Ecological sensitive area accident monitoring emergency management system driven by multiple parameters

Abstract

The invention relates to the field of accident monitoring emergency management systems, in particular to a multi-parameter-driven ecological sensitive area accident monitoring emergency management system which comprises an ecological sensitive area accident monitoring emergency management system. The ecological sensitive area accident monitoring emergency management system comprises a data processing terminal, a video AI recognition system, an environment automatic monitoring system, a road condition monitoring and predicting system, an emergency automatic processing system and a real-time interconnection and interworking system, and the data processing terminal is connected with the video AI recognition system. The vehicle driving state and the environment change are monitored in real time, the environment pollution risk caused by traffic accidents is found and processed in time, the system integrates the video AI recognition technology, the water quality automatic monitor, the automatic emergency processing equipment and the like, and the system has the advantages of being high in practicability and high in practicability. And the functions of automatic identification, graded early warning, automatic emergency processing and the like of emergencies can be realized.

Description

Technical Field

[0001] The present invention relates to the field of accident monitoring emergency management systems, and in particular to a multi-parameter driven ecologically sensitive area accident monitoring emergency management system. Background Art

[0002] With the continuous expansion of the highway network, more and more roads pass through ecologically sensitive areas such as drinking water sources and nature reserves. Once a traffic accident occurs in such areas, especially an accident involving vehicles transporting hazardous chemicals, it may cause serious pollution to the environment. Considering that when a highway passes through an ecologically sensitive area, if a traffic accident occurs, it may cause leakage of hazardous chemicals, which in turn causes serious pollution to the local environment, especially in ecologically sensitive areas such as drinking water sources and nature reserves, the fragility of the environment makes it possible for any pollution incident to bring about irreversible ecological disasters. Therefore, we propose a multi-parameter driven ecologically sensitive area accident monitoring and emergency management system to solve the above-mentioned problems. Summary of the invention

[0003] The purpose of the present invention is to solve the shortcomings of the background technology and propose a multi-parameter driven ecological sensitive area accident monitoring emergency management system.

[0004] To achieve the above objectives, the technical solution adopted by the present invention is: a multi-parameter driven ecologically sensitive area accident monitoring and emergency management system, including an ecologically sensitive area accident monitoring and emergency management system, the ecologically sensitive area accident monitoring and emergency management system including a data processing terminal, a video AI recognition system, an automatic environmental monitoring system, a road condition monitoring and prediction system, an automatic emergency processing system and a real-time interconnection system, the data processing terminal is connected to the video AI recognition system, the video AI recognition system is connected to the automatic environmental monitoring system, the automatic environmental monitoring system is connected to the road condition monitoring and prediction system, the road condition monitoring and prediction system is connected to the automatic emergency processing system, and the automatic emergency processing system is connected to the real-time interconnection system.

[0005] Preferably, the video AI recognition system includes a monitoring data synchronization system, a feature data extraction system and an alienated data weight system, the monitoring data synchronization system is connected to the feature data extraction system, and the feature data extraction system is connected to the alienated data weight system.

[0006] Preferably, the automatic environment monitoring system includes an ecological environment monitoring system, a change trend deduction system and an influencing factor access system, the ecological environment monitoring system is connected to the change trend deduction system, and the change trend deduction system is connected to the influencing factor access system.

[0007] Preferably, the road condition monitoring and prediction system includes a road condition data monitoring system, a side condition data monitoring system and a safety factor determination system, the road condition data monitoring system is connected to the side condition data monitoring system, and the side condition data monitoring system is connected to the safety factor determination system.

[0008] Preferably, the automatic emergency processing system includes an emergency equipment processing system, an on-site data collection system and a detailed data notification system, the emergency equipment processing system is connected to the on-site data collection system, and the on-site data collection system is connected to the detailed data notification system.

[0009] Preferably, the real-time interconnection system includes a regional data interconnection system, a cross-regional data access system and an intelligent linkage response system, the regional data interconnection system is connected to the cross-regional data access system, and the cross-regional data access system is connected to the intelligent linkage response system.

[0010] Preferably, the road condition monitoring prediction algorithm adopted by the road condition monitoring prediction system includes a target detection algorithm and a target tracking algorithm. The target detection algorithm adopts the YOLO series algorithm to divide the input image into multiple grid units, and each grid unit is responsible for predicting a certain number of bounding boxes and the confidence and category probability of these bounding boxes containing targets.

[0011] Preferably, the target tracking algorithm adopts a Kalman filter algorithm, assuming that the motion state of the target conforms to a linear dynamic model, and for the position tracking of the vehicle during straight-line driving, the target state equation is expressed as:

[0012]

[0013] Among them, X K is the state vector at the current moment;

[0014] F is the state transfer matrix;

[0015] B is the control input matrix;

[0016] U K-1 is the control input;

[0017] W K-1 is the process noise;

[0018] Its target observation equation is expressed as:

[0019]

[0020] Among them, Z K is the observation vector;

[0021] H is the observation matrix;

[0022] V Kis the observation noise.

[0023] Preferably, the automatic environmental monitoring system adopts a logistic regression model and a cluster analysis model in environmental monitoring prediction. The logistic regression model is used to predict binary classification problems, such as whether a certain area is an ecological danger zone. The probability of an event occurring is estimated by establishing a linear regression equation. Assuming the probability of an ecological danger event occurring is P, the formula of the logistic regression model is:

[0024]

[0025] Among them, β0 is the intercept;

[0026] β1, β2, …, β n is the regression coefficient;

[0027] X1, X2, …, X n It is a factor that affects ecological risks.

[0028] Preferably, the cluster analysis model is used to group regions with similar ecological characteristics together by calculating the Euclidean distance from the data point to each cluster center. The specific calculation formula is:

[0029]

[0030] Among them, X i and Y i is the i-th feature of the two data points;

[0031] n is the number of features.

[0032] Compared with the prior art, the present invention has the following beneficial effects:

[0033] 1. The present invention monitors the driving status of vehicles and environmental changes in real time, timely discovers and handles environmental pollution risks caused by traffic accidents. The system integrates video AI recognition technology, automatic water quality monitoring instrument, automatic emergency handling equipment, etc., and can realize automatic recognition of emergencies, graded warning, automatic emergency handling and other functions. The system is also interconnected with highway management centers, fire protection, ecological environment management departments, etc. to form a complete emergency response network, which helps to reduce social and economic losses caused by environmental pollution accidents and save a lot of emergency handling costs. Through early warning and rapid response, it can effectively reduce the spread of pollutants, thereby reducing governance costs.

[0034] 2. The video AI recognition system can realize AI video monitoring and recognition. Through the installation of high-definition video surveillance equipment on ecologically sensitive roads, artificial intelligence technology can be used to identify whether the vehicle is in an abnormal driving state, such as an emergency stop or rollover of a hazardous chemical vehicle, and trigger the alarm system. The automatic environmental monitoring system can realize automatic monitoring of water quality and geology in various regions. Water quality monitors are installed in the runoff collection system to monitor water quality changes in real time. Once abnormal water quality is found, such as a hazardous chemical leak, the system will trigger the second-level alarm and initiate emergency treatment measures. The automatic emergency treatment system can use the emergency equipment treatment system to automatically open the accident emergency treatment equipment, such as the valves of the sedimentation tank and the emergency tank, to collect pollutants and prevent further spread of pollution.

[0035] 3. Through the intelligent linkage response system, the intelligent linkage coordination system can be connected to the highway management center, fire department, ecological environment management department and other institutions to realize information sharing, so that when an emergency environmental incident occurs, the emergency response can be quickly initiated and the actions of various departments can be coordinated. At the same time, the on-site data collection system and the detailed data notification system can realize graded early warning. Different levels of early warning mechanisms are formulated according to the data collection at the accident site and the severity of the accident to ensure that accidents of various sizes can be handled in a timely and effective manner, which is conducive to practical use.

[0036] 4. The road condition monitoring prediction algorithm adopted by the road condition monitoring prediction system includes a target detection algorithm and a target tracking algorithm. The target state equation and the target observation equation are established. Through continuous prediction and updating steps, combined with observation information, the state of the target is optimally estimated. The target can be tracked quickly during video processing. In traffic scenarios, for fast-moving vehicles or video monitoring systems that require real-time processing, the target position can be updated quickly, and it has a certain robustness to changes in the target's appearance. Therefore, by real-time monitoring of vehicle driving status and environmental changes, the environmental pollution risks caused by traffic accidents can be discovered and dealt with in a timely manner.

[0037] 5. Through the automatic environmental monitoring system, the logistic regression model and cluster analysis model are used in environmental monitoring prediction to predict binary classification problems. The probability of an event is estimated by establishing a linear regression equation. The logistic regression model and cluster analysis model are established to group areas with similar ecological characteristics together. The clusters are determined by calculating the Euclidean distance from the data point to each cluster center. In the prediction of ecological danger zones, the known danger zones and safe zones can be clustered first, and then the new area can be divided into the corresponding category to determine whether it belongs to a danger zone. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1A schematic diagram of the structure of an ecologically sensitive area accident monitoring and emergency management system of the present invention, which is a multi-parameter driven ecologically sensitive area accident monitoring and emergency management system;

[0039] Figure 2 This is a schematic diagram of the structure of a video AI recognition system for an ecologically sensitive area accident monitoring and emergency management system driven by multiple parameters of the present invention;

[0040] Figure 3 This is a schematic diagram of the structure of an automatic environmental monitoring system of an ecologically sensitive area accident monitoring and emergency management system driven by multiple parameters of the present invention;

[0041] Figure 4 This is a schematic diagram of the structure of a road condition monitoring and prediction system of an ecologically sensitive area accident monitoring and emergency management system driven by multiple parameters of the present invention;

[0042] Figure 5 This is a schematic diagram of an automatic emergency processing system of an ecologically sensitive area accident monitoring and emergency management system driven by multiple parameters according to the present invention;

[0043] Figure 6 A schematic diagram of a real-time interconnection system of a multi-parameter driven ecologically sensitive area accident monitoring and emergency management system of the present invention;

[0044] Figure 7 This is a schematic diagram of the workflow of an ecologically sensitive area accident monitoring and emergency management system of the present invention, which is driven by multiple parameters.

[0045] 1. Accident monitoring and emergency management system in ecologically sensitive areas; 11. Data processing terminal; 12. Video AI recognition system; 121. Monitoring data synchronization system; 122. Feature data extraction system; 123. Alienated data weight system; 13. Automatic environmental monitoring system; 131. Ecological environment monitoring system; 132. Change trend deduction system; 133. Influencing factor access system; 14. Road condition monitoring and prediction system; 141. Road condition data monitoring system; 142. Edge condition data monitoring system; 143. Safety factor determination system; 15. Automatic emergency processing system; 151. Emergency equipment processing system; 152. On-site data collection system; 153. Detailed data notification system; 16. Real-time interconnection system; 161. Regional data interconnection system; 162. Cross-regional data access system; 163. Intelligent linkage response system. DETAILED DESCRIPTION

[0046] The following description is used to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are only examples, and those skilled in the art may think of other obvious variations.

[0047] like Figure 1-Figure 7A multi-parameter driven ecologically sensitive area accident monitoring and emergency management system shown includes an ecologically sensitive area accident monitoring and emergency management system 1, the ecologically sensitive area accident monitoring and emergency management system 1 includes a data processing terminal 11, a video AI recognition system 12, an automatic environmental monitoring system 13, a road condition monitoring and prediction system 14, an automatic emergency processing system 15 and a real-time interconnection system 16, the data processing terminal 11 is connected to the video AI recognition system 12, the video AI recognition system 12 is connected to the automatic environmental monitoring system 13, the automatic environmental monitoring system 13 is connected to the road condition monitoring and prediction system 14, the road condition monitoring and prediction system 14 is connected to the automatic emergency processing system 15, and the automatic emergency processing system 15 is connected to the real-time interconnection system 16;

[0048] Furthermore, in the specific implementation, the vehicle driving status and environmental changes are monitored in real time, and the environmental pollution risks caused by traffic accidents are discovered and handled in time. The system integrates video AI recognition technology, automatic water quality monitoring instruments, automatic emergency handling equipment, etc., and can realize the functions of automatic identification, graded warning, automatic emergency handling, etc. of emergencies. The system is also interconnected with the highway management center, fire protection, ecological environment management departments, etc. to form a complete emergency response network, which helps to reduce the social and economic losses caused by environmental pollution accidents, save a lot of emergency handling costs, and effectively reduce the spread of pollutants through early warning and rapid response, thereby reducing governance costs.

[0049] Among them, the video AI recognition system 12 includes a monitoring data synchronization system 121, a feature data extraction system 122 and an alienated data weight system 123, the monitoring data synchronization system 121 is connected to the feature data extraction system 122, and the feature data extraction system 122 is connected to the alienated data weight system 123, the automatic environment monitoring system 13 includes an ecological environment monitoring system 131, a change trend deduction system 132 and an influencing factor access system 133, the ecological environment monitoring system 131 is connected to the change trend deduction system 132, and the change trend deduction system 132 is connected to the influencing factor access system 133, the road condition monitoring and prediction system 14 includes a road condition data monitoring system 141, a side condition data monitoring system 142 and a safety factor determination system 143, the road condition data monitoring system 141 is connected to the side condition data monitoring system 142, and the side condition data monitoring system 142 is connected to the safety factor determination system 143;

[0050] Furthermore, in specific implementation, AI video monitoring and recognition can be achieved through the video AI recognition system 12. Through the installation of high-definition video surveillance equipment on roads in ecologically sensitive areas, artificial intelligence technology can be used to identify whether the vehicle is in an abnormal driving state, such as an emergency stop or rollover of a hazardous chemical vehicle, and trigger the alarm system. The automatic environmental monitoring system 13 can realize automatic monitoring of water quality and geology in various regions. Water quality monitors can be installed in the runoff collection system to monitor water quality changes in real time. Once abnormal water quality is found, such as a hazardous chemical leak, the system will trigger a second-level alarm and initiate emergency response measures.

[0051] The automatic emergency processing system 15 includes an emergency equipment processing system 151, an on-site data collection system 152 and a detailed data notification system 153. The emergency equipment processing system 151 is connected to the on-site data collection system 152, and the on-site data collection system 152 is connected to the detailed data notification system 153. The real-time interconnection system 16 includes a regional data interconnection system 161, a cross-regional data access system 162 and an intelligent linkage response system 163. The regional data interconnection system 161 is connected to the cross-regional data access system 162, and the cross-regional data access system 162 is connected to the intelligent linkage response system 163.

[0052] Furthermore, in specific implementation, the emergency automatic processing system 15 can utilize the emergency equipment processing system 151 to automatically open the accident emergency processing equipment, such as the valves of the sedimentation tank and the emergency tank, to collect pollutants and prevent further spread of pollution. At the same time, the intelligent linkage response system 163 can intelligently link the collaborative system with the highway management center, fire department, ecological environment management department and other institutions to network and realize information sharing, so that when an emergency environmental incident occurs, it can quickly initiate an emergency response and coordinate the actions of various departments. At the same time, the on-site data collection system 152 and the detailed data notification system 153 can realize graded warning, and formulate different levels of warning mechanisms according to the data collection at the accident site and the severity of the accident, to ensure that accidents of various sizes can be handled in a timely and effective manner, which is conducive to practical use.

[0053] Among them, the road condition monitoring prediction algorithm adopted by the road condition monitoring prediction system 14 includes a target detection algorithm and a target tracking algorithm. The target detection algorithm adopts the YOLO series algorithm to divide the input image into multiple grid units, each grid unit is responsible for predicting a certain number of bounding boxes and the confidence and category probability of these bounding boxes containing targets. The target tracking algorithm adopts the Kalman filter algorithm. Assuming that the motion state (position, speed, etc.) of the target conforms to the linear dynamic model, for the position tracking of the vehicle during straight-line driving, its target state equation is expressed as:

[0054]

[0055] Among them, X K is the state vector at the current moment (including position and velocity);

[0056] F is the state transfer matrix;

[0057] B is the control input matrix;

[0058] U K-1 is the control input (can be 0 if there is no external control input);

[0059] W K-1 is the process noise;

[0060] Its target observation equation is expressed as:

[0061]

[0062] Among them, Z K is the observation vector (such as the vehicle position information obtained by the sensor);

[0063] H is the observation matrix;

[0064] V K is the observation noise;

[0065] Through continuous prediction and updating steps, combined with observation information, the state of the target is optimally estimated, and the target can be quickly tracked during video processing. In traffic scenarios, for fast-moving vehicles or video surveillance systems that require real-time processing, the target position can be quickly updated, and it has a certain degree of robustness to changes in the target's appearance (such as partial occlusion of the vehicle, changes in lighting, etc.). Therefore, by real-time monitoring of vehicle driving status and environmental changes, the environmental pollution risks caused by traffic accidents can be discovered and dealt with in a timely manner.

[0066] The automatic environmental monitoring system 13 adopts a logistic regression model and a cluster analysis model in environmental monitoring prediction. The logistic regression model is used to predict binary classification problems, such as whether a certain area is an ecologically dangerous area (yes / no). The probability of an event is estimated by establishing a linear regression equation. Assuming the probability of an ecologically dangerous event is P, the formula of the logistic regression model is:

[0067]

[0068] Among them, β0 is the intercept;

[0069] β1, β2, ..., β n is the regression coefficient;

[0070] X1, X2, …, X nare factors that affect ecological risk (such as variables such as terrain, weather, and vegetation); the cluster analysis model is used to group areas with similar ecological characteristics together by calculating the Euclidean distance from the data point to each cluster center. The specific calculation formula is:

[0071]

[0072] Among them, X i and Y i is the i-th feature of the two data points;

[0073] n is the number of features, which determines the clustering. In the prediction of ecological danger zones, we can first cluster the known danger zones and safe zones, and then divide the new areas into corresponding categories to determine whether they belong to danger zones.

[0074] Working principle:

[0075] The present invention monitors the driving status of vehicles and environmental changes in real time, and promptly detects and handles environmental pollution risks caused by traffic accidents. The system integrates video AI recognition technology, automatic water quality monitoring instruments, automatic emergency response equipment, etc., and can realize functions such as automatic recognition of emergencies, graded warning, and automatic emergency response. The system is also interconnected with highway management centers, fire departments, and ecological environment management departments to form a complete emergency response network, which helps to reduce social and economic losses caused by environmental pollution accidents and save a lot of emergency response costs. Through early warning and rapid response, the spread of pollutants can be effectively reduced, thereby reducing the cost of governance. The video AI recognition system 12 can realize AI video monitoring and recognition. Through the installation of high-definition video monitoring equipment on ecologically sensitive roads, artificial intelligence technology can be used to identify whether the vehicle is in an abnormal driving state, such as an emergency stop or rollover of a hazardous chemical vehicle, and trigger the alarm system. The automatic environmental monitoring system 13 can realize automatic monitoring of water quality and geology in various regions. Water quality monitors are installed in the runoff collection system to monitor water quality changes in real time. Once abnormal water quality is found, such as a hazardous chemical leak, the system will trigger a second-level alarm and initiate emergency treatment measures. The automatic emergency treatment system 15 can The emergency equipment processing system 151 can be used to automatically open the valves of accident emergency processing equipment, such as sedimentation tanks and emergency tanks, to collect pollutants and prevent further spread of pollution. At the same time, the intelligent linkage response system 163 can be used to intelligently link the collaborative system with the highway management center, fire department, ecological environment management department and other institutions to achieve information sharing, so that when an emergency environmental incident occurs, an emergency response can be quickly initiated and the actions of various departments can be coordinated. At the same time, the on-site data collection system 152 and the detailed data notification system 153 can achieve graded early warning, and different levels of early warning mechanisms can be formulated according to the data collection at the accident site and the severity of the accident, ensuring that accidents of various sizes can be handled in a timely and effective manner, which is conducive to practical use.

[0076] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions only describe the principles of the present invention. The present invention may be subject to various changes and improvements without departing from the spirit and scope of the present invention. These changes and improvements fall within the scope of the present invention. The scope of protection claimed by the present invention is defined by the attached claims and their equivalents.

Claims

1. A multi-parameter driven ecologically sensitive area accident monitoring and emergency management system, comprising an ecologically sensitive area accident monitoring and emergency management system (1), characterized in that: The ecologically sensitive area accident monitoring and emergency management system (1) comprises a data processing terminal (11), a video AI recognition system (12), an automatic environmental monitoring system (13), a road condition monitoring and prediction system (14), an automatic emergency processing system (15) and a real-time interconnection system (16), wherein the data processing terminal (11) is connected to the video AI recognition system (12), the video AI recognition system (12) is connected to the automatic environmental monitoring system (13), the automatic environmental monitoring system (13) is connected to the road condition monitoring and prediction system (14), the road condition monitoring and prediction system (14) is connected to the automatic emergency processing system (15), and the automatic emergency processing system (15) is connected to the real-time interconnection system (16).

2. The multi-parameter driven ecologically sensitive area accident monitoring and emergency management system according to claim 1, characterized in that: The video AI recognition system (12) comprises a monitoring data synchronization system (121), a feature data extraction system (122) and an alienated data weight system (123), wherein the monitoring data synchronization system (121) is connected to the feature data extraction system (122), and the feature data extraction system (122) is connected to the alienated data weight system (123).

3. The multi-parameter driven ecologically sensitive area accident monitoring and emergency management system according to claim 1, characterized in that: The automatic environmental monitoring system (13) comprises an ecological environment monitoring system (131), a change trend deduction system (132) and an influencing factor access system (133); the ecological environment monitoring system (131) is connected to the change trend deduction system (132), and the change trend deduction system (132) is connected to the influencing factor access system (133).

4. The multi-parameter driven ecologically sensitive area accident monitoring and emergency management system according to claim 1, characterized in that: The road condition monitoring and prediction system (14) comprises a road condition data monitoring system (141), a side condition data monitoring system (142) and a safety factor determination system (143); the road condition data monitoring system (141) is connected to the side condition data monitoring system (142), and the side condition data monitoring system (142) is connected to the safety factor determination system (143).

5. The multi-parameter driven ecologically sensitive area accident monitoring and emergency management system according to claim 1, characterized in that: The emergency automatic processing system (15) comprises an emergency equipment processing system (151), an on-site data collection system (152) and a detailed data notification system (153); the emergency equipment processing system (151) is connected to the on-site data collection system (152), and the on-site data collection system (152) is connected to the detailed data notification system (153).

6. The multi-parameter driven ecologically sensitive area accident monitoring and emergency management system according to claim 1, characterized in that: The real-time interconnection system (16) comprises a regional data interconnection system (161), a cross-region data access system (162) and an intelligent linkage response system (163); the regional data interconnection system (161) is connected to the cross-region data access system (162), and the cross-region data access system (162) is connected to the intelligent linkage response system (163).

7. The multi-parameter driven ecologically sensitive area accident monitoring and emergency management system according to claim 4, characterized in that: The road condition monitoring prediction algorithm adopted by the road condition monitoring prediction system (14) includes a target detection algorithm and a target tracking algorithm. The target detection algorithm adopts the YOLO series algorithm to divide the input image into multiple grid units, each grid unit is responsible for predicting a certain number of bounding boxes and the confidence and category probability of these bounding boxes containing targets.

8. The multi-parameter driven ecologically sensitive area accident monitoring and emergency management system according to claim 7, characterized in that: The target tracking algorithm adopts the Kalman filter algorithm. Assuming that the motion state of the target conforms to the linear dynamic model, the target state equation for the position tracking of the vehicle during straight-line driving is expressed as: Among them, X K is the state vector at the current moment; F is the state transfer matrix; B is the control input matrix; U K-1 is the control input; W K-1 is the process noise; Its target observation equation is expressed as: Among them, Z K is the observation vector; H is the observation matrix; V K is the observation noise.

9. The multi-parameter driven ecologically sensitive area accident monitoring and emergency management system according to claim 3, characterized in that: The automatic environmental monitoring system (13) adopts a logistic regression model and a cluster analysis model in environmental monitoring prediction. The logistic regression model is used to predict binary classification problems, such as whether a certain area is an ecologically dangerous area. The probability of an event occurring is estimated by establishing a linear regression equation. Assuming the probability of an ecologically dangerous event occurring is P, the formula of the logistic regression model is: Among them, β0 is the intercept; β1, β2, ..., β n is the regression coefficient; X1, X2, …, X n It is a factor that affects ecological risks.

10. The multi-parameter driven ecologically sensitive area accident monitoring and emergency management system according to claim 9, characterized in that: The cluster analysis model is used to group areas with similar ecological characteristics together by calculating the Euclidean distance from the data point to each cluster center. The specific calculation formula is: Among them, X i and Y i is the i-th feature of the two data points; n is the number of features.

Images