Whole-scene internet-of-things integrated fusion management system and method for smart park

Abstract

The invention discloses a smart park full-scene Internet of Things integrated fusion management system and method, and relates to the technical field of Internet of Things, and the method comprises the steps: building a three-dimensional model of a park, dividing a management and control region in the three-dimensional model, capturing a dangerous event, and obtaining the management and control degree of monitoring equipment; shooting a panoramic image of the park, and dividing a calibration area in the panoramic image to obtain an early warning degree of the monitoring equipment; establishing a first information sequence according to the management and control area and the calibration area in the monitoring picture of each monitoring device; and establishing a second information sequence according to the management and control degree and the early warning degree, and judging whether to carry out early warning prompt on the picture display duration or not according to the picture display duration set for each monitoring device in the current alternate switching process. According to the invention, the management and control area and the calibration area are analyzed to judge whether the pre-warning prompt is carried out on the picture display duration of the set monitoring picture, so that the problem of unreasonable display duration setting when the pictures are switched in turn is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of Internet of Things, and specifically to a smart park full-scenario Internet of Things integrated fusion management system and method. Background Art

[0002] A smart park refers to a modern industrial park that uses advanced information and communication technologies to realize intelligent and networked management and services within the park. It creates a more convenient and comfortable working environment for enterprises and employees within the park by improving operational efficiency, reducing resource consumption, and enhancing corporate competitiveness. In smart supervision, the park generally includes multiple surveillance cameras. At present, the mainstream display method of these surveillance images includes switching to display each surveillance screen in turn, so that security personnel can quickly browse the real-time status of the park; however, for the switching of surveillance screens, currently only a simple timed switching method is used, and no analysis is performed based on the actual situation of the park and the historical alarm events of each surveillance camera. There will be unreasonable situations in the display time of the set surveillance screen. Summary of the invention

[0003] The purpose of the present invention is to provide a smart park full-scenario Internet of Things integrated fusion management system and method to solve the problems raised in the prior art.

[0004] To achieve the above object, the present invention provides the following technical solutions: The integrated fusion management method of the Internet of Things in all scenarios of a smart park includes the following steps: Step S100: Establish a three-dimensional model of the park, divide the control area with restricted access in the three-dimensional model, capture and analyze the dangerous events corresponding to the history of each monitoring device in the park, and extract the dangerous monitoring fragment corresponding to each dangerous event; analyze the control area in the dangerous monitoring fragment corresponding to the monitoring device to obtain the control degree of the monitoring device; Step S200: Take a panoramic image of the park and mark all the calibration corner points in the panoramic image. The calibration corner points are points at the corners in the park that are likely to block the line of sight. According to the calibration corner points, the calibration area in the panoramic image is divided; in the monitoring screen corresponding to the monitoring device, the moving objects passing through the calibration area are analyzed to obtain the warning level of the monitoring device; Step S300: Obtain the warning area in the monitoring screen according to the control area and the calibration area in the monitoring screen of each monitoring device, and obtain the characteristic area corresponding to the monitoring device according to the pixel area occupied by different types of areas in the monitoring screen corresponding to the monitoring device; establish a first information sequence according to the characteristic area corresponding to each monitoring device; Step S400: Establish a second information sequence according to the control level and warning level of the monitoring device; determine whether to issue a warning prompt for the screen display time according to the screen display time set for each monitoring device during the current rotation switching process, as well as the first information sequence and the second information sequence.

[0005] Furthermore, step S100 includes: Step S110: extract all control areas in the three-dimensional model, and divide the range corresponding to the control area in the monitoring screen corresponding to each monitoring device as the control range. The monitoring devices are all gun cameras with fixed positions and monitoring angles; Capture the dangerous events corresponding to each monitoring device. The dangerous events are events in which a mobile object is detected to enter the control range from the monitoring screen corresponding to the monitoring device. The monitoring segment between the time when the mobile object enters the control range and the time when it leaves the control range is used as the dangerous monitoring segment. All dangerous monitoring segments corresponding to each monitoring device are obtained. Controlled areas are specific areas within the park where entry and exit of personnel, vehicles and materials are strictly restricted or prohibited based on key factors such as safety, operation and privacy. These areas carry important facilities, sensitive information or dangerous goods in the park and are the core focus of park management. Dangerous goods storage areas, key production operation areas and special resource protection areas are all controlled areas. Controlled areas are of great significance to the security and privacy management of the park. When people are found entering such areas, alarms should be triggered and recorded in time. Step S120: Add the duration of all dangerous monitoring segments corresponding to a certain monitoring device c to obtain the dangerous duration D of a certain monitoring device c. c , and then get the duration of each monitoring device, and extract the maximum duration of danger D max ; The rectangular area box corresponding to the moving object involved in the dangerous event on the monitoring screen is taken as the moving object range; several moments in the dangerous monitoring fragment are randomly obtained, and the number of pixels where the moving object range and the control range intersect at each moment is obtained, and the average value is calculated as the characteristic pixel number S of a certain monitoring device c c , and then get the control degree of a monitoring device c: , V1 and V2 are the first weight and the second weight respectively, V1+V2=1, e is the natural exponent, K is the quantitative coefficient, K>0.

[0006] Further, step S200 includes: Step S210: according to the location of the calibration corner point, an area with a radius r and a calibration corner point as the center of the panoramic image is used as the calibration area; a monitoring segment of a monitoring device within a period of time is randomly extracted, and a range corresponding to the calibration area is divided out in the monitoring screen corresponding to the monitoring segment as the calibration range; a rectangular area box corresponding to a moving object G in the monitoring screen is used as the target area, and the center point of the target area is used as the target point; The moment when the target area intersects with the calibration area is taken as the first moment, and the direction from the position of the target point at the moment before the first moment to the position of the target point at the first moment is taken as the first moving direction; the moment when the target area and the calibration area do not intersect for the first time after the first moment is taken as the second moment, and the direction from the position of the target point at the moment before the second moment to the position of the target point at the second moment is taken as the second moving direction; Step S220: If the distance between the target point corresponding to a certain moment between the first moment and the second moment and the calibrated corner point is less than the distance threshold, or the angle between the first moving direction and the second moving direction is less than the angle threshold, then a certain moving object G is taken as a feature object; all feature objects in the monitoring segment are obtained, and the variance between the number of pixels is calculated according to the number of pixels in the target area corresponding to each feature object at the first moment, and the variance is used as the warning level of a certain monitoring device.

[0007] In this solution, moving objects include: human body, animals and vehicles, etc. The calibrated corner points are the positions that are not easy to find at the corners, and the calibrated areas are those areas where collision accidents are prone to occur. When people are walking normally, due to the obstruction of vision, they are prone to collide with moving objects such as people, animals or vehicles rushing out from other positions. It can be seen that this part of the area should also be the focus of monitoring equipment. Since the areas of human body, animals and vehicles on the monitoring screen are different, which is specifically manifested in different numbers of pixels, when the variance corresponding to the number of pixels is large, it means that the probability of different moving objects existing in the calibrated area is high, that is, the more types of moving objects passing through the calibrated area, and under normal circumstances, when the corner area allows people and animals to pass through, and allows vehicles to pass through, then the probability of accidents in this area is higher, and more attention should be paid to the calibrated area.

[0008] Furthermore, step S300 includes: Step S310: The area where the control area and the calibration area in the monitoring screen corresponding to a certain monitoring device intersect is taken as the warning area, and the weight of the control area is set to W1, the weight of the calibration area is set to W2, and the weight of the warning area is set to W3. The pixel area of ​​each type of area in the monitoring screen of a certain monitoring device is obtained, and then the characteristic area of ​​a certain monitoring device is obtained. , where H is the number of region types, H=3, 1≤h≤H, W his the weight of the h-th region, S h is the pixel area of ​​the h-th category area; Step S320: Randomly sort each monitoring device in the park, and normalize the characteristic area according to the characteristic area of ​​each monitoring device, so as to establish a first information sequence Y.

[0009] It should be noted that the control area is an area with restricted access, and the calibration area is an area prone to accidents. The warning area where the two intersect should have the characteristics of higher accident risks and dangers, such as the entrances and exits of hazardous chemical warehouses and the surrounding corners. On the one hand, because the storage of chemicals itself is extremely dangerous, it is subject to strict control; on the other hand, there are frequent exchanges of people and vehicles at the entrances and exits, and there are blind spots at the corners, which are prone to collisions, falling objects and other accidents. It is a calibration area with a high incidence of accidents. For example, in some chemical parks, vehicles transporting hazardous chemicals may scrape and collide with other vehicles or pedestrians at the corners of the warehouse entrances and exits due to insufficient turning radius and obstructed vision, which may cause serious accidents such as hazardous chemical leaks, so these areas should be given higher attention.

[0010] Furthermore, step S400 includes: Step S410: According to the control level and warning level of a certain monitoring device, the warning value of a certain monitoring device is obtained as follows: , where RL is the control level, e is the natural index, K1 is the adjustment coefficient, and d is the warning level; and according to the warning value of each monitoring device, the warning value is normalized, and according to the arrangement order of the monitoring devices, a second information sequence X is established; according to the current screen display time of each monitoring device, the screen display time is normalized, and according to the arrangement order of the monitoring devices, a display time sequence C is established; Step S420: Calculate the Pearson correlation coefficient P1 between sequence Y and sequence C, and the Pearson correlation coefficient P2 between sequence X and sequence C. If both P1 and P2 exist, <P ^ And P2 <P ^ , where P ^ The coefficient threshold is used to give an early warning of the screen display duration set during the current switching process.

[0011] The integrated fusion management system of the Internet of Things for all scenarios in the smart park includes a control degree calculation module, an early warning degree calculation module, a first information sequence establishment module, and an early warning prompt module; Control degree calculation module: used to establish a three-dimensional model of the park, divide the control area with restricted access in the three-dimensional model, capture and analyze the dangerous events corresponding to the history of each monitoring device in the park, and extract the dangerous monitoring fragments corresponding to each dangerous event; analyze the control area in the dangerous monitoring fragments corresponding to the monitoring equipment to obtain the control degree of the monitoring equipment; Warning degree calculation module: used to take a panoramic image of the park and mark all the calibration corner points in the panoramic image. The calibration corner points are points at the corners in the park that are easy to block the line of sight. The calibration area in the panoramic image is divided according to the calibration corner points; the moving objects passing through the calibration area in the monitoring screen corresponding to the monitoring device are analyzed to obtain the warning degree of the monitoring device; The first information sequence establishment module is used to obtain the warning area in the monitoring screen according to the control area and the calibration area in the monitoring screen of each monitoring device, and obtain the characteristic area corresponding to the monitoring device according to the pixel area occupied by different types of areas in the monitoring screen corresponding to the monitoring device; establish the first information sequence according to the characteristic area corresponding to each monitoring device; Early warning prompt module: used to establish a second information sequence according to the control level and early warning level of the monitoring equipment; determine whether to issue an early warning prompt for the screen display time according to the screen display time set for each monitoring equipment during the current rotation switching process, as well as the first information sequence and the second information sequence.

[0012] Further, the control degree calculation module includes a danger monitoring segment extraction unit and a control degree calculation unit; Dangerous situation monitoring fragment extraction unit: used to extract all control areas in the three-dimensional model and divide the control range in the monitoring screen corresponding to the monitoring equipment; capture and analyze the dangerous situation events corresponding to the history of each monitoring equipment in the park, and extract the dangerous situation monitoring fragment corresponding to each dangerous situation event; Control degree calculation unit: used to analyze the control area in the dangerous situation monitoring segment corresponding to the monitoring equipment to obtain the control degree of the monitoring equipment.

[0013] Further, the early warning prompt module includes a sequence establishment unit and an early warning prompt unit; Sequence establishment unit: used to obtain the warning value of a certain monitoring device according to the control degree and warning degree of a certain monitoring device, and then establish a second information sequence; according to the current screen display time of each monitoring device, establish a display time sequence; The early warning prompt unit is used to determine whether to issue an early warning prompt for the screen display time set in the current rotation switching process according to the display time sequence, the first information sequence and the second information sequence.

[0014] Compared with the prior art, the beneficial effects of the present invention are as follows: the present invention provides an integrated fusion management system and method for the entire IoT scenario of a smart park, including: establishing a three-dimensional model of the park, dividing the control area in the three-dimensional model, capturing dangerous events, and obtaining the control degree of the monitoring equipment; shooting a panoramic image of the park, dividing the calibration area in the panoramic image, and obtaining the warning degree of the monitoring equipment; establishing a first information sequence based on the control area and the calibration area in the monitoring screen of each monitoring device; establishing a second information sequence based on the control degree and the warning degree, and judging whether to give a warning prompt for the screen display time according to the screen display time set for each monitoring device during the current rotation switching process. The present invention analyzes the control area and the calibration area to judge whether to give a warning prompt for the screen display time of the set monitoring screen, thereby optimizing the problem of unreasonable display time setting when the screen is switched in rotation. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is a flow chart of the integrated fusion management method of the Internet of Things for all scenarios in a smart park of the present invention; Figure 2 This is a structural diagram of the integrated fusion management system of the smart park's full-scenario Internet of Things of the present invention. DETAILED DESCRIPTION

[0016] 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.

[0017] Example: Figure 1 As shown, the present invention provides a technical solution for the integrated fusion management method of the full-scenario Internet of Things in a smart park, comprising the following steps: Step S100: Establish a three-dimensional model of the park, divide the access-restricted control area in the three-dimensional model, capture and analyze the dangerous events corresponding to the history of each monitoring device in the park, and extract the dangerous monitoring fragment corresponding to each dangerous event; analyze the control area in the dangerous monitoring fragment corresponding to the monitoring device to obtain the control degree of the monitoring device.

[0018] Step S110: extract all control areas in the three-dimensional model, and divide the range corresponding to the control area in the monitoring screen corresponding to each monitoring device as the control range. The monitoring devices are all gun cameras with fixed positions and monitoring angles; Capture the dangerous events corresponding to each monitoring device. The dangerous events are events in which a mobile object is detected to enter the control range from the monitoring screen corresponding to the monitoring device. The monitoring segment between the time when the mobile object enters the control range and the time when it leaves the control range is used as the dangerous monitoring segment. All dangerous monitoring segments corresponding to each monitoring device are obtained. Controlled areas are specific ranges within the park where entry and exit of personnel, vehicles and materials are strictly restricted or prohibited based on key factors such as safety, operation and privacy. These areas carry important facilities, sensitive information or dangerous goods in the park and are the core focus of park management. Dangerous goods storage areas, key production operation areas and special resource protection areas are all controlled areas. Controlled areas are of great significance to the security and privacy management of the park. When people are found entering such areas, alarms should be triggered and recorded in time.

[0019] Step S120: Add the duration of all dangerous monitoring segments corresponding to a certain monitoring device c to obtain the dangerous duration D of a certain monitoring device c. c , and then get the duration of each monitoring device, and extract the maximum duration of danger D max ; The rectangular area box corresponding to the moving object involved in the dangerous event on the monitoring screen is taken as the moving object range; several moments in the dangerous monitoring fragment are randomly obtained, and the number of pixels where the moving object range and the control range intersect at each moment is obtained, and the average value is calculated as the characteristic pixel number S of a certain monitoring device c c , and then get the control degree of a monitoring device c: , V1 and V2 are the first weight and the second weight respectively, V1+V2=1, e is the natural exponent, K is the quantitative coefficient, K>0.

[0020] Step S200: Take a panoramic image of the park and mark all calibrated corner points in the panoramic image. The calibrated corner points are points at the corners in the park that are likely to block the line of sight, and divide the calibrated area in the panoramic image according to the calibrated corner points; analyze the moving objects passing through the calibrated area in the monitoring screen corresponding to the monitoring device to obtain the warning level of the monitoring device.

[0021] Step S210: according to the location of the calibration corner point, an area with a radius r and a calibration corner point as the center of the panoramic image is used as the calibration area; a monitoring segment of a monitoring device within a period of time is randomly extracted, and a range corresponding to the calibration area is divided out in the monitoring screen corresponding to the monitoring segment as the calibration range; a rectangular area box corresponding to a moving object G in the monitoring screen is used as the target area, and the center point of the target area is used as the target point; The moment when the target area intersects with the calibration area is taken as the first moment, and the direction from the position of the target point at the moment before the first moment to the position of the target point at the first moment is taken as the first moving direction; the moment when the target area and the calibration area do not intersect for the first time after the first moment is taken as the second moment, and the direction from the position of the target point at the moment before the second moment to the position of the target point at the second moment is taken as the second moving direction; Step S220: If the distance between the target point corresponding to a certain moment between the first moment and the second moment and the calibrated corner point is less than the distance threshold, or the angle between the first moving direction and the second moving direction is less than the angle threshold, then a certain moving object G is taken as a feature object; all feature objects in the monitoring segment are obtained, and the variance between the number of pixels is calculated according to the number of pixels in the target area corresponding to each feature object at the first moment, and the variance is used as the warning level of a certain monitoring device.

[0022] Step S300: According to the control area and the calibration area in the monitoring screen of each monitoring device, the warning area in the monitoring screen is obtained, and the characteristic area corresponding to the monitoring device is obtained according to the pixel area occupied by different types of areas in the monitoring screen corresponding to the monitoring device; according to the characteristic area corresponding to each monitoring device, a first information sequence is established.

[0023] Step S310: The area where the control area and the calibration area in the monitoring screen corresponding to a certain monitoring device intersect is taken as the warning area, and the weight of the control area is set to W1, the weight of the calibration area is set to W2, and the weight of the warning area is set to W3. The pixel area of ​​each type of area in the monitoring screen of a certain monitoring device is obtained, and then the characteristic area of ​​a certain monitoring device is obtained. , where H is the number of region types, H=3, 1≤h≤H, W h is the weight of the h-th region, S h is the pixel area of ​​the h-th category area; Step S320: Randomly sort each monitoring device in the park, and normalize the characteristic area according to the characteristic area of ​​each monitoring device, so as to establish a first information sequence Y.

[0024] It should be noted that the control area is an area with restricted access, and the calibration area is an area prone to accidents. The warning area where the two intersect should have the characteristics of higher accident risks and dangers, such as the entrances and exits of hazardous chemical warehouses and the surrounding corners. On the one hand, because the storage of chemicals itself is extremely dangerous, it is subject to strict control; on the other hand, there are frequent exchanges of people and vehicles at the entrances and exits, and there are blind spots at the corners, which are prone to collisions, falling objects and other accidents. It is a calibration area with a high incidence of accidents. For example, in some chemical parks, vehicles transporting hazardous chemicals may scratch and collide with other vehicles or pedestrians at the corners of warehouse entrances and exits due to insufficient turning radius, obstructed vision, etc., which may cause serious accidents such as leakage of hazardous chemicals. Therefore, these areas should have a higher degree of attention. In this embodiment, W1=0.25, W2=0.3, W3=0.45. There are m monitoring devices in the park, then the first information sequence Y={y1,y2,…,y m}, where y1,y2,…,y m They are the characteristic areas of the monitoring devices numbered 1, 2, …, m respectively.

[0025] Step S400: Establish a second information sequence according to the control level and warning level of the monitoring device; determine whether to issue a warning prompt for the screen display time according to the screen display time set for each monitoring device during the current rotation switching process, as well as the first information sequence and the second information sequence.

[0026] Step S410: According to the control level and warning level of a certain monitoring device, the warning value of a certain monitoring device is obtained as follows: , where RL is the control level, e is the natural index, K1 is the adjustment coefficient, and d is the warning level; and according to the warning value of each monitoring device, the warning value is normalized, and according to the arrangement order of the monitoring devices, a second information sequence X is established; according to the current screen display time of each monitoring device, the screen display time is normalized, and according to the arrangement order of the monitoring devices, a display time sequence C is established; Step S420: Calculate the Pearson correlation coefficient P1 between sequence Y and sequence C, and the Pearson correlation coefficient P2 between sequence X and sequence C. If both P1 and P2 exist, <P ^ And P2 <P ^ , where P ^ The coefficient threshold is used to give an early warning of the screen display duration set during the current switching process.

[0027] In this embodiment, the second information sequence X={x1,x2,…,x m}, where x1,x2,…,x mare the warning values ​​corresponding to the monitoring devices with serial numbers 1, 2, ..., m; the display duration sequence C={c1,c2, ..., c m}, where c1, c2, …, c m They are the screen display durations of the monitoring devices numbered 1, 2, …, m respectively.

[0028] It should be noted that the Pearson correlation coefficient is a statistical indicator used to measure the strength and direction of the linear relationship between two variables (the variables can be regarded as a sequence). The Pearson correlation coefficient is a prior art. Here, the Pearson correlation coefficient P1 is obtained by the first information sequence Y and the display time sequence C as an example. The formula is: , where m is the number of monitoring devices, y i is the normalized characteristic area of ​​the monitoring device with serial number i, c i The normalized screen display time of the monitoring device with serial number i. is the mean of the characteristic areas in sequence Y, is the mean of the display duration of the pictures in sequence C. The value range of the Pearson correlation coefficient r is [0,1], where 1 indicates a complete positive linear correlation, -1 indicates a complete negative linear correlation, and 0 indicates no linear correlation. In this embodiment, the coefficient threshold P ^ That is to say, when P1<0.5 and P2<0.5, it is judged that the screen display duration set in the current switching process is unreasonable, and an early warning should be issued. The coefficient threshold of the actual situation should be adjusted according to the actual situation.

[0029] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. The integrated fusion management method of the Internet of Things in all scenarios of a smart park is characterized by: The following steps are involved: Step S100: Establish a three-dimensional model of the park, divide the control area with restricted access in the three-dimensional model, capture and analyze the dangerous events corresponding to the history of each monitoring device in the park, and extract the dangerous monitoring fragment corresponding to each dangerous event; analyze the control area in the dangerous monitoring fragment corresponding to the monitoring device to obtain the control degree of the monitoring device; Step S200: taking a panoramic image of the park, and marking all calibration corner points in the panoramic image, wherein the calibration corner points are points at the corners in the park that are likely to block the line of sight, and dividing the calibration area in the panoramic image according to the calibration corner points; Analyze the moving objects passing through the calibration area in the monitoring screen corresponding to the monitoring device to obtain the warning level of the monitoring device; Step S300: Obtain the warning area in the monitoring screen according to the control area and the calibration area in the monitoring screen of each monitoring device, and obtain the characteristic area corresponding to the monitoring device according to the pixel area occupied by different types of areas in the monitoring screen corresponding to the monitoring device; Establishing a first information sequence according to the characteristic area corresponding to each monitoring device; Step S400: establishing a second information sequence according to the control level and warning level of the monitoring device; According to the screen display duration set for each monitoring device during the current rotation switching process, as well as the first information sequence and the second information sequence, it is determined whether to issue an early warning prompt for the screen display duration.

2. The integrated fusion management method for all-scenario Internet of Things in a smart park according to claim 1 is characterized in that: Step S100 includes: Step S110: extract all control areas in the three-dimensional model, and divide the range corresponding to the control area in the monitoring screen corresponding to each monitoring device as the control range. The monitoring devices are all gun cameras with fixed positions and monitoring angles; Capturing the dangerous event corresponding to each monitoring device, wherein the dangerous event is an event in which a mobile object is detected to enter a control range from the monitoring screen corresponding to the monitoring device, and taking the monitoring segment between the time when the mobile object enters the control range and the time when it leaves the control range as the dangerous monitoring segment, and obtaining all the dangerous monitoring segments corresponding to each monitoring device; Step S120: Add the duration of all dangerous monitoring segments corresponding to a certain monitoring device c to obtain the dangerous duration D of a certain monitoring device c. c , and then get the duration of each monitoring device, and extract the maximum duration of danger D max ; The rectangular area box corresponding to the moving object involved in the dangerous event on the monitoring screen is taken as the moving object range; several moments in the dangerous monitoring fragment are randomly obtained, and the number of pixels where the moving object range and the control range intersect at each moment is obtained, and the average value is calculated as the characteristic pixel number S of a certain monitoring device c c , and then get the control degree of a monitoring device c: , V1 and V2 are the first weight and the second weight respectively, V1+V2=1, e is the natural exponent, K is the quantitative coefficient, K>

0.

3. The integrated fusion management method for all-scenario Internet of Things in a smart park according to claim 2 is characterized in that: Step S200 includes: Step S210: according to the location of the calibration corner point, an area with a radius r and a calibration corner point as the center of the panoramic image is used as the calibration area; a monitoring segment of a monitoring device within a period of time is randomly extracted, and a range corresponding to the calibration area is divided out in the monitoring screen corresponding to the monitoring segment as the calibration range; a rectangular area box corresponding to a moving object G in the monitoring screen is used as the target area, and the center point of the target area is used as the target point; The moment when the target area intersects with the calibration area is taken as the first moment, and the direction from the position of the target point at the moment before the first moment to the position of the target point at the first moment is taken as the first moving direction; the moment when the target area and the calibration area do not intersect for the first time after the first moment is taken as the second moment, and the direction from the position of the target point at the moment before the second moment to the position of the target point at the second moment is taken as the second moving direction; Step S220: If the distance between the target point corresponding to a certain moment between the first moment and the second moment and the calibrated corner point is less than the distance threshold, or the angle between the first moving direction and the second moving direction is less than the angle threshold, then the moving object G is taken as a feature object; all feature objects in the monitoring segment are obtained, and the variance between the number of pixels is calculated according to the number of pixels in the target area corresponding to each feature object at the first moment, and the variance is used as the warning level of a certain monitoring device.

4. The integrated fusion management method for all-scenario Internet of Things in a smart park according to claim 1 is characterized in that: Step S300 includes: Step S310: The area where the control area and the calibration area in the monitoring screen corresponding to a certain monitoring device intersect is taken as the warning area, and the weight of the control area is set to W1, the weight of the calibration area is set to W2, and the weight of the warning area is set to W3. The pixel area of ​​each type of area in the monitoring screen of a certain monitoring device is obtained, and then the characteristic area of ​​a certain monitoring device is obtained. , where H is the number of region types, H=3, 1≤h≤H, W h is the weight of the h-th region, S h is the pixel area of ​​the h-th category area; Step S320: Randomly sort each monitoring device in the park, and normalize the characteristic area according to the characteristic area of ​​each monitoring device, so as to establish a first information sequence Y.

5. The integrated fusion management method for all-scenario Internet of Things in a smart park according to claim 4 is characterized in that: Step S400 includes: Step S410: According to the control level and warning level of a certain monitoring device, the warning value of a certain monitoring device is obtained as follows: , where RL is the control level, e is the natural index, K1 is the adjustment coefficient, and d is the warning level; and according to the warning value of each monitoring device, the warning value is normalized, and according to the arrangement order of the monitoring devices, a second information sequence X is established; according to the current screen display time of each monitoring device, the screen display time is normalized, and according to the arrangement order of the monitoring devices, a display time sequence C is established; Step S420: Calculate the Pearson correlation coefficient P1 between sequence Y and sequence C, and the Pearson correlation coefficient P2 between sequence X and sequence C. If both P1 and P2 exist, <P ^ And P2 <P ^ , where P ^ The coefficient threshold is used to give an early warning of the screen display duration set during the current switching process.

6. A smart park full-scenario Internet of Things integrated fusion management system, used to execute the smart park full-scenario Internet of Things integrated fusion management method described in any one of claims 1-5, characterized in that: The system includes a control degree calculation module, an early warning degree calculation module, a first information sequence establishment module and an early warning prompt module; Control degree calculation module: used to establish a three-dimensional model of the park, divide the control area with restricted access in the three-dimensional model, capture and analyze the dangerous events corresponding to the history of each monitoring device in the park, and extract the dangerous monitoring fragments corresponding to each dangerous event; analyze the control area in the dangerous monitoring fragments corresponding to the monitoring equipment to obtain the control degree of the monitoring equipment; Warning degree calculation module: used to take a panoramic image of the park and mark all the calibration corner points in the panoramic image. The calibration corner points are points at the corners in the park that are easy to block the line of sight, and the calibration area in the panoramic image is divided according to the calibration corner points; Analyze the moving objects passing through the calibration area in the monitoring screen corresponding to the monitoring device to obtain the warning level of the monitoring device; The first information sequence establishment module is used to obtain the warning area in the monitoring screen according to the control area and the calibration area in the monitoring screen of each monitoring device, and obtain the characteristic area corresponding to the monitoring device according to the pixel area occupied by different types of areas in the monitoring screen corresponding to the monitoring device; Establishing a first information sequence according to the characteristic area corresponding to each monitoring device; Early warning prompt module: used to establish a second information sequence according to the control level and early warning level of the monitoring equipment; According to the screen display duration set for each monitoring device during the current rotation switching process, as well as the first information sequence and the second information sequence, it is determined whether to issue an early warning prompt for the screen display duration.

7. The smart park full-scenario IoT integrated fusion management system according to claim 6 is characterized in that: The control degree calculation module includes a danger monitoring segment extraction unit and a control degree calculation unit; Dangerous situation monitoring fragment extraction unit: used to extract all control areas in the three-dimensional model and divide the control range in the monitoring screen corresponding to the monitoring equipment; capture and analyze the dangerous situation events corresponding to the history of each monitoring equipment in the park, and extract the dangerous situation monitoring fragment corresponding to each dangerous situation event; Control degree calculation unit: used to analyze the control area in the dangerous situation monitoring segment corresponding to the monitoring equipment to obtain the control degree of the monitoring equipment.

8. The smart park full-scenario IoT integrated fusion management system according to claim 6 is characterized in that: The early warning prompt module includes a sequence establishment unit and an early warning prompt unit; Sequence establishment unit: used to obtain the warning value of a certain monitoring device according to the control degree and warning degree of a certain monitoring device, and then establish a second information sequence; according to the current screen display time of each monitoring device, establish a display time sequence; Early warning prompt unit: used to determine whether to issue an early warning prompt for the screen display duration set in the current alternating switching process according to the display duration sequence, the first information sequence and the second information sequence.

Images