Slope plant state monitoring and early warning system based on big data analysis
By adopting big data analysis technology in the slope plant status monitoring system and combining vehicle, road and slope plant data for multi-dimensional analysis, the problem of low accuracy of analysis results in traditional systems is solved, and higher monitoring accuracy and stability are achieved.
Patent Information
- Application Number
- CN202411929175.0
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-12-25
- Publication Date
- 2025-03-25
AI Technical Summary
The traditional slope plant status monitoring and early warning system fails to fully consider the impact of vehicles on slope plants, resulting in low accuracy of analysis results, affecting the effectiveness of slope plant status monitoring.
The slope plant status monitoring and early warning system based on big data analysis is adopted. The image acquisition module collects images of slope plants and road vehicles, and the identification unit identifies vehicle, road and slope plant data. The data analysis module combines these data for multi-dimensional analysis. The early warning module decides whether to issue an early warning based on the analysis results.
It improves the accuracy and reliability of slope plant growth status monitoring, enhances the sensitivity to slope plant growth status, ensures that relevant personnel should be reminded to take countermeasures as soon as possible to avoid deterioration of slope plant growth and improves the stability of slope plant growth.
Smart Images
Figure CN119693802A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of slope plant monitoring, and in particular to a slope plant status monitoring and early warning system based on big data analysis. Background Art
[0002] The selection and configuration of urban slope plants is an important part of urban greening and ecological construction. It is of great significance to maintain slope stability, prevent soil erosion, and improve the quality of urban ecological environment.
[0003] Since slope plants are of great significance to the urban ecological environment, their growth status needs to be monitored to ensure the stability and safety of the slope. The traditional slope plant status monitoring and early warning system monitors the growth data of slope plants, processes and analyzes them to evaluate the growth status of slope plants and the stability of the slope. When it is detected that the slope plant status is abnormal or the slope stability is threatened, it automatically issues an early warning to remind relevant personnel to take countermeasures to ensure the stability of slope plant growth.
[0004] Traditional slope plant status monitoring and early warning systems generally analyze the growth status of slope plants by monitoring the growth status and environmental parameters of slope plants. However, since slope plants are usually set up on both sides of the road, vehicles will raise a lot of dust and produce exhaust gas during driving, which will affect the growth of slope plants. If the analysis is based only on the growth data of slope plants, the accuracy of the analysis results may be low, affecting the effect of slope plant status monitoring. Summary of the invention
[0005] The purpose of the present invention is to provide a slope plant status monitoring and early warning system based on big data analysis to solve the following technical problems: How to improve the monitoring effect of slope plant status.
[0006] The purpose of the present invention can be achieved by the following technical solutions: A slope plant status monitoring and early warning system based on big data analysis, the system comprising: An image acquisition module, comprising a camera unit and a recognition unit, wherein the camera unit is used to capture images of slope plants and vehicles traveling on the road; The recognition unit is used to recognize vehicle and road data and slope plant data in the image captured by the camera unit; A data analysis module, used to analyze the growth status of slope plants by combining the vehicle and road data and slope plant data identified by the identification unit; The early warning module is used to combine the analysis results of the data analysis module to decide whether an early warning is needed.
[0007] Furthermore, the analysis process of the data analysis module includes: S1: Calculate the daily road driving impact coefficient by combining the vehicle and road data identified by the identification unit; S2: By comparing the daily road traffic impact coefficient with the preset traffic impact coefficient threshold, and judging whether the traffic conditions on that day will affect the growth of slope plants according to the comparison results; S3: Calculate the daily growth status qualification coefficient of the slope plants by combining the daily road traffic impact coefficient with the slope plant data identified by the identification unit; S4: comparing the daily growth status qualification coefficient of the slope plants with a preset growth status qualification coefficient threshold, and judging whether the growth status of the slope plants is qualified according to the comparison result; S5: By combining the change amount of the qualified coefficient of the slope plant growth state within a period of time, the growth situation of the slope plants within a period of time is analyzed, and whether the growth situation of the slope plants is qualified is judged according to the analysis result.
[0008] Furthermore, the calculation process in S1 includes: By formula Calculate the road traffic impact coefficient on the i-th day ; Where i is any day of monitoring slope plants, is the damaged area of the slope road on the i-th day, is the crack area in the slope road on the i-th day, is the total area of the slope road, a is any vehicle traveling on the slope road in one day, b is the total number of vehicles traveling on the slope road in one day, is the exhaust gas emission of the vehicle on the i-th day, is the weight coefficient of the a-th vehicle, which is set based on empirical fitting. for The standard value can be selected and set according to the allowable error in the empirical data. is the speed of the ath vehicle on the slope road on the i-th day, is the preset driving speed, is the weight of the ath vehicle on the slope road on the i-th day, is the preset weight, is the error correction coefficient, which is set based on empirical fitting.
[0009] Furthermore, the comparison process in S2 includes: By calculating the road traffic impact coefficient of the i-th day The preset road traffic impact coefficient threshold Make a comparison; like The system determines that the road traffic has a great impact on that day. The exhaust emissions and dust raised by vehicles during driving will affect the growth of slope plants, and issues early warnings to strengthen monitoring and management. like The system judged that the impact on road driving on that day was small, and the vehicles emitted little exhaust gas and raised little dust during driving, which would not affect the growth of slope plants.
[0010] Furthermore, the calculation process in S3 includes: By formula Calculate the qualified coefficient of the growth state of slope plants on the i-th day ; in, is the green area of slope plants on the i-th day, is the green area of slope plants on the day before the i-th day, n is the total number of days for slope plant detection, For all The average value of For any slope green plant, is the total number of green plants on the slope, is the stem height of the sth slope green plant on the i-th day, is the preset stem height, is the number of spots of the sth slope green plant on the i-th day, The preset number of spots.
[0011] Furthermore, the comparison process in S4 includes: By calculating the qualified coefficient of the growth state of the slope plants on the i-th day The preset growth status qualification coefficient threshold Make a comparison; like , the system judges that the growth status of slope plants on the i-th day is good; Otherwise, the system determines that the growth status of the slope plants on the i-th day is poor and issues an early warning through the early warning module.
[0012] Furthermore, the analysis process in S5 includes: The data obtained by continuous monitoring of the data analysis module is used to establish a real-time change curve of the qualified coefficient of slope plant growth status ; And through the formula Calculate the change in the qualified coefficient of slope plant growth status over a period of time ; in, The time point for the first growth monitoring of slope plants. is the time point of the i-th growth monitoring of slope plants, is the proportionality coefficient, which is set according to empirical fitting. For all The average value of .
[0013] Furthermore, the analysis process in S5 also includes: By calculating the change of the qualified coefficient of slope plant growth status over a period of time The preset change threshold Make a comparison; like , the system judges that the growth trend of slope plants has been good for a period of time, the vitality of vegetation is strong, and no warning is needed; like The system determines that the growth trend of slope plants has deviated over a period of time, that the vitality of vegetation is gradually lost, and issues timely warnings.
[0014] Beneficial effects of the present invention: (1) The present invention collects data on slope roads, driving vehicles and slope plants, and analyzes the growth status of slope plants through a data analysis module with the support of diversified data, thereby improving the accuracy and reliability of the analysis results. In addition, the diversified data can improve the sensitivity of slope plant status monitoring, thereby improving the monitoring effect of the slope plant growth status. In addition, by combining the analysis results of the data analysis module to decide whether to issue an early warning, it can ensure that relevant personnel are reminded to take countermeasures in the first place, avoid the deterioration of the growth of slope plants, and improve the stability of slope plant growth.
[0015] (2) The present invention calculates the daily road driving impact coefficient by combining vehicle and road data, and compares it with a preset driving impact coefficient threshold, so as to judge whether the driving conditions will affect the growth of slope plants. Then, by combining this parameter with the slope plant data, the daily growth status qualification coefficient of the slope plants is calculated, and combined with the preset growth status qualification coefficient threshold for analysis, an accurate judgment can be made on whether the growth status of the slope plants is qualified. Moreover, after combining diversified data such as vehicle and road data, the accuracy of the growth status analysis results of the slope plants can be improved.
[0016] (3) The present invention calculates the road traffic impact coefficient of the i-th day Preset road traffic impact coefficient threshold Through this comparison method, it is possible to judge whether the exhaust emissions and dust raised by vehicles during driving on that day will affect the growth of slope plants, so as to decide whether it is necessary to issue early warnings to strengthen monitoring and management, to ensure the monitoring effect of the growth status of slope plants, and this data can provide diversified data support for the subsequent growth status analysis of slope plants, thereby improving the accuracy of the analysis results, thereby further improving the reliability of the monitoring results of the growth status of slope plants.
[0017] (4) The present invention calculates the qualified coefficient of the growth state of the slope plants on the i-th day. The preset growth status qualification coefficient threshold For comparison, the qualified coefficient of the growth status of the slope plants on the i-th day is The calculation combines the road traffic impact coefficient of the i-th day Therefore, the accuracy and reliability of its data are relatively high. Under this premise, by combining with the preset growth status qualification coefficient threshold for comparison, the diversified data can ensure that the system can make an accurate judgment on whether the growth status of the slope plants on the i-th day is good, thereby improving the accuracy of the slope plant status monitoring results.
[0018] (5) The present invention calculates the change in the qualified coefficient of the slope plant growth state over a period of time. The preset change threshold Compare and judge When , it means that the qualified coefficient of slope plant growth status is relatively stable or gradually increasing over a period of time, which means that the growth trend of slope plants is good and the vitality of vegetation is vigorous over a period of time. Otherwise, it means that the growth trend of slope plants is deviated and the vitality of vegetation is gradually lost. Through such settings, the growth trend of slope plants over a period of time can be analyzed, so as to further judge the status of slope plants and improve the monitoring effect of slope plant status. BRIEF DESCRIPTION OF THE DRAWINGS
[0019] The present invention will be further described below in conjunction with the accompanying drawings.
[0020] Figure 1 It is a schematic block diagram of a slope plant status monitoring and early warning system based on big data analysis in the present invention; Figure 2 It is a flow chart of the analysis process of the data analysis module in the present invention. DETAILED DESCRIPTION
[0021] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.
[0022] See also Figure 1 As shown, in one embodiment, the present application provides a slope plant status monitoring and early warning system based on big data analysis, the system comprising: An image acquisition module, comprising a camera unit and a recognition unit, wherein the camera unit is used to capture images of slope plants and vehicles traveling on the road; The recognition unit is used to recognize vehicle and road data and slope plant data in the image captured by the camera unit; A data analysis module, used to analyze the growth status of slope plants by combining the vehicle and road data and slope plant data identified by the identification unit; The early warning module is used to combine the analysis results of the data analysis module to decide whether an early warning is needed; Through the above technical solution, this embodiment provides an image acquisition module, including a camera unit and a recognition unit. During the monitoring period of slope plants, the system will use the camera unit to take images of slope plants at a unified time point every day, as well as images of all vehicles traveling on the road every day. Then, the recognition unit recognizes the vehicle and road data and slope plant data in the image taken by the camera unit, including the crack area and damaged area in the slope road, the speed, displacement and weight of the traveling vehicle, and the stem height, number of spots and green plant area of the slope plants. Then, the data analysis module is combined with the vehicle and road data and slope plant data recognized by the recognition unit to analyze the growth status of the slope plants, and the early warning module is combined with the analysis results of the data analysis module to decide whether an early warning is needed; Through such a setting, the system collects data on slope roads, data on moving vehicles and data on slope plants, and analyzes the growth status of slope plants through the data analysis module with the support of diversified data, which can improve the accuracy and reliability of the analysis results. In addition, the diversified data can improve the sensitivity of slope plant status monitoring, thereby improving the monitoring effect of the growth status of slope plants. By combining the analysis results of the data analysis module to decide whether to issue an early warning, it can ensure that relevant personnel are reminded to take countermeasures in the first place to avoid the deterioration of the growth of slope plants and improve the stability of slope plant growth.
[0023] See also Figure 2 As shown, the analysis process of the data analysis module includes: S1: Calculate the daily road driving impact coefficient by combining the vehicle and road data identified by the identification unit; S2: By comparing the daily road traffic impact coefficient with the preset traffic impact coefficient threshold, and judging whether the traffic conditions on that day will affect the growth of slope plants according to the comparison results; S3: Calculate the daily growth status qualification coefficient of the slope plants by combining the daily road traffic impact coefficient with the slope plant data identified by the identification unit; S4: comparing the daily growth status qualification coefficient of the slope plants with a preset growth status qualification coefficient threshold, and judging whether the growth status of the slope plants is qualified according to the comparison result; S5: Analyze the growth situation of the slope plants over a period of time by combining the change amount of the qualified coefficient of the slope plant growth state over a period of time, and judge whether the growth situation of the slope plants is qualified according to the analysis results; Through the above technical scheme, this embodiment provides an analysis process of the data analysis module, firstly, by combining the vehicle and road data identified by the identification unit, the daily road driving impact coefficient is calculated, and the daily road driving impact coefficient is compared with the preset driving impact coefficient threshold, and whether the driving conditions of the day will affect the growth of slope plants is judged according to the comparison result, and then the daily road driving impact coefficient is combined with the slope plant data identified by the identification unit to calculate the daily growth state qualification coefficient of the slope plants, and the daily growth state qualification coefficient of the slope plants is compared with the preset growth state qualification coefficient threshold, and whether the growth state of the slope plants is qualified is judged according to the comparison result, and finally, by combining the change amount of the slope plant growth state qualification coefficient over a period of time, the growth trend of the slope plants over a period of time is analyzed, and whether the growth trend of the slope plants is qualified is further judged according to the analysis result; Through such a setting, by combining the vehicle and road data to calculate the daily road driving impact coefficient, and comparing it with the preset driving impact coefficient threshold, it is possible to judge whether the driving conditions will affect the growth of slope plants. Afterwards, by combining this parameter with the slope plant data to calculate the daily growth status qualification coefficient of the slope plants, and analyzing it with the preset growth status qualification coefficient threshold, it is possible to make an accurate judgment on whether the growth status of the slope plants is qualified. After combining diversified data such as vehicle and road data, the accuracy of the growth status analysis results of the slope plants can be improved.
[0024] The calculation process in S1 includes: By formula Calculate the road traffic impact coefficient on the i-th day ; Where i is any day of monitoring slope plants, is the damaged area of the slope road on the i-th day, is the crack area in the slope road on the i-th day, is the total area of the slope road, a is any vehicle driving on the slope road in one day, b is the total number of vehicles driving on the slope road in one day, is the exhaust gas emission of the vehicle on the i-th day, is the weight coefficient of the ath vehicle, which is set based on empirical fitting. for The standard value of is the speed of the ath vehicle on the slope road on the i-th day, is the preset driving speed, is the weight of the ath vehicle on the slope road on the i-th day, is the preset weight, is the error correction coefficient, which is set according to empirical fitting; Through the above technical solution, this embodiment provides the road driving influence coefficient of the i-th day: , can be obtained by formula It is calculated that the crack area and damaged area in the slope road directly affect the amount of dust on the road, and the speed and weight of the vehicle will affect the amount of dust on the road. A large amount of dust accumulates on the surface of the slope plants, which will affect the growth and development of the plants. The exhaust gas emissions of the vehicles will directly affect the survival of the slope plants. Therefore, when the crack area and damaged area in the slope road on the i-th day are larger, and the exhaust gas emissions of the vehicles on the i-th day are higher, the speed and weight are greater, then the road driving impact coefficient on the i-th day is The larger the coefficient is, the greater the dust and exhaust gas on the road on that day have a greater impact on the growth of slope plants. On the contrary, when the crack area and damaged area on the slope road on the i-th day are smaller, and the exhaust gas emission of the vehicle on the i-th day is lower, and the speed and weight are higher, then the road driving impact coefficient on the i-th day is The smaller it is, the smaller the impact of dust and exhaust gas from the road on the growth of slope plants is. By setting this up, the road traffic influence coefficient of the i-th day is calculated. Afterwards, it can provide diversified data support for the subsequent growth status analysis of slope plants to ensure the reliability of data and analysis results. The data can reflect the growth environment of slope plants. Based on the data, it can be decided whether it is necessary to strengthen the monitoring and management of the growth status of slope plants, so as to further ensure the monitoring effect of the growth status of slope plants.
[0025] The comparison process in S2 includes: By calculating the road traffic impact coefficient of the i-th day The preset road traffic impact coefficient threshold Make a comparison; like The system determines that the road traffic has a great impact on that day. The exhaust emissions and dust raised by vehicles during driving will affect the growth of slope plants, and issues early warnings to strengthen monitoring and management. like The system judges that the impact of road traffic on that day is small, and the exhaust emissions and dust raised by vehicles on that day are small, which will not affect the growth of slope plants; Through the above technical solution, this embodiment calculates the road traffic influence coefficient of the i-th day The preset road traffic impact coefficient threshold Through this comparison method, it is possible to judge whether the exhaust emissions and dust raised by vehicles during driving on that day will affect the growth of slope plants, so as to decide whether it is necessary to issue early warnings to strengthen monitoring and management, to ensure the monitoring effect of the growth status of slope plants, and this data can provide diversified data support for the subsequent growth status analysis of slope plants, thereby improving the accuracy of the analysis results, thereby further improving the reliability of the monitoring results of the growth status of slope plants.
[0026] The calculation process in S3 includes: By formula Calculate the qualified coefficient of the growth state of slope plants on the i-th day ; in, is the green area of slope plants on the i-th day, is the green area of slope plants on the day before the i-th day, n is the total number of days for slope plant detection, For all The average value of For any slope green plant, is the total number of green plants on the slope, is the stem height of the sth slope green plant on the i-th day, is the preset stem height, is the number of spots of the sth slope green plant on the i-th day, is the preset number of spots; Through the above technical solution, this embodiment provides the growth status qualification coefficient of the slope plant on the i-th day , can be obtained by formula Obviously, when the green plant area of the slope plants on the i-th day is higher than the green plant area of the previous day, and the stem height of all the slope green plants on the i-th day is higher and the number of spots is smaller, then the growth state qualification coefficient of the slope plants on the i-th day is The higher the value, the better the growth status of the slope plants on that day and the better the growth trend in history. On the contrary, when the green plant area of the slope plants on the i-th day is lower than the green plant area of the previous day, and the stem height of all the slope green plants on the i-th day is lower and the number of spots is more, then the qualified coefficient of the growth status of the slope plants on the i-th day is The lower the value, the worse the growth state of the slope plants on that day and the worse the growth trend than in the past. This calculation method can be used to analyze the daily growth state of the slope plants and combine the road traffic impact coefficient of the i-th day. , which can improve the accuracy of the calculation results, thereby further improving the accuracy and reliability of the analysis results of the daily growth status of slope plants.
[0027] The comparison process in S4 includes: By calculating the qualified coefficient of the growth state of the slope plants on the i-th day The preset growth status qualification coefficient threshold Make a comparison; like , the system judges that the growth status of slope plants on the i-th day is good; Otherwise, the system determines that the growth status of the slope plants on the i-th day is poor and issues an early warning through the early warning module; Through the above technical solution, this embodiment calculates the qualified coefficient of the growth state of the slope plants on the i-th day Preset growth status qualification coefficient threshold For comparison, the qualified coefficient of the growth status of the slope plants on the i-th day is The calculation combines the road traffic impact coefficient of the i-th day Therefore, the accuracy and reliability of its data are relatively high. Under this premise, by combining with the preset growth status qualification coefficient threshold for comparison, the diversified data can ensure that the system can make an accurate judgment on whether the growth status of the slope plants on the i-th day is good, thereby improving the accuracy of the slope plant status monitoring results.
[0028] The analysis process in S5 includes: The data obtained by continuous monitoring of the data analysis module is used to establish a real-time change curve of the qualified coefficient of slope plant growth status ; And through the formula Calculate the change in the qualified coefficient of slope plant growth status over a period of time ; in, The time point for the first growth monitoring of slope plants. is the time point of the i-th growth monitoring of slope plants, is the proportionality coefficient, which is set according to empirical fitting. For all The average value of Through the above technical solution, this embodiment provides the change of the qualified coefficient of the slope plant growth state within a period of time. , can be obtained through the formula By calculating and obtaining the change in the qualified coefficient of the growth status of slope plants over a period of time, the change in the qualified coefficient of the growth status of slope plants every day over a period of time can be known, and because the qualified coefficient of the growth status of slope plants can directly reflect the growth status of slope plants, by analyzing the change in the qualified coefficient of the growth status of slope plants every day over a period of time, the growth trend of slope plants during this period of time can be analyzed, thereby further monitoring the status of slope plants.
[0029] The analysis process in S5 further includes: By calculating the change of the qualified coefficient of slope plant growth status over a period of time The preset change threshold Make a comparison; like , the system judges that the growth trend of slope plants has been good for a period of time, the vitality of vegetation is strong, and no warning is needed; like , the system determines the deviation of the growth trend of slope plants over a period of time, the vitality of vegetation is gradually lost, and issues a timely warning; Through the above technical solution, this embodiment calculates the change of the qualified coefficient of the slope plant growth state within a period of time. The preset change threshold Compare and judge When , it means that the qualified coefficient of slope plant growth status is relatively stable or gradually increasing over a period of time, which means that the growth trend of slope plants is good and the vitality of vegetation is vigorous over a period of time. Otherwise, it means that the growth trend of slope plants is deviated and the vitality of vegetation is gradually lost. Through such settings, the growth trend of slope plants over a period of time can be analyzed, so as to further judge the status of slope plants and improve the monitoring effect of slope plant status.
[0030] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.
Claims
1. A slope plant status monitoring and early warning system based on big data analysis, characterized in that: The system comprises: An image acquisition module, comprising a camera unit and a recognition unit, wherein the camera unit is used to capture images of slope plants and vehicles traveling on the road; The recognition unit is used to recognize vehicle and road data and slope plant data in the image captured by the camera unit; A data analysis module, used to analyze the growth status of slope plants by combining the vehicle and road data and slope plant data identified by the identification unit; The early warning module is used to combine the analysis results of the data analysis module to decide whether an early warning is needed.
2. According to the big data analysis-based slope plant status monitoring and early warning system of claim 1, it is characterized in that: The analysis process of the data analysis module includes: S1: Calculate the daily road driving impact coefficient by combining the vehicle and road data identified by the identification unit; S2: By comparing the daily road traffic impact coefficient with the preset traffic impact coefficient threshold, and judging whether the traffic conditions on that day will affect the growth of slope plants according to the comparison results; S3: Calculate the daily growth status qualification coefficient of the slope plants by combining the daily road traffic impact coefficient with the slope plant data identified by the identification unit; S4: comparing the daily growth status qualification coefficient of the slope plants with a preset growth status qualification coefficient threshold, and judging whether the growth status of the slope plants is qualified according to the comparison result; S5: By combining the change amount of the qualified coefficient of the slope plant growth state within a period of time, the growth situation of the slope plants within a period of time is analyzed, and whether the growth situation of the slope plants is qualified is judged according to the analysis result.
3. A slope plant status monitoring and early warning system based on big data analysis according to claim 2, characterized in that: The calculation process in S1 includes: By formula Calculate the road traffic impact coefficient on the i-th day ; Where i is any day of monitoring slope plants, is the damaged area of the slope road on the i-th day, is the crack area in the slope road on the i-th day, is the total area of the slope road, a is any vehicle driving on the slope road in one day, b is the total number of vehicles driving on the slope road in one day, is the exhaust gas emission of the vehicle on the i-th day, is the weight coefficient of the ath vehicle, which is set based on empirical fitting. for The standard value of is the speed of the ath vehicle on the slope road on the i-th day, is the preset driving speed, is the weight of the ath vehicle on the slope road on the i-th day, is the preset weight, is the error correction coefficient, which is set based on empirical fitting.
4. The slope plant status monitoring and early warning system based on big data analysis according to claim 3 is characterized in that: The comparison process in S2 includes: By calculating the road traffic impact coefficient of the i-th day The preset road traffic impact coefficient threshold Make a comparison; like The system determines that the road traffic has a great impact on that day. The exhaust emissions and dust raised by vehicles during driving will affect the growth of slope plants, and issues early warnings to strengthen monitoring and management. like The system judged that the impact on road driving on that day was small, and the vehicles emitted little exhaust gas and raised little dust during driving, which would not affect the growth of slope plants.
5. The slope plant status monitoring and early warning system based on big data analysis according to claim 4 is characterized in that: The calculation process in S3 includes: By formula Calculate the qualified coefficient of the growth state of slope plants on the i-th day ; in, is the green area of slope plants on the i-th day, is the green area of slope plants on the day before the i-th day, n is the total number of days for slope plant detection, For all The average value of For any slope green plant, is the total number of green plants on the slope, is the stem height of the sth slope green plant on the i-th day, is the preset stem height, is the number of spots of the sth slope green plant on the i-th day, The preset number of spots.
6. The slope plant status monitoring and early warning system based on big data analysis according to claim 5 is characterized in that: The comparison process in S4 includes: By calculating the qualified coefficient of the growth state of the slope plants on the i-th day The preset growth status qualification coefficient threshold Make a comparison; like , the system judges that the growth status of slope plants on the i-th day is good; Otherwise, the system determines that the growth status of the slope plants on the i-th day is poor and issues an early warning through the early warning module.
7. The slope plant status monitoring and early warning system based on big data analysis according to claim 6 is characterized in that: The analysis process in S5 includes: The data obtained by continuous monitoring of the data analysis module is used to establish a real-time change curve of the qualified coefficient of slope plant growth status ; And through the formula Calculate the change in the qualified coefficient of slope plant growth status over a period of time ; in, The time point for the first growth monitoring of slope plants. is the time point of the i-th growth monitoring of slope plants, is the proportionality coefficient, which is set according to empirical fitting. For all The average value of .
8. The slope plant status monitoring and early warning system based on big data analysis according to claim 7 is characterized in that: The analysis process in S5 further includes: By calculating the change of the qualified coefficient of slope plant growth status over a period of time The preset change threshold Make a comparison; like , the system judges that the growth trend of slope plants has been good for a period of time, the vitality of vegetation is strong, and no warning is needed; like The system determines that the growth trend of slope plants has deviated over a period of time, that the vitality of vegetation is gradually lost, and issues timely warnings.