time, it collects the transmission data of monitoring points and conducts assessment and analysis of timeliness impact to judge whether there are timeliness abnormalities in the data transmission of monitoring points, so as to improve the stability and timeliness of data transmission. That is, through in-depth analysis from two perspectives of the acquisition device and the acquisition object, it helps to improve the accuracy and effectiveness of the assessment and analysis results;

[0038] (2) The present invention also conducts in-depth analysis of the transmission data through the way of data feedback to judge the risk level of the delay impact of the transmission data, so as to manage the monitoring points reasonably and timely, further improve the stability and timeliness of data transmission, reduce the impact of data on the analysis results. At the same time, it collects the working data of monitoring points and conducts efficiency assessment and analysis operations to maintain and replace the monitoring points with too low efficiency, so as to manage the monitoring points reasonably, and at the same time helps to improve the efficiency of snow and wind current supervision. Description of the Drawings

[0039] The present invention will be further described below with reference to the drawings;

[0040] Figure 1 is the system flow block diagram of the present invention;

[0041] Figure 2 is the partial analysis diagram in Embodiment 1 of the present invention. Detailed Embodiment

[0042] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0043] Embodiment 1:

[0044] Please refer to Figures 1 to 2As shown in the figure, the present invention is an intelligent monitoring system for snow and wind flow based on artificial intelligence, including a server, a snow and wind supervision and analysis unit, a timeliness analysis unit, a warning management unit, a risk assessment unit, and an efficiency analysis unit. The server is unidirectionally communicatively connected to both the snow and wind supervision and analysis unit and the timeliness analysis unit. The snow and wind supervision and analysis unit is unidirectionally communicatively connected to the warning management unit. The timeliness analysis unit is unidirectionally communicatively connected to both the risk assessment unit and the efficiency analysis unit. The risk assessment unit is bidirectionally communicatively connected to the efficiency analysis unit. The efficiency analysis unit is unidirectionally communicatively connected to the warning management unit;

[0045] When the server generates an operation management instruction and sends the operation management instruction to the snow and wind supervision and analysis unit and the timeliness analysis unit, after receiving the operation management instruction, the snow and wind supervision and analysis unit immediately collects the environmental data of the road. The environmental data includes environmental wind speed, the snow particle content per unit volume, and the snow particle characteristic image, and conducts a snow and wind hazard assessment and analysis on the environmental data to determine whether there is a hazard in the snow and wind flow, so as to give a timely warning. The specific process of the snow and wind hazard assessment and analysis is as follows:

[0046] Mark the road as the analysis area. There are monitoring points set in the analysis area. Collect the time duration from the start operation time to the end operation time of the monitoring points and mark it as the time threshold. Divide the time threshold into i sub-time periods, where i is a natural number greater than zero. Obtain the environmental wind speed, the snow particle content per unit volume, and the snow particle characteristic image in the analysis area within each sub-time period, and analyze and process the snow particle characteristic image to obtain the snow instantaneous flux value. Label the environmental wind speed, the snow particle content per unit volume, and the snow instantaneous flux value as HFi, DXi, and XSi respectively;

[0047] Obtain according to the formula Get the snow and wind hazard assessment coefficient. Among them, a1, a2, and a4 are the preset proportional factor coefficients of the environmental wind speed, the snow particle content per unit volume, and the snow instantaneous flux value respectively. The proportional factor coefficients are used to correct the deviations that occur in the formula calculation process of each parameter, so as to make the calculation result more accurate. a1, a2, and a4 are all positive numbers greater than zero. a3 is the preset compensation factor coefficient, with a value of 2.432. Pi is the snow and wind hazard assessment coefficient. Establish a rectangular coordinate system with time as the X-axis and the snow and wind hazard assessment coefficient Pi as the Y-axis. Draw the snow and wind hazard assessment coefficient curve by the way of anchor points. Obtain the sum of the differences between the two endpoints of the rising section from the snow and wind hazard assessment coefficient curve and mark it as the growth trend value. Obtain the sum of the differences between the two endpoints of the falling section from the snow and wind hazard assessment coefficient curve and mark it as the decline trend value. Then mark the ratio of the growth trend value to the decline trend value as the risk trend value, and compare and analyze the risk trend value with the preset risk trend value threshold stored in it:

[0048] If the risk trend value is greater than the preset risk trend value threshold, a risk signal is generated and sent to the early warning management unit. After receiving the risk signal, the early warning management unit immediately displays the warning text corresponding to the risk signal to promptly formulate a response plan and reduce the harmful impact of the snowstorm;

[0049] If the risk trend value is less than or equal to the preset risk trend value threshold, a normal signal is generated. When the normal signal is generated, the average snow depth value in the analysis area for each sub - time period is immediately collected to obtain the snow accumulation value per unit time in the analysis area for each sub - time period. Then, the snow accumulation value per unit time is compared and analyzed with the preset snow accumulation value threshold per unit time. If the snow accumulation value per unit time is greater than the preset snow accumulation value threshold per unit time, the number and label of the sub - time periods corresponding to the snow accumulation value per unit time being greater than the preset snow accumulation value threshold per unit time are marked as the hazard period value, and the hazard period value is compared and analyzed with the preset hazard period value threshold stored in it:

[0050] If the hazard period value is less than or equal to the preset hazard period value threshold, no signal is generated;

[0051] If the hazard period value is greater than the preset hazard period value threshold, a hazard signal is generated. When the hazard signal and the normal signal are obtained, an impact signal is obtained and sent to the early warning management unit. After receiving the impact signal, the early warning management unit immediately displays the warning text corresponding to the impact signal to reduce the harmful impact of the snowstorm and improve the supervision and early warning effect on the snowstorm flow;

[0052] After receiving the operation management instruction, the timeliness analysis unit immediately collects the transmission data of the monitoring point. The transmission data includes data missing values and delay risk values, and conducts a timeliness impact assessment and analysis on the transmission data to determine whether there is a timeliness anomaly in the data transmission of the monitoring point, so as to improve the stability and timeliness of data transmission. The specific timeliness impact assessment and analysis process is as follows:

[0053] Obtain the historical transmission record list of the monitoring point. From the historical transmission record list, obtain the transmission duration, reception duration, and operation duration of the monitoring point. The transmission duration refers to the duration between the start time of transmission of the monitoring point and the start time of reception by the receiver. The reception duration refers to the duration between the start time of reception by the receiver and the end time of reception. The operation duration refers to the duration between the start time of transmission of the monitoring point and the end time of reception by the receiver. Then, compare and analyze the transmission duration, reception duration, and operation duration with the preset transmission duration threshold, preset reception duration threshold, and preset operation duration threshold respectively. Thus, obtain the sum of the parts of the transmission duration, reception duration, and operation duration that are greater than the preset transmission duration threshold, preset reception duration threshold, and preset operation duration threshold, and mark it as the delay risk value. It should be noted that the larger the value of the delay risk value, the greater the impact on the timeliness of data transmission;

[0054] Divide the operation duration into o sub-time periods, where o is a natural number greater than zero. Obtain the data missing value of the receiver within each sub-time period. The data missing value refers to the ratio of the number of lost data during transmission to the total number of data transmissions in this sub-time period. Then, compare and analyze the data missing value with the preset data missing value threshold. If the data missing value is greater than the preset data missing value threshold, mark the sub-time period corresponding to the data missing value greater than the preset data missing value threshold as the data missing segment. Obtain the ratio of the data missing segment to the total number of sub-time periods, and mark it as the missing risk ratio. Then, compare and analyze the delay risk value and the missing risk ratio with the preset delay risk value threshold and preset missing risk ratio threshold stored internally:

[0055] If the delay risk value is less than or equal to the preset delay risk value threshold, and the missing risk ratio is less than or equal to the preset missing risk ratio threshold, generate a timeliness signal. Send the timeliness signal to the warning management unit through the efficiency analysis unit. After receiving the timeliness signal, the warning management unit immediately displays the warning text corresponding to the timeliness signal, so as to intuitively understand the stability and timeliness of data transmission;

[0056] If the delay risk value is greater than the preset delay risk value threshold, or the missing risk ratio is greater than the preset missing risk ratio threshold, generate a failure signal, and send the failure signal to the warning management unit and the risk assessment unit through the efficiency analysis unit. After receiving the failure signal, the warning management unit immediately displays the warning text corresponding to the failure signal, so as to timely re-upload and manage the data and improve the stability and timeliness of data transmission.

[0057] Embodiment 2:

[0058] After receiving the failure signal, the risk assessment unit immediately conducts an in-depth analysis of the transmitted data to determine the risk level of the delay impact of the transmitted data, so as to manage the monitoring points in a timely and reasonable manner, improve the stability and timeliness of data transmission, reduce the impact of data on the analysis results, and improve the accuracy of the analysis results. The specific in-depth analysis process of the transmitted data is as follows:

[0059] Retrieve the delay risk value and the missing risk ratio from the timeliness analysis unit, and respectively obtain the parts of the delay risk value and the missing risk ratio that exceed the preset delay risk value threshold and the preset missing risk ratio threshold. Then, mark the parts of the delay risk value and the missing risk ratio that exceed the preset delay risk value threshold and the preset missing risk ratio threshold as the delay impact value and the missing impact value, labeled as YW and QS;

[0060] At the same time, obtain the mean value of the snow and wind hazard assessment coefficients corresponding to the operation duration, and mark it as the average snow and wind hazard assessment coefficient, labeled as PF;

[0061] According to the formula Obtain the delay impact risk coefficient. Among them, b1, b2, and b4 are the preset weight factor coefficients of the delay impact value, the missing impact value, and the average snow and wind hazard assessment coefficient respectively, b3 is the preset correction coefficient, with a value of 1.864, b1, b2, and b4 are all positive numbers greater than zero, W is the delay impact risk coefficient, and compare and analyze the delay impact risk coefficient W with the preset delay impact risk coefficient interval stored in its internal memory:

[0062] If the delay impact risk coefficient W is greater than the maximum value in the preset delay impact risk coefficient interval, the greater the impact caused by the data transmission delay, and a first-level impact signal is generated;

[0063] If the delay impact risk coefficient W is within the preset delay impact risk coefficient interval, a second-level impact signal is generated;

[0064] If the delay impact risk coefficient W is less than the minimum value in the preset delay impact risk coefficient interval, a third-level impact signal is generated. Among them, the impact degrees corresponding to the first-level impact signal, the second-level impact signal, and the third-level impact signal decrease in turn. Then, send the first-level impact signal, the second-level impact signal, and the third-level impact signal to the early warning management unit through the efficiency analysis unit. After receiving the first-level impact signal, the second-level impact signal, and the third-level impact signal, the early warning management unit immediately performs the corresponding early warning operations for the first-level impact signal, the second-level impact signal, and the third-level impact signal, so as to improve the stability and timeliness of data transmission, and further help improve the accuracy of the supervision and analysis results of the snow and wind flow;

[0065] After receiving the timeliness signal, the efficiency analysis unit immediately collects the working data of the monitoring point. The working data includes the number of data transmissions and the operation duration, and performs an efficiency evaluation and analysis operation on the working data to maintain the monitoring points with too low efficiency, improve the supervision effect and work efficiency. The specific process of the efficiency evaluation and analysis operation is as follows:

[0066] Obtain the number of data transmissions within the time threshold, and then obtain the number of transmission repetitions from the number of data transmissions. The number of transmission repetitions refers to the situation where data is missing in the first transmission and is transmitted again in the second transmission, and then it is repeated once, and so on. Mark the ratio of the number of transmission repetitions to the number of data transmissions as the transmission risk ratio, labeled as CB. It should be noted that the larger the value of the transmission risk ratio CB, the lower the efficiency of the monitoring point;

[0067] At the same time, obtain the operation duration within the time threshold, and compare and analyze the operation duration with the preset operation duration threshold. If the operation duration is greater than the preset operation duration threshold, mark the part where the operation duration is greater than the preset operation duration threshold as the delay value, and then mark the ratio of the delay value to the operation duration as the duration risk ratio, labeled as SB. The duration risk ratio is an influencing parameter reflecting the data transmission efficiency;

[0068] According to the formula Obtain the efficiency evaluation coefficient, where α and β are the preset proportionality coefficients of the transmission risk ratio and the duration risk ratio respectively, ε is the preset correction factor coefficient, α > β > ε0, G is the efficiency evaluation coefficient, and compare and analyze the efficiency evaluation coefficient G with the preset efficiency evaluation coefficient threshold stored in it:

[0069] If the efficiency evaluation coefficient G is less than or equal to the preset efficiency evaluation coefficient threshold, no signal is generated;

[0070] If the efficiency evaluation coefficient G is greater than the preset efficiency evaluation coefficient threshold, a supervision signal is generated. After the supervision signal is generated, immediately obtain the part where the efficiency evaluation coefficient G is greater than the preset efficiency evaluation coefficient threshold, and mark it as the risk efficiency value, and compare and analyze the risk efficiency value with the preset risk efficiency value threshold stored in it:

[0071] If the risk efficiency value is less than or equal to the preset risk efficiency value threshold, a maintenance signal is generated;

[0072] If the risk efficiency value is greater than the preset risk efficiency value threshold, a replacement signal is generated, and the maintenance signal and the replacement signal are sent to the early warning management unit. After receiving the maintenance signal and the replacement signal, the early warning management unit immediately displays the corresponding words "Maintenance" and "Replacement" of the maintenance signal and the replacement signal respectively, so as to intuitively reflect the operation efficiency of the monitoring point, facilitate the reasonable management of the monitoring point, improve the accuracy of data supervision, and at the same time help improve the supervision efficiency of the snow and wind flow.

[0073] In summary, the present invention collects the environmental data of the highway and conducts a snow and wind hazard assessment and analysis. On the one hand, it realizes the supervision effect of the snow and wind flow. On the other hand, it is convenient to judge the hazard situation of the snow and wind flow, so as to give an early warning in time, reduce the harm and influence degree of the snow and wind, and improve the supervision and early warning effect of the snow and wind flow. At the same time, it collects the transmission data of the monitoring point and conducts a timeliness impact assessment and analysis to judge whether there is a timeliness anomaly in the data transmission of the monitoring point, so as to improve the stability and timeliness of data transmission. That is, through in-depth analysis from two perspectives of the acquisition device and the acquisition object, it helps to improve the accuracy and effectiveness of the assessment and analysis results. In addition, through the way of data feedback, in-depth analysis of the transmission data is carried out to judge the risk level of the delay impact of the transmission data, so as to manage the monitoring point reasonably and timely, further improve the stability and timeliness of data transmission, reduce the influence degree of data on the analysis result, and at the same time collect the working data of the monitoring point and conduct an efficiency assessment and analysis operation to maintain and replace the monitoring point with too low efficiency, so as to manage the monitoring point reasonably and help improve the supervision efficiency of the snow and wind flow.

[0074] The setting of the size of the threshold is for the convenience of comparison. Regarding the size of the threshold, it depends on the amount of sample data and the number of base numbers set by those skilled in the art for each group of sample data; as long as it does not affect the proportional relationship between the parameter and the quantified value. The above formulas are all obtained by collecting a large amount of data for software simulation and selecting a formula close to the true value. The coefficients in the formula are set by those skilled in the art according to the actual situation. As mentioned above, only the preferred specific embodiments of the present invention are described, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention, according to the technical solution and inventive concept of the present invention, makes equivalent substitutions or changes, and should be covered by the protection scope of the present invention.

Claims

1. An intelligent monitoring system for snow and wind flow based on artificial intelligence, characterized in that, It includes a server, a snowstorm supervision and analysis unit, a timeliness analysis unit, a warning management unit, a risk assessment unit, and an efficiency analysis unit; When the server generates an operation management instruction and sends it to the snowstorm supervision and analysis unit and the timeliness analysis unit, after receiving the operation management instruction, the snowstorm supervision and analysis unit immediately collects the environmental data of the road. The environmental data includes environmental wind speed, snow particle content per unit volume, and snow particle characteristic images, and conducts a snowstorm hazard assessment and analysis on the environmental data to obtain a risk signal and a normal signal, and sends the obtained risk signal to the warning management unit; After receiving the operation management instruction, the timeliness analysis unit immediately collects the transmission data of the monitoring point. The transmission data includes data missing values and delay risk values, and conducts a timeliness impact assessment and analysis on the transmission data. The obtained timeliness signal is sent to the warning management unit through the efficiency analysis unit, and the obtained failure signal is sent to the warning management unit and the risk assessment unit through the efficiency analysis unit; After receiving the failure signal, the risk assessment unit immediately conducts an in-depth analysis of the transmission data, and sends the obtained primary impact signal, secondary impact signal, and tertiary impact signal to the warning management unit through the efficiency analysis unit. After receiving the primary impact signal, secondary impact signal, and tertiary impact signal, the warning management unit immediately performs the warning operations corresponding to the primary impact signal, secondary impact signal, and tertiary impact signal; After receiving the timeliness signal, the efficiency analysis unit immediately collects the working data of the monitoring point. The working data includes the number of data transmissions and the operation duration, and conducts an efficiency assessment and analysis operation on the working data, and sends the obtained maintenance signal and replacement signal to the warning management unit.

2. The intelligent monitoring system for snow and wind flow based on artificial intelligence according to claim 1, characterized in that, The process of the snowstorm hazard assessment and analysis of the snowstorm supervision and analysis unit is as follows: SS1: Mark the road as the analysis area. There are monitoring points set in the analysis area. The duration from the start operation time to the end operation time of the monitoring point is collected and marked as the time threshold. The time threshold is divided into i sub-time periods, where i is a natural number greater than zero. The environmental wind speed, snow particle content per unit volume, and snow particle characteristic images of the analysis area in each sub-time period are obtained, and the snow particle characteristic images are analyzed and processed to obtain the snow instantaneous flux value from them, and the environmental wind speed, snow particle content per unit volume, and snow instantaneous flux value are respectively labeled as HFi, DXi, and XSi; SS12: Obtained according to the formula to obtain the snow and wind hazard assessment coefficient, where a1, a2, and a4 are respectively the preset proportional factor coefficients of the environmental wind speed, the snow particle content per unit volume, and the snow instantaneous flux value. a1, a2, and a4 are all positive numbers greater than zero. a3 is the preset compensation factor coefficient, with a value of 2.

432. Pi is the snow and wind hazard assessment coefficient. A rectangular coordinate system is established with time as the X-axis and the snow and wind hazard assessment coefficient Pi as the Y-axis. The snow and wind hazard assessment coefficient curve is drawn by means of anchor points. The sum of the differences between the two endpoints of the rising segment is obtained from the snow and wind hazard assessment coefficient curve and marked as the growth trend value. The sum of the differences between the two endpoints of the falling segment is obtained from the snow and wind hazard assessment coefficient curve and marked as the decline trend value. Furthermore, the ratio of the growth trend value to the decline trend value is marked as the risk trend value, and the risk trend value is compared and analyzed with the preset risk trend value threshold stored in it: If the risk trend value is greater than the preset risk trend value threshold, a risk signal is generated; If the risk trend value is less than or equal to the preset risk trend value threshold, a normal signal is generated.

3. An intelligent monitoring system for wind and snow flow based on artificial intelligence according to claim 2, characterized in that, When the snowstorm supervision and analysis unit obtains a normal signal, it immediately collects the average snow depth value of the analysis area in each sub-time period to obtain the snow accumulation value per unit time of the analysis area in each sub-time period, and compares and analyzes the snow accumulation value per unit time with the preset snow accumulation value threshold per unit time. If the snow accumulation value per unit time is greater than the preset snow accumulation value threshold per unit time, the number and label of the sub-time periods corresponding to the snow accumulation value per unit time being greater than the preset snow accumulation value threshold per unit time are marked as the hazard period value, and the hazard period value is compared and analyzed with the preset hazard period value threshold stored in it: If the value of the hazard time period is less than or equal to the preset hazard time period value threshold, no signal is generated. If the value of the hazard time period is greater than the preset hazard time period value threshold, a hazard signal is generated. When the hazard signal and the normal signal are obtained, an impact signal is obtained and sent to the early warning management unit.

4. An intelligent monitoring system for wind and snow flow based on artificial intelligence according to claim 1, characterized in that, The time effect impact assessment and analysis process of the time effect analysis unit is as follows: Step 1: Obtain the historical transmission record list of the monitoring point. From the historical transmission record list, obtain the transmission duration, reception duration, and operation duration of the monitoring point. The transmission duration refers to the duration between the start time of transmission of the monitoring point and the start time of reception of the receiver. The reception duration refers to the duration between the start time of reception of the receiver and the end time of reception. The operation duration refers to the duration between the start time of transmission of the monitoring point and the end time of reception of the receiver. Then, compare and analyze the transmission duration, reception duration, and operation duration with the preset transmission duration threshold, preset reception duration threshold, and preset operation duration threshold respectively. Then, obtain the sum of the parts of the transmission duration, reception duration, and operation duration that are greater than the preset transmission duration threshold, preset reception duration threshold, and preset operation duration threshold, and mark it as the delay risk value. Step 2: Divide the operation duration into o sub-time periods, where o is a natural number greater than zero. Obtain the data missing value within each sub-time period. The data missing value refers to the ratio of the number of lost data during transmission to the total number of data transmissions in this sub-time period. Then, compare and analyze the data missing value with the preset data missing value threshold. If the data missing value is greater than the preset data missing value threshold, mark the sub-time period corresponding to the data missing value greater than the preset data missing value threshold as the data missing segment. Obtain the ratio of the data missing segment to the total sub-time periods and mark it as the missing risk ratio. Then, compare and analyze the delay risk value and the missing risk ratio with the preset delay risk value threshold and preset missing risk ratio threshold stored in its internal record: If the delay risk value is less than or equal to the preset delay risk value threshold and the missing risk ratio is less than or equal to the preset missing risk ratio threshold, a time effect signal is generated. If the delay risk value is greater than the preset delay risk value threshold or the missing risk ratio is greater than the preset missing risk ratio threshold, a failure signal is generated.

5. The intelligent monitoring system for snow - wind flow based on artificial intelligence according to claim 1, characterized in that, The in-depth analysis process of the transmitted data of the risk assessment unit is as follows: Step 1: Retrieve the delay risk value and the missing risk ratio from the time effect analysis unit. Thus, obtain the parts of the delay risk value and the missing risk ratio that exceed the preset delay risk value threshold and the preset missing risk ratio threshold respectively, and mark the parts of the delay risk value and the missing risk ratio that exceed the preset delay risk value threshold and the preset missing risk ratio threshold as the delay impact value YW and the missing impact value QS respectively. Step 2: Obtain the average value of the snow and wind hazard assessment coefficients corresponding to the operation duration and mark it as the average snow and wind hazard assessment coefficient PF. According to the formula, obtain the delay impact risk coefficient W, and compare and analyze the delay impact risk coefficient W with the preset delay impact risk coefficient range stored in its internal record: If the delay impact risk coefficient W is greater than the maximum value in the preset delay impact risk coefficient range, a first-level impact signal is generated; if the delay impact risk coefficient W is within the preset delay impact risk coefficient range, a second-level impact signal is generated; If the delay impact risk coefficient W is less than the minimum value in the preset delay impact risk coefficient range, a third-level impact signal is generated.

6. The intelligent monitoring system for wind and snow flow based on artificial intelligence according to claim 1, characterized in that, The efficiency evaluation analysis operation process of the efficiency analysis unit is as follows: S1: Obtain the number of data transmissions within the time threshold, and then obtain the number of transmission repetitions from the number of data transmissions. The number of transmission repetitions refers to the situation where data is missing in the first transmission and is transmitted again in the second transmission, repeating once in this case, and so on. Mark the ratio of the number of transmission repetitions to the number of data transmissions as the transmission risk ratio CB; S12: Obtain the operation duration within the time threshold, and compare and analyze the operation duration with the preset operation duration threshold. If the operation duration is greater than the preset operation duration threshold, mark the part where the operation duration is greater than the preset operation duration threshold as the delay value, and then mark the ratio of the delay value to the operation duration as the duration risk ratio SB; S13: Obtain the efficiency evaluation coefficient G according to the formula, and compare and analyze the efficiency evaluation coefficient G with the preset efficiency evaluation coefficient threshold stored in it: If the efficiency evaluation coefficient G is less than or equal to the preset efficiency evaluation coefficient threshold, no signal is generated; If the efficiency evaluation coefficient G is greater than the preset efficiency evaluation coefficient threshold, a supervision signal is generated; S14: After the supervision signal is generated, immediately obtain the part where the efficiency evaluation coefficient G is greater than the preset efficiency evaluation coefficient threshold, and mark it as the risk efficiency value, and compare and analyze the risk efficiency value with the preset risk efficiency value threshold stored in it: If the risk efficiency value is less than or equal to the preset risk efficiency value threshold, a maintenance signal is generated; If the risk efficiency value is greater than the preset risk efficiency value threshold, a replacement signal is generated.

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